From b175fdd38ed81638e47c1e897ed65bb87efbb8c4 Mon Sep 17 00:00:00 2001
From: weimingc <17592131+meenchen@users.noreply.github.com>
Date: Thu, 8 Oct 2026 10:09:40 -0700
Subject: [PATCH 1/4] docs: introduce AutoQuant KV cache quantization

Signed-off-by: weimingc <17592131+meenchen@users.noreply.github.com>
---
 README.md                                     |   1 +
 .../autoquant-kv-qwen3-8b-accuracy-memory.png | Bin 0 -> 125821 bytes
 ...autoquant-kv-qwen3-8b-layer-allocation.png | Bin 0 -> 55619 bytes
 .../assets/autoquant-kv-qwen3-8b-methods.json | 159 +++++++++++++
 .../assets/autoquant-kv-qwen3-8b-results.csv  |  48 ++++
 .../assets/autoquant-kv-search.svg            | 113 +++++++++
 docs/source/announcements/autoquant-kv.rst    | 218 ++++++++++++++++++
 docs/source/index.rst                         |   7 +
 8 files changed, 546 insertions(+)
 create mode 100644 docs/source/announcements/assets/autoquant-kv-qwen3-8b-accuracy-memory.png
 create mode 100644 docs/source/announcements/assets/autoquant-kv-qwen3-8b-layer-allocation.png
 create mode 100644 docs/source/announcements/assets/autoquant-kv-qwen3-8b-methods.json
 create mode 100644 docs/source/announcements/assets/autoquant-kv-qwen3-8b-results.csv
 create mode 100644 docs/source/announcements/assets/autoquant-kv-search.svg
 create mode 100644 docs/source/announcements/autoquant-kv.rst

diff --git a/README.md b/README.md
index dc838483e88..33e306b98f6 100644
--- a/README.md
+++ b/README.md
@@ -27,6 +27,7 @@ Model Optimizer is also integrated with [NVIDIA Megatron-Bridge](https://github.
 
 ## Latest News
 
+- [2026/10/08] [BLOG: AutoQuant KV: Automatic Mixed-Precision KV Cache Quantization](https://nvidia.github.io/Model-Optimizer/announcements/autoquant-kv.html): Per-layer KV format search, GEMM composition, and a Qwen3-8B accuracy case study.
 - [2026/09/16] [BLOG: Scale Learning: Training NVFP4 Block Scales to Recover Accuracy](https://nvidia.github.io/Model-Optimizer/announcements/scale-learning.html)
 - [2026/09/16] [**End-to-end W4A4 NVFP4 + QAD tutorial for Qwen3.6-35B-A3B**](./examples/megatron_bridge/tutorials/Qwen3.6-35B-A3B): NVFP4 W4A4 PTQ plus quantization-aware distillation, reaching up to 1.30x vLLM throughput over BF16 and 3.1x smaller checkpoints while recovering the accuracy W4A4 costs.
 - [2026/09/09] [BLOG: Improving NVFP4 Accuracy with Local-Hessian Weight Scales](https://nvidia.github.io/Model-Optimizer/announcements/local-hessian.html)
diff --git a/docs/source/announcements/assets/autoquant-kv-qwen3-8b-accuracy-memory.png b/docs/source/announcements/assets/autoquant-kv-qwen3-8b-accuracy-memory.png
new file mode 100644
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diff --git a/docs/source/announcements/assets/autoquant-kv-qwen3-8b-methods.json b/docs/source/announcements/assets/autoquant-kv-qwen3-8b-methods.json
new file mode 100644
index 00000000000..6885e14a426
--- /dev/null
+++ b/docs/source/announcements/assets/autoquant-kv-qwen3-8b-methods.json
@@ -0,0 +1,159 @@
+{
+  "model": {
+    "id": "Qwen/Qwen3-8B",
+    "revision": "b968826d9c46dd6066d109eabc6255188de91218",
+    "weight_dtype": "BF16",
+    "attention_layers": 36,
+    "kv_heads": 8,
+    "head_dim": 128
+  },
+  "runtime": {
+    "kind": "development vLLM",
+    "image_sha256": "95f552c4d541fbcf1d4fea060f604f9256591424e6fe12732abbd903f19da486",
+    "tensor_parallelism": 1,
+    "data_parallelism": 4,
+    "cuda_graphs": true,
+    "prefix_caching": true,
+    "chunked_prefill": true
+  },
+  "search": {
+    "dataset": "Nemotron Post-Training Dataset v3",
+    "packed_rows": 64,
+    "tokens_per_row": 8192,
+    "scored_tail_tokens": 1024,
+    "seed": 1234,
+    "disjoint_validation_rows": 64,
+    "benchmark_prompts_separate": true,
+    "candidates": [
+      "FP8 K/V",
+      "FP8 K / NVFP4 V",
+      "NVFP4 K/V"
+    ],
+    "requested_budgets": [
+      5.375,
+      5.8125
+    ]
+  },
+  "autoquant_scale_policy": {
+    "mode": "default",
+    "kv_scale": 1.0,
+    "persistent_kv_scale_tensors_removed": 72,
+    "note": "Layer maps and BF16 weights were preserved; search was not rerun after removing scale tensors."
+  },
+  "accuracy_drop": {
+    "formula": "recipe accuracy_pct - BF16 accuracy_pct",
+    "units": "percentage points",
+    "positive": "better than BF16",
+    "negative": "worse than BF16",
+    "rounding": "Differences use unrounded input scores; table rounds half up to two decimal places.",
+    "uncertainty": "Point-estimate differences. CSV stderr_pp refers to each absolute accuracy, not the difference. No paired significance test is implied."
+  },
+  "uncertainty": {
+    "general": "Retained evaluator SE unless the CSV uncertainty_basis specifies a reconstruction or approximation. Repeated completions do not add independent benchmark problems.",
+    "scicode_bf16_nvfp4": "Approximate binomial SE: 100 * sqrt(p * (1-p) / 2704); repeated completions treated as independent.",
+    "scicode_native_6p25": "Mean of three independent run scores: 543/2704, 519/2704, 524/2704. SE is their sample SD / sqrt(3). CSV response counts sum across the three runs.",
+    "scicode_other": "Repeat-derived standard errors retained in the CSV."
+  },
+  "coverage": {
+    "six_non_scicode_tasks": "Complete successful response coverage for every displayed cell.",
+    "scicode": "Fixed denominator 2704 per run; missing and unsuccessful generations remain failures.",
+    "withheld": "NVFP4 and NVFP4 Four-over-Six AIME2025 controls used unmatched runtime/topology. Legacy-kernel results and unavailable matched AA-LCR, TauBench2, and TerminalBench results are excluded."
+  },
+  "payload": {
+    "formula_bytes": "resident_tokens * 36 * 8 * 128 * 2 * effective_kv_bits / 8",
+    "figure_resident_tokens": 32768,
+    "includes": "NVFP4 block scales",
+    "excludes": [
+      "allocator padding",
+      "cache-page rounding",
+      "tensor-scale metadata",
+      "weights",
+      "activations",
+      "runtime workspaces"
+    ],
+    "scope": "Calculated KV payload; not total GPU memory, throughput, or concurrency."
+  },
+  "benchmarks": {
+    "AIME2025": {
+      "task": "AIME_2025_aa_v2",
+      "max_model_len": 40960,
+      "max_new_tokens": 32768,
+      "temperature": 0.6,
+      "top_p": 0.95,
+      "repeats": 64,
+      "exact_denominator": 1920,
+      "reasoning_parser": null
+    },
+    "AIME2026": {
+      "task": "ns_aime2026",
+      "max_model_len": 40960,
+      "max_new_tokens": 32768,
+      "temperature": 1.0,
+      "top_p": 0.95,
+      "repeats": 64,
+      "exact_denominator": 1920,
+      "reasoning_parser": null
+    },
+    "GPQA": {
+      "task": "gpqa_diamond_aa_v3",
+      "max_model_len": 40960,
+      "max_new_tokens": 32768,
+      "temperature": 0.6,
+      "top_p": 0.95,
+      "repeats": 64,
+      "exact_denominator": 12672,
+      "reasoning_parser": null
+    },
+    "LiveCodeBench": {
+      "task": "ns_livecodebench",
+      "max_model_len": 40960,
+      "max_new_tokens": 32768,
+      "temperature": 1.0,
+      "top_p": 0.95,
+      "repeats": 8,
+      "exact_denominator": 3632,
+      "reasoning_parser": null,
+      "dataset": "test-v6-2408-2505"
+    },
+    "IFBench": {
+      "task": "ns_ifbench",
+      "max_model_len": 262144,
+      "max_new_tokens": 131072,
+      "temperature": 1.0,
+      "top_p": 0.95,
+      "repeats": 5,
+      "exact_denominator": 1500,
+      "reasoning_parser": "qwen3",
+      "tool_call_parser": "qwen3_coder",
+      "canonical_metric": "pass@1[avg-of-5].prompt_loose_accuracy",
+      "stderr_method": "sample standard error reconstructed from the five cumulative prompt-loose repeat aggregates",
+      "yarn_factor": 8
+    },
+    "SciCode": {
+      "task": "ns_scicode",
+      "max_model_len": 262144,
+      "max_new_tokens": 131072,
+      "temperature": 0.6,
+      "top_p": 0.95,
+      "repeats": 8,
+      "exact_denominator": 2704,
+      "reasoning_parser": "qwen3",
+      "tool_call_parser": "qwen3_coder",
+      "yarn_factor": 8,
+      "canonical_metric": "pass@1[avg-of-8].subtask_accuracy"
+    },
+    "RULER-128K": {
+      "task": "ruler-128k-chat",
+      "max_model_len": 131072,
+      "max_new_tokens": 2048,
+      "temperature": 1.0,
+      "top_p": 0.95,
+      "exact_denominator": 6500,
+      "reasoning_parser": null,
+      "sequence_tokens": 128000,
+      "subtasks": 13,
+      "samples_per_subtask": 500
+    }
+  },
+  "availability": "Development runtime and study checkpoints are not distributed with the blog. The pinned merged starter example uses a different candidate menu and search dataset. Local BF16 results have not been independently reproduced under the same evaluation contract."
+}
diff --git a/docs/source/announcements/assets/autoquant-kv-qwen3-8b-results.csv b/docs/source/announcements/assets/autoquant-kv-qwen3-8b-results.csv
new file mode 100644
index 00000000000..508721d7a54
--- /dev/null
+++ b/docs/source/announcements/assets/autoquant-kv-qwen3-8b-results.csv
@@ -0,0 +1,48 @@
+benchmark,variant,effective_kv_bits,search_budget_bits,scale_mode,accuracy_pct,stderr_pp,expected_responses,observed_responses,successful_responses,evaluated_runs,uncertainty_basis,coverage,run_id,response_statistics_sha256,baseline_accuracy_pct,accuracy_drop_pp
+AIME2025,BF16,16,,uniform control,67.03125,1.2431339808,1920,1920,1920,1,retained evaluator standard error,complete,d447ce0880c9093d,bd6f1b7705946f68fb80d5ea5bcb83e9756d3f69cec8689e8a333fcae49ca077,67.03125,0.00000
+AIME2025,FP8,8,,uniform control,66.6145833333,0.912881176,1920,1920,1920,1,retained evaluator standard error,complete,619316a9812fb952,d7730e88649aa46d3c3347c304a4bbeeaa0c5546917508a9716634177dafa770,67.03125,-0.4166666667
+AIME2025,Native 6.25,6.250003814697266,,fixed mixed control,66.1979166667,1.1098804,1920,1920,1920,1,retained evaluator standard error,complete,b104b8c493cdffc6,b2ecdcb026c3ed8ebccf190c047d10c52d839ea9943f0b2140ffc133d7fe137a,67.03125,-0.8333333333
+AIME2025,AutoQuant 5.33,5.326392703586155,5.375,default,65.15625,0.9942983559,1920,1920,1920,1,retained evaluator standard error,complete,c30ba7a6323e2ea5,839f5eda34b1bace455d1a0ba4185be0e58591efeedf454f8681444639d9fd96,67.03125,-1.87500
+AIME2025,AutoQuant 5.76,5.763892703586155,5.8125,default,66.7708333333,1.080282785,1920,1920,1920,1,retained evaluator standard error,complete,ac3f2941703fad8a,b31caae041754c595a8bcd86766065ef014193e36e9743d36a6a4963b3aa7ca8,67.03125,-0.2604166667
+AIME2026,BF16,16,,uniform control,66.1979166667,0.6205405,1920,1920,1920,1,retained evaluator standard error,complete,c5fc78b1ae3ac5b9,0281a2bf25dac614ff5536142f0551bc63caa91dfe03676295fa455fec32084c,66.1979166667,0E-10
+AIME2026,FP8,8,,uniform control,66.9270833333,0.6694086,1920,1920,1920,1,retained evaluator standard error,complete,0af7d0ee93f0656e,a22fab3d010123670f64c53924aba5fad32316891476e9df5408fa9a205d2235,66.1979166667,0.7291666666
+AIME2026,NVFP4,4.5,,uniform control,66.1458333333,0.6812991969,1920,1920,1920,1,retained evaluator standard error,complete,eb803219fd7aa436,641dccdf2e82a54c7bb7642afc0c99f2709c68ee49da7a654b92c38e59823e71,66.1979166667,-0.0520833334
+AIME2026,NVFP4 Four-over-Six,4.5,,uniform control,65.9895833333,0.7967488207,1920,1920,1920,1,retained evaluator standard error,complete,af45102aee11239b,d0ed4d6ed1f7ba5908b92d20c0cd549a281cb29624f336da5af8b3bb3d5b41bd,66.1979166667,-0.2083333334
+AIME2026,Native 6.25,6.250003814697266,,fixed mixed control,67.2916666667,0.6161186,1920,1920,1920,1,retained evaluator standard error,complete,b1466674187d72b9,1df08166db792683ded03d12eed8ab7016579494e606467f08c57bd542892e57,66.1979166667,1.0937500000
+AIME2026,AutoQuant 5.33,5.326392703586155,5.375,default,66.3020833333,0.6645021,1920,1920,1920,1,retained evaluator standard error,complete,43a53ba568f55c00,7fcd521a3e17526ae229ca817beeb281cabb1d750aeeeb4e5c41400b0fa78b27,66.1979166667,0.1041666666
+AIME2026,AutoQuant 5.76,5.763892703586155,5.8125,default,65.5729166667,0.7045092,1920,1920,1920,1,retained evaluator standard error,complete,1db2dea5e96e342a,c501876a61cf148bce29c62f4f702b95b66af185a958fc7b6ae72cd3cfa36f62,66.1979166667,-0.6250000000
+GPQA,BF16,16,,uniform control,59.1461489899,0.4307730663,12672,12672,12672,1,retained evaluator standard error,complete,9df121dd3156cc99,8171805e78e41f9e24d13daad0bcd6ef8686068caf6b5eac8ad5b98cb2a099af,59.1461489899,0E-10
+GPQA,FP8,8,,uniform control,59.7932449495,0.4627207691,12672,12672,12672,1,retained evaluator standard error,complete,34e2b1a38e3cd56a,39a5236f123988513add3a899c1ecc09233ad1d2e41a249e5945cce4980138db,59.1461489899,0.6470959596
+GPQA,NVFP4,4.5,,uniform control,57.9072,0.4696,12672,12672,12672,1,retained evaluator standard error,complete,c2b3e1987915554b,d4e4a40cbd527942a5669f9729da807b784d35c3cf475d3bc8fe32c1a2ac5277,59.1461489899,-1.2389489899
+GPQA,NVFP4 Four-over-Six,4.5,,uniform control,57.6626,0.4132,12672,12672,12672,1,retained evaluator standard error,complete,edb9e83c442812c1,af60b7512d641bc88e2113f69e69a1cf872af6cd5105d49aa2ec246bc1f4321a,59.1461489899,-1.4835489899
+GPQA,Native 6.25,6.250003814697266,,fixed mixed control,58.5621843434,0.4363072794,12672,12672,12672,1,retained evaluator standard error,complete,c2df0a93be8e21fc,493afe5c2fdb1450dd3a3f122e85ea8b7452e77470e96841d2eda1f4d1f2f997,59.1461489899,-0.5839646465
+GPQA,AutoQuant 5.33,5.326392703586155,5.375,default,58.0886994949,0.4368373068,12672,12672,12672,1,retained evaluator standard error,complete,cc240e2e38858572,f34cbf03f19ea5ed85bd299f87cc4fa052576f11a4650c40bb1aeed17e35b892,59.1461489899,-1.0574494950
+GPQA,AutoQuant 5.76,5.763892703586155,5.8125,default,58.9330808081,0.4295660611,12672,12672,12672,1,retained evaluator standard error,complete,330235f6a0906f48,d2d1e0e6bb4eb00015847890fd5a853f1ed8b5888e15c7b7d3acafdd54ec9d1a,59.1461489899,-0.2130681818
+LiveCodeBench,BF16,16,,uniform control,55.2588105727,0.2836609,3632,3632,3632,1,retained evaluator standard error,complete,2852516a68615637,1ee0cd0ac51642021b1362102001ab55c81357a5201275874db48f04631e1ed5,55.2588105727,0E-10
+LiveCodeBench,FP8,8,,uniform control,55.5892070485,0.2663335,3632,3632,3632,1,retained evaluator standard error,complete,8cfb0e90fcb99b51,4c08293d05f418637835db34da93b56dc9901ba33b9acfd6c9815ea8b996e322,55.2588105727,0.3303964758
+LiveCodeBench,NVFP4,4.5,,uniform control,53.359030837,0.2488875396,3632,3632,3632,1,retained evaluator standard error,complete,04dc3f9e9261080b,9a5e547f070243902e08456a7d13de4f9c83bc9aaeb18a99a22548277033438c,55.2588105727,-1.8997797357
+LiveCodeBench,NVFP4 Four-over-Six,4.5,,uniform control,54.2676211454,0.3354385051,3632,3632,3632,1,retained evaluator standard error,complete,0f6e5e140224462b,bd39fa7733a55075978bb40ab3a84f87b6fc284a85454fc05a401055da762aef,55.2588105727,-0.9911894273
+LiveCodeBench,Native 6.25,6.250003814697266,,fixed mixed control,55.4515418502,0.3899318355,3632,3632,3632,1,retained evaluator standard error,complete,cdfebca75ac1538c,31e4880a558943609594a6bcc82f38cd534c3507bd02972cdf08ce6213a02fd5,55.2588105727,0.1927312775
+LiveCodeBench,AutoQuant 5.33,5.326392703586155,5.375,default,54.7081497797,0.3555008,3632,3632,3632,1,retained evaluator standard error,complete,7e81dfdc15831851,9babcadffa70874ad36cd37fbe7744fad2367a59906c9058e325c4835c304c01,55.2588105727,-0.5506607930
+LiveCodeBench,AutoQuant 5.76,5.763892703586155,5.8125,default,54.7081497797,0.3651188,3632,3632,3632,1,retained evaluator standard error,complete,0255a6ea0cb7f9ac,429092e0ade1aad58b371e11b0e98ec8fe02091c48d0d1783be4e0f8e196b603,55.2588105727,-0.5506607930
+IFBench,BF16,16,,uniform control,32.8666666667,0.3645392831,1500,1500,1500,1,five prompt-loose repeat aggregates,complete,0e8e890b388d44a0,956e77237b5f418ef1a2466bc3d9fabf14471b6329358b37758632d0eb24c487,32.8666666667,0E-10
+IFBench,FP8,8,,uniform control,35.4666666667,0.8416914188,1500,1500,1500,1,five prompt-loose repeat aggregates,complete,ea98f540e2c5a09d,bad480451660de9c568c588e9245a6dbab82ecbe554f70c8bb07413a0a032ff5,32.8666666667,2.6000000000
+IFBench,NVFP4,4.5,,uniform control,31.8666666667,0.6828046736,1500,1500,1500,1,five prompt-loose repeat aggregates,complete,cb45f5af7c18f625,de7a10608ce3e976e8a23584d5753c8b03f4052873a019354ff2a97997349541,32.8666666667,-1.0000000000
+IFBench,NVFP4 Four-over-Six,4.5,,uniform control,31.4,0.383550663,1500,1500,1500,1,five prompt-loose repeat aggregates,complete,055829139e76fdfe,9d3399ece46717964ed42904fc35842a9a6e79950d0a801a39909764a7f020f4,32.8666666667,-1.4666666667
+IFBench,Native 6.25,6.250003814697266,,fixed mixed control,33.8,0.549343042,1500,1500,1500,1,five prompt-loose repeat aggregates,complete,09af635acac81ac5,3b8cbb86aa2c4d9c1045b8b59291175c88582a3e1ed2e969bec8f48d7b7e65a2,32.8666666667,0.9333333333
+IFBench,AutoQuant 5.33,5.326392703586155,5.375,default,33.5333333333,0.5769652406,1500,1500,1500,1,five prompt-loose repeat aggregates,complete,8ead8183bccd45b1,fe70d1ab3a728b7a0b46e6e31446be9c9d68626e2f789b5ab485aa8297c307cf,32.8666666667,0.6666666666
+IFBench,AutoQuant 5.76,5.763892703586155,5.8125,default,32.8,1.0706176826,1500,1500,1500,1,five prompt-loose repeat aggregates,complete,c5737396d3c3597c,8c05639c5edc22f3cec8ede037a0514b62a4362aedb8a67271afecc9389601c0,32.8666666667,-0.0666666667
+RULER-128K,BF16,16,,uniform control,77.2953846154,0.4808081172,6500,6500,6500,1,retained evaluator standard error,complete,35c745e2bd343f20,a60f70e5ff087d40e620574435a4ee67e065b26ae8ec85405b220e7bceb8a720,77.2953846154,0E-10
+RULER-128K,FP8,8,,uniform control,76.6792307692,0.4849603281,6500,6500,6500,1,retained evaluator standard error,complete,0da669e6b2973e47,ba4072c1c24c6db93d0ac0a016c84c4317e2a265e677ffbcdd8cf353d81b5549,77.2953846154,-0.6161538462
+RULER-128K,NVFP4,4.5,,uniform control,70.7584615385,0.5191439907,6500,6500,6500,1,retained evaluator standard error,complete,b464b6af71dfb21d,9cc6615953e3c648a1a094fbb69dd2ce50d170fd71a855ee8735703593742a9f,77.2953846154,-6.5369230769
+RULER-128K,NVFP4 Four-over-Six,4.5,,uniform control,71.2092307692,0.5216063139,6500,6500,6500,1,retained evaluator standard error,complete,98889c0725a1d596,8bfe80ca3e8eb8cb8568e2bf80a5bdc098d3e1b54257ba2aef7d19f237139547,77.2953846154,-6.0861538462
+RULER-128K,Native 6.25,6.250003814697266,,fixed mixed control,76.32,0.4867454609,6500,6500,6500,1,retained evaluator standard error,complete,77d65ef41ce14c61,ae361f2634a2f4203185f02a9a877ed119294027ae4dbfa728a87a556913bbef,77.2953846154,-0.9753846154
+RULER-128K,AutoQuant 5.33,5.326392703586155,5.375,default,75.2146153846,0.4986442535,6500,6500,6500,1,retained evaluator standard error,complete,122d0eddceaee4c4,7d3918a324ef821788b703fe349c5005ddb2eb6f438da1346b4a4138faea9350,77.2953846154,-2.0807692308
+RULER-128K,AutoQuant 5.76,5.763892703586155,5.8125,default,76.2507692308,0.4893242623,6500,6500,6500,1,retained evaluator standard error,complete,db1747e14b9da97a,bb15105e2bbdde29159b5c23290d3e9970bc3f3d5a4bc6737e7928614f812272,77.2953846154,-1.0446153846
+SciCode,BF16,16,,uniform control,20.3772189349,0.7746183547,2704,2696,2696,1,approximate binomial SE; 2704 subtasks/repeats treated as independent,fixed denominator; missing or unsuccessful generations remain failures,e547522a4e2bbaf8,5d2bb4436ae1b761555246212a6d12fa21544b1c1fc410f9dcf14c0922d44502,20.3772189349,0E-10
+SciCode,FP8,8,,uniform control,19.2307692308,2.2033861927,2704,2696,2696,1,retained evaluator standard error,fixed denominator; missing or unsuccessful generations remain failures,87f3a5b43c71f76a,92de9285fab222919c105d100ee08f71b94fd39e1bf89169472a7deba58a117d,20.3772189349,-1.1464497041
+SciCode,NVFP4,4.5,,uniform control,16.900887574,0.7206918409,2704,2696,2694,1,approximate binomial SE; 2704 subtasks/repeats treated as independent,fixed denominator; missing or unsuccessful generations remain failures,88e4268c578c62ba,f9dc1bd6417dfc223584e1ab8c2d4e2341b271e35df19b2ac05198b17b15b65c,20.3772189349,-3.4763313609
+SciCode,NVFP4 Four-over-Six,4.5,,uniform control,17.6775147929,2.9004117626,2704,2696,2696,1,retained evaluator standard error,fixed denominator; missing or unsuccessful generations remain failures,5352b42e3f30598c,05d00e48f8256237a56f27d0308d7251edcfdcf474b25909c4e7011507a7eb6d,20.3772189349,-2.6997041420
+SciCode,Native 6.25,6.250003814697266,,fixed mixed control,19.55128205128205,0.27036134368172215,8112,8072,8064,3,sample SD of three independent run accuracies / sqrt(3),fixed denominator in each run; all failures retained,ee00b3dfd7dbcdd6;0f9b9be765215a7a;489463b1115e73cb,819b5578160d959cd746c34d0b5835b4a34ca23df918082b53ee6aaa437ea3c7;0134f5e28b67d9584a79876a7541edcc6665538d6539b840fd3b432cdcb89719;17f4e54c414418dbab8caef23ab12c9aa0ec6b8205cb66b8a4b9b3433cb88d1f,20.3772189349,-0.82593688361795
+SciCode,AutoQuant 5.33,5.326392703586155,5.375,default,19.6375739645,1.6010860572,2704,2696,2696,1,retained evaluator standard error,fixed denominator; missing or unsuccessful generations remain failures,05039954415822c5,6f35da30a947ff07c41eef03ba3cdf60ef57fa81e8281659b6246cd4bc6e46fc,20.3772189349,-0.7396449704
+SciCode,AutoQuant 5.76,5.763892703586155,5.8125,default,20.47477744807122,3.151671891914785,2704,2692,2690,1,retained evaluator standard error,fixed denominator; missing or unsuccessful generations remain failures,58c99323c11e8f64,3642e29604c1c24fd3c22f546dca43379a5717085ebfb4161e69c7a0202b1772,20.3772189349,0.09755851317122
diff --git a/docs/source/announcements/assets/autoquant-kv-search.svg b/docs/source/announcements/assets/autoquant-kv-search.svg
new file mode 100644
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--- /dev/null
+++ b/docs/source/announcements/assets/autoquant-kv-search.svg
@@ -0,0 +1,113 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="640" height="796" viewBox="0 0 640 796" role="img" aria-labelledby="title desc">
+<title id="title">From layer sensitivity to a mixed-precision KV recipe</title>
+<desc id="desc">Illustrative search with four equal-width layers. Compare each isolated layer/candidate trial with a BF16 KV reference. NVFP4 sensitivities are 0.03, 0.32, 0.09, 0.02; FP8 sensitivities are 0.01, 0.02, 0.01, 0.01. Under a 5.375-bit budget, select NVFP4, FP8, NVFP4, NVFP4. Upgrading layer 2 gives the largest sensitivity reduction, 0.30.</desc>
+<defs><marker id="arrow" viewBox="0 0 10 10" refX="8" refY="5" markerWidth="5" markerHeight="5" orient="auto-start-reverse"><path d="M 0 0 L 10 5 L 0 10 Z" fill="#64748b"/></marker></defs>
+<style>
+text { font-family: Arial, Helvetica, sans-serif; fill: #172b3a; }
+.heading { font-size: 25px; font-weight: 700; }
+.label { font-size: 21px; }
+.small { font-size: 20px; fill: #475569; }
+.layer { font-size: 20px; fill: #64748b; text-anchor: middle; }
+.format { font-size: 20px; font-weight: 700; text-anchor: middle; }
+.score { font-size: 25px; font-weight: 600; text-anchor: middle; }
+.panel { fill: #f8fafc; stroke: #dbe3eb; }
+.bf16 { fill: #e9edf2; stroke: #cbd5e1; }
+.nvfp4 { fill: #eaf5d8; stroke: #7ea93e; }
+.fp8 { fill: #e2edff; stroke: #4d83c8; }
+.flow { fill: none; stroke: #64748b; stroke-width: 1.7; marker-end: url(#arrow); }
+</style>
+<rect x="0" y="0" width="640" height="796" rx="16" class="" fill="#fff"/>
+<text x="24" y="34" class="" font-size="28" font-weight="700">From sensitivity to a KV recipe</text>
+<text x="24" y="62" class="small" >Illustrative example · four equal-width layers</text>
+<rect x="12" y="84" width="616" height="222" rx="14" class="panel" />
+<circle cx="40" cy="114" r="17" fill="#172b3a"/>
+<text x="40" y="121" class="format" style="fill: #fff">1</text>
+<text x="68" y="122" class="heading" >Measure each layer / candidate</text>
+<text x="32" y="151" class="small" >Same prompts. Change one layer’s KV format.</text>
+<text x="212" y="178" class="layer" >L1</text>
+<text x="312" y="178" class="layer" >L2</text>
+<text x="412" y="178" class="layer" >L3</text>
+<text x="512" y="178" class="layer" >L4</text>
+<text x="32" y="212" class="label" >Reference</text>
+<rect x="172" y="186" width="80" height="36" rx="6" class="bf16" />
+<text x="212" y="211" class="format" >BF16</text>
+<path d="M 253 204 H 268" class="flow"/>
+<rect x="272" y="186" width="80" height="36" rx="6" class="bf16" />
+<text x="312" y="211" class="format" >BF16</text>
+<path d="M 353 204 H 368" class="flow"/>
+<rect x="372" y="186" width="80" height="36" rx="6" class="bf16" />
+<text x="412" y="211" class="format" >BF16</text>
+<path d="M 453 204 H 468" class="flow"/>
+<rect x="472" y="186" width="80" height="36" rx="6" class="bf16" />
+<text x="512" y="211" class="format" >BF16</text>
+<text x="32" y="256" class="label" >Trial</text>
+<rect x="172" y="230" width="80" height="36" rx="6" class="bf16" />
+<text x="212" y="255" class="format" >BF16</text>
+<path d="M 253 248 H 268" class="flow"/>
+<rect x="272" y="230" width="80" height="36" rx="6" class="nvfp4" stroke-width="2.2"/>
+<text x="312" y="255" class="format" >NVFP4</text>
+<path d="M 353 248 H 368" class="flow"/>
+<rect x="372" y="230" width="80" height="36" rx="6" class="bf16" />
+<text x="412" y="255" class="format" >BF16</text>
+<path d="M 453 248 H 468" class="flow"/>
+<rect x="472" y="230" width="80" height="36" rx="6" class="bf16" />
+<text x="512" y="255" class="format" >BF16</text>
+<path d="M 555 204 H 606 V 287 H 592" class="flow"/>
+<path d="M 555 248 H 580 V 271 H 472 V 278" class="flow"/>
+<text x="32" y="292" class="small" >Restore, then repeat for all choices</text>
+<rect x="428" y="279" width="164" height="24" rx="5" class="" fill="#e4eaf1"/>
+<text x="510" y="298" class="" font-size="20" font-weight="700" text-anchor="middle">Output KL</text>
+<path d="M 320 313 V 331" class="flow"/>
+<rect x="12" y="342" width="616" height="192" rx="14" class="panel" />
+<circle cx="40" cy="372" r="17" fill="#172b3a"/>
+<text x="40" y="379" class="format" style="fill: #fff">2</text>
+<text x="68" y="380" class="heading" >Build the sensitivity map</text>
+<text x="32" y="409" class="small" >Lower KL = closer to the reference</text>
+<text x="212" y="436" class="layer" >L1</text>
+<text x="312" y="436" class="layer" >L2</text>
+<text x="412" y="436" class="layer" >L3</text>
+<text x="512" y="436" class="layer" >L4</text>
+<rect x="28" y="446" width="105" height="30" rx="6" class="nvfp4" />
+<text x="80" y="468" class="format" >NVFP4</text>
+<rect x="172" y="442" width="80" height="38" rx="6" class="" fill="#edf1f5"/>
+<text x="212" y="469" class="score" style="fill: #172b3a">0.03</text>
+<rect x="272" y="442" width="80" height="38" rx="6" class="" fill="#ffdfce"/>
+<text x="312" y="469" class="score" style="fill: #953c12">0.32</text>
+<rect x="372" y="442" width="80" height="38" rx="6" class="" fill="#edf1f5"/>
+<text x="412" y="469" class="score" style="fill: #172b3a">0.09</text>
+<rect x="472" y="442" width="80" height="38" rx="6" class="" fill="#edf1f5"/>
+<text x="512" y="469" class="score" style="fill: #172b3a">0.02</text>
+<rect x="28" y="490" width="105" height="30" rx="6" class="fp8" />
+<text x="80" y="512" class="format" >FP8</text>
+<rect x="172" y="486" width="80" height="38" rx="6" class="" fill="#edf1f5"/>
+<text x="212" y="513" class="score" style="fill: #172b3a">0.01</text>
+<rect x="272" y="486" width="80" height="38" rx="6" class="" fill="#e2edff"/>
+<text x="312" y="513" class="score" style="fill: #285e9d">0.02</text>
+<rect x="372" y="486" width="80" height="38" rx="6" class="" fill="#edf1f5"/>
+<text x="412" y="513" class="score" style="fill: #172b3a">0.01</text>
+<rect x="472" y="486" width="80" height="38" rx="6" class="" fill="#edf1f5"/>
+<text x="512" y="513" class="score" style="fill: #172b3a">0.01</text>
+<path d="M 320 541 V 573" class="flow"/>
+<rect x="376" y="545" width="238" height="30" rx="15" class="" fill="#172b3a"/>
+<text x="495" y="567" class="label" style="fill: #fff" text-anchor="middle">Budget: 5.375 bits</text>
+<path d="M 372 560 H 324" class="flow"/>
+<rect x="12" y="586" width="616" height="162" rx="14" class="panel" />
+<circle cx="40" cy="616" r="17" fill="#172b3a"/>
+<text x="40" y="623" class="format" style="fill: #fff">3</text>
+<text x="68" y="624" class="heading" >Optimize and assign formats</text>
+<text x="32" y="653" class="small" >Minimum total KL within the bit budget</text>
+<text x="212" y="680" class="layer" >L1</text>
+<text x="312" y="680" class="layer" >L2</text>
+<text x="412" y="680" class="layer" >L3</text>
+<text x="512" y="680" class="layer" >L4</text>
+<text x="32" y="715" class="label" >Selected KV</text>
+<rect x="172" y="689" width="80" height="36" rx="6" class="nvfp4" />
+<text x="212" y="714" class="format" >NVFP4</text>
+<rect x="272" y="689" width="80" height="36" rx="6" class="fp8" stroke-width="2.2"/>
+<text x="312" y="714" class="format" >FP8</text>
+<rect x="372" y="689" width="80" height="36" rx="6" class="nvfp4" />
+<text x="412" y="714" class="format" >NVFP4</text>
+<rect x="472" y="689" width="80" height="36" rx="6" class="nvfp4" />
+<text x="512" y="714" class="format" >NVFP4</text>
+<text x="320" y="779" class="label" text-anchor="middle">(4.5 + 8 + 4.5 + 4.5) / 4 = 5.375 bits</text>
+</svg>
diff --git a/docs/source/announcements/autoquant-kv.rst b/docs/source/announcements/autoquant-kv.rst
new file mode 100644
index 00000000000..4920976708d
--- /dev/null
+++ b/docs/source/announcements/autoquant-kv.rst
@@ -0,0 +1,218 @@
+:orphan:
+
+AutoQuant KV: Automatic Mixed-Precision KV Cache Quantization
+#############################################################
+
+:Author: Model Optimizer Team
+:Date: October 8, 2026
+:Tags: autoquantize, kv-cache, quantization, mixed-precision, modelopt
+
+Introduction to AutoQuant
+*************************
+
+**AutoQuant automates mixed-precision quantization.** Instead of assigning one
+format everywhere, NVIDIA Model Optimizer measures layer sensitivity and
+selects formats under a storage budget. Sensitive layers retain more precision;
+less sensitive layers use fewer bits.
+
+GEMM AutoQuant applies this idea to the model's matrix multiplications.
+**KV AutoQuant extends it to attention keys and values**, whose storage grows
+with context length and concurrent requests. The two searches can be combined,
+with independent budgets for weights and the KV cache.
+
+Below, we explain the search workflow and API, then use Qwen3-8B as a case
+study of the accuracy and memory tradeoff.
+
+How KV AutoQuant works
+**********************
+
+KV AutoQuant searches over K/V format pairs using representative prompts and
+an average-bit budget.
+
+**Prepare candidates.** The starter recipe uses constant scales and offers FP8
+K/V (8 bits) and NVFP4 K/V (4.5 bits including block scales). The case study
+also includes FP8 K with NVFP4 V (6.25 bits for equal K/V widths).
+
+**Score each choice.** Compare each isolated layer/candidate trial against a
+reference with K/V quantization disabled in all eligible layers. KL divergence
+measures the change in output predictions; existing GEMM quantizers stay fixed.
+BF16 K/V is the reference, not an implicit solver choice.
+
+.. figure:: assets/autoquant-kv-search.svg
+   :alt: Three-stage illustrative search: score isolated layer/candidate trials against BF16 KV, build a sensitivity map, and optimize under a 5.375-bit budget. NVFP4 scores are 0.03, 0.32, 0.09, and 0.02; FP8 scores are 0.01, 0.02, 0.01, and 0.01. Select FP8 for layer 2 and NVFP4 for the other three layers.
+   :width: 640px
+   :align: center
+
+   Layer 2 receives FP8 because its upgrade gives the largest error reduction
+   within the budget. Values are illustrative, with equal K/V widths across layers.
+
+**Select one candidate per layer.** An integer program minimizes total
+sensitivity under the storage constraint:
+
+.. math::
+
+   \begin{gathered}
+   \min_{\{c_\ell\}}\sum_\ell s_{\ell,c_\ell} \\[4pt]
+   \text{subject to}\quad
+   \frac{\sum_\ell w_\ell b_{\ell,c_\ell}}{\sum_\ell w_\ell} \le B.
+   \end{gathered}
+
+Here :math:`s` is KL sensitivity, :math:`b` is the width-weighted K/V bit cost
+including scales, :math:`w` is the layer's total K/V width, and :math:`B` is
+``effective_bits``. Layers matched by ``disabled_layers`` are preserved and
+excluded from the budget.
+
+**Apply and reuse.** Validate the assembled model on held-out tasks: summed
+sensitivity is an estimate of combined error. A saved search checkpoint lets
+you change the budget without rescoring, provided you restart from the same
+pre-search model state and keep scoring data, candidates, and scales unchanged.
+
+API and examples
+****************
+
+KV AutoQuant uses ``modelopt.torch.quantization.auto_quantize`` with
+``cost_model="kv_cache"`` and ``method="kl_div"``. Given a Hugging Face causal
+LM and a batch-size-one calibration loader yielding device-placed
+``input_ids`` and ``attention_mask`` tensors:
+
+.. code-block:: python
+
+   import modelopt.torch.quantization as mtq
+   from modelopt.recipe import load_recipe
+
+   cfg = load_recipe(
+       "general/auto_quantize/kv_fp8_nvfp4_cast_kl_div_at_5p4bits"
+   ).auto_quantize
+
+   def forward_step(model, batch):
+       logits = model(**batch, use_cache=False).logits
+       return logits[batch["attention_mask"].bool()]
+
+   model, search_state = mtq.auto_quantize(
+       model.eval(),
+       constraints={"cost_model": "kv_cache", "effective_bits": 5.4},
+       quantization_formats=[
+           c.model_dump(exclude_none=True) for c in cfg.candidate_formats
+       ],
+       data_loader=calib_loader,
+       forward_step=forward_step,
+       disabled_layers=cfg.disabled_layers,
+       num_calib_steps=len(calib_loader),
+       num_score_steps=min(len(calib_loader), cfg.score_size),
+       method="kl_div",
+       checkpoint="kv_search.pth",
+   )
+
+For search **and Hugging Face export**, use the shipped example. In a CUDA
+environment, install a checkout containing ModelOpt
+`#2272 <https://github.com/NVIDIA/Model-Optimizer/pull/2272>`_ and
+`#2273 <https://github.com/NVIDIA/Model-Optimizer/pull/2273>`_:
+
+.. code-block:: bash
+
+   git clone https://github.com/NVIDIA/Model-Optimizer.git Model-Optimizer-autoquant-kv
+   cd Model-Optimizer-autoquant-kv
+   git checkout 1d392999b45626f9c06ff0bc9797b03529fe24e3
+   python -m pip install -e '.[hf]'
+   python -m pip install -r examples/hf_ptq/requirements.txt
+
+   mkdir -p outputs/qwen3-8b-kv
+   python examples/hf_ptq/hf_ptq.py \
+     --pyt_ckpt_path Qwen/Qwen3-8B \
+     --recipe general/auto_quantize/kv_fp8_nvfp4_cast_kl_div_at_5p4bits \
+     --dataset cnn_dailymail --calib_size 128 --calib_seq 512 --batch_size 1 \
+     --kv_auto_quantize_checkpoint outputs/qwen3-8b-kv/search.pth \
+     --export_path outputs/qwen3-8b-kv/hf
+
+The starter recipe searches full FP8 K/V and full NVFP4 K/V with constant
+scales. This small example demonstrates the workflow; use representative data
+for an application recipe. Preserve ``hf_quant_config.json`` and exported
+scales when serving with a compatible runtime.
+
+Combining KV and GEMM AutoQuant
+*******************************
+
+A composed recipe runs **GEMM AutoQuant first, then KV AutoQuant**. The shipped
+example uses gradient-based NVFP4/FP8 GEMM search, freezes its selected
+weight/activation quantizers and calibrated tensors, then runs forward-KL KV
+search. Its 5.4-bit weight and 5.4-bit KV targets constrain separate storage
+domains; this is a sequential search, not one joint bit budget.
+
+Use the command above with the following recipe and separate search-state
+files, plus a fresh export directory:
+
+.. code-block:: bash
+
+   --recipe general/auto_quantize/nvfp4_fp8_gradient_then_kv_fp8_nvfp4_cast_kl_div_at_5p4bits \
+   --auto_quantize_checkpoint gemm_search.pth \
+   --kv_auto_quantize_checkpoint kv_after_gemm_search.pth
+
+Changing the GEMM configuration requires recomputing KV sensitivity with a new
+checkpoint. Fixed GEMM PTQ can also precede KV search; the
+`HF PTQ guide <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/hf_ptq#autoquantize>`_
+describes both compositions. Serving support must cover the combined weight
+and KV formats; an export marked ``kv_cache_deployment_supported: false``
+requires additional runtime support.
+
+Mixed-precision results
+***********************
+
+In our **Qwen3-8B case study**, AQ at **5.76 effective KV bits** reduces modeled cache
+payload by **64.0% versus BF16**, with a **−1.04 percentage-point change on
+RULER-128K**, compared with −6.54 for uniform NVFP4. These results isolate KV
+quantization: all model weights remain BF16. They do not measure the composed
+GEMM+KV recipe above.
+
+.. image:: assets/autoquant-kv-qwen3-8b-accuracy-memory.png
+   :alt: AutoQuant 5.76 uses 1.62 GiB of modeled KV payload versus 4.50 GiB for BF16 at 32768 tokens; RULER accuracy changes by minus 1.04 points versus minus 6.54 for NVFP4.
+   :width: 100%
+
+**BF16 shows accuracy (%); every recipe column shows result minus BF16 in
+percentage points. Negative means an accuracy drop; positive means an
+improvement.** Differences use unrounded scores. AutoQuant results use only
+default K/V scales of 1.0.
+
+.. csv-table:: Accuracy change: result − BF16 (percentage points)
+   :header: "Benchmark","BF16 (%)","FP8","NVFP4","NVFP4 4/6","Native 6.25","AQ 5.33","AQ 5.76"
+
+   "AIME2025","67.03","-0.42","—","—","-0.83","-1.88","-0.26"
+   "AIME2026","66.20","+0.73","-0.05","-0.21","+1.09","+0.10","-0.63"
+   "GPQA","59.15","+0.65","-1.24","-1.48","-0.58","-1.06","-0.21"
+   "LiveCodeBench","55.26","+0.33","-1.90","-0.99","+0.19","-0.55","-0.55"
+   "IFBench","32.87","+2.60","-1.00","-1.47","+0.93","+0.67","-0.07"
+   "RULER-128K","77.30","-0.62","-6.54","-6.09","-0.98","-2.08","-1.04"
+   "SciCode","20.38","-1.15","-3.48","-2.70","-0.83","-0.74","+0.10"
+
+
+**Native 6.25** is a fixed mixed-precision layer map. **AQ 5.33** and
+**AQ 5.76** are AutoQuant allocations at their achieved bit costs.
+**NVFP4 4/6** uses Four-over-Six block-scale selection. AQ 5.76 uses **7.8% less
+KV payload than Native 6.25**, with accuracy within 0.63 points of BF16 on
+AIME2025, AIME2026, GPQA, LiveCodeBench, and IFBench.
+
+The study searched FP8 K/V, FP8 K with NVFP4 V, and NVFP4 K/V. Both AQ maps keep
+all values in NVFP4; 17 of 36 layers use FP8 keys at 5.33 bits, and 26 at 5.76
+bits. Their requested budgets were 5.375 and 5.8125 bits.
+
+.. image:: assets/autoquant-kv-qwen3-8b-layer-allocation.png
+   :alt: AutoQuant selects FP8 keys in 17 of 36 layers at 5.33 bits and 26 layers at 5.76 bits; all value caches use NVFP4.
+   :width: 100%
+
+The study used a development runtime with asymmetric K/V support, beyond the
+starter recipe's candidate menu. See the
+`vLLM integration <https://github.com/vllm-project/vllm/pull/56116>`_ for the
+per-layer FP8/NVFP4 path. Payload calculations include block scales but exclude
+runtime overhead and weights; they are not total GPU-memory or throughput measurements.
+
+.. rst-class:: table-note
+
+   All comparisons use the same development vLLM image (TP1/DP4) and matched
+   settings per benchmark. Dashes withhold unmatched AIME2025 controls.
+   SciCode counts missing/unsuccessful generations as failures in 2,704 trials
+   per run; Native 6.25 averages three runs. Differences are point estimates,
+   not significance claims. Download the
+   :download:`results CSV <assets/autoquant-kv-qwen3-8b-results.csv>` for scores,
+   standard errors, and response counts, and the
+   :download:`evaluation setup <assets/autoquant-kv-qwen3-8b-methods.json>` for
+   search data and benchmark settings. Study checkpoints and runtime are not
+   distributed with this post.
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 8ecd7d0aa38..cf276b63a93 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -13,6 +13,7 @@ Release notes, technical updates, examples, and deployment stories from the Mode
          <button class="announcement-tag is-active" type="button" data-tag="all" aria-pressed="true">All</button>
          <button class="announcement-tag" type="button" data-tag="release" aria-pressed="false">Release</button>
          <button class="announcement-tag" type="button" data-tag="autoquantize" aria-pressed="false">AutoQuantize</button>
+         <button class="announcement-tag" type="button" data-tag="kv-cache" aria-pressed="false">KV cache</button>
          <button class="announcement-tag" type="button" data-tag="quantization" aria-pressed="false">Quantization</button>
          <button class="announcement-tag" type="button" data-tag="nvfp4" aria-pressed="false">NVFP4</button>
          <button class="announcement-tag" type="button" data-tag="qad" aria-pressed="false">QAD</button>
@@ -29,6 +30,12 @@ Release notes, technical updates, examples, and deployment stories from the Mode
      </div>
 
    <div class="announcement-grid" id="announcement-grid">
+     <article class="announcement-card" data-date="2026-10-08" data-title="AutoQuant KV: Automatic Mixed-Precision KV Cache Quantization" data-summary="Learn how AutoQuant selects per-layer KV formats under a storage budget and combines with GEMM quantization, with Qwen3-8B as a case study." data-tags="autoquantize kv-cache quantization mixed-precision modelopt">
+       <div class="announcement-card-meta">October 8, 2026 &middot; Model Optimizer Team</div>
+       <h2><a href="announcements/autoquant-kv.html">AutoQuant KV: Automatic Mixed-Precision KV Cache Quantization</a></h2>
+       <p>Learn how AutoQuant selects per-layer KV formats under a storage budget and combines with GEMM quantization, with Qwen3-8B as a case study.</p>
+       <div class="announcement-card-tags"><span>autoquantize</span><span>kv-cache</span><span>quantization</span><span>mixed-precision</span><span>modelopt</span></div>
+     </article>
      <article class="announcement-card" data-date="2026-09-16" data-title="Scale Learning: Training NVFP4 Block Scales to Recover Accuracy" data-summary="How ModelOpt learns NVFP4 per-block weight scales with gradients instead of setting them with a calibration rule, and what that recovers." data-tags="scale-learning quantization nvfp4 qad modelopt">
        <div class="announcement-card-meta">September 16, 2026 &middot; Model Optimizer Team</div>
        <h2><a href="announcements/scale-learning.html">Scale Learning: Training NVFP4 Block Scales to Recover Accuracy</a></h2>

From d6ecde2641e15a32db82c564566a6c4eff978435 Mon Sep 17 00:00:00 2001
From: weimingc <17592131+meenchen@users.noreply.github.com>
Date: Thu, 8 Oct 2026 10:38:00 -0700
Subject: [PATCH 2/4] docs: use SVG consistently for AutoQuant KV figures

Signed-off-by: weimingc <17592131+meenchen@users.noreply.github.com>
---
 .../autoquant-kv-qwen3-8b-accuracy-memory.png | Bin 125821 -> 0 bytes
 .../autoquant-kv-qwen3-8b-accuracy-memory.svg | 621 +++++++++++++++
 ...autoquant-kv-qwen3-8b-layer-allocation.png | Bin 55619 -> 0 bytes
 ...autoquant-kv-qwen3-8b-layer-allocation.svg | 713 ++++++++++++++++++
 docs/source/announcements/autoquant-kv.rst    |   4 +-
 5 files changed, 1336 insertions(+), 2 deletions(-)
 delete mode 100644 docs/source/announcements/assets/autoquant-kv-qwen3-8b-accuracy-memory.png
 create mode 100644 docs/source/announcements/assets/autoquant-kv-qwen3-8b-accuracy-memory.svg
 delete mode 100644 docs/source/announcements/assets/autoquant-kv-qwen3-8b-layer-allocation.png
 create mode 100644 docs/source/announcements/assets/autoquant-kv-qwen3-8b-layer-allocation.svg

diff --git a/docs/source/announcements/assets/autoquant-kv-qwen3-8b-accuracy-memory.png b/docs/source/announcements/assets/autoquant-kv-qwen3-8b-accuracy-memory.png
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+     </g>
+    </g>
+    <g id="text_19">
+     <text style="font-size: 11px; font-family: 'Arial', 'DejaVu Sans', 'Bitstream Vera Sans', 'Computer Modern Sans Serif', 'Lucida Grande', 'Verdana', 'Geneva', 'Lucid', 'Helvetica', 'Avant Garde', sans-serif; text-anchor: middle" x="522.72" y="201.699543" transform="rotate(-0 522.72 201.699543)">Attention layer (zero-based)</text>
+    </g>
+   </g>
+   <g id="matplotlib.axis_2">
+    <g id="ytick_1">
+     <g id="line2d_19">
+      <defs>
+       <path id="me536f3ab90" d="M 0 0
+L -3.5 0
+" style="stroke: #000000; stroke-width: 0.8"/>
+      </defs>
+      <g>
+       <use xlink:href="#me536f3ab90" x="198.72" y="95.823" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_20">
+      <text style="font-size: 11px; font-family: 'Arial', 'DejaVu Sans', 'Bitstream Vera Sans', 'Computer Modern Sans Serif', 'Lucida Grande', 'Verdana', 'Geneva', 'Lucid', 'Helvetica', 'Avant Garde', sans-serif; text-anchor: end" x="191.72" y="99.82715" transform="rotate(-0 191.72 99.82715)">5.33 bits: 17 FP8 K layers</text>
+     </g>
+    </g>
+    <g id="ytick_2">
+     <g id="line2d_20">
+      <g>
+       <use xlink:href="#me536f3ab90" x="198.72" y="146.853" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_21">
+      <text style="font-size: 11px; font-family: 'Arial', 'DejaVu Sans', 'Bitstream Vera Sans', 'Computer Modern Sans Serif', 'Lucida Grande', 'Verdana', 'Geneva', 'Lucid', 'Helvetica', 'Avant Garde', sans-serif; text-anchor: end" x="191.72" y="150.85715" transform="rotate(-0 191.72 150.85715)">5.76 bits: 26 FP8 K layers</text>
+     </g>
+    </g>
+   </g>
+   <g id="patch_3">
+    <path d="M 198.72 172.368
+L 198.72 70.308
+" style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/>
+   </g>
+   <g id="patch_4">
+    <path d="M 198.72 172.368
+L 846.72 172.368
+" style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/>
+   </g>
+  </g>
+  <g id="text_22">
+   <text style="font-weight: 700; font-size: 17px; font-family: 'Arial', 'DejaVu Sans', 'Bitstream Vera Sans', 'Computer Modern Sans Serif', 'Lucida Grande', 'Verdana', 'Geneva', 'Lucid', 'Helvetica', 'Avant Garde', sans-serif; text-anchor: start" x="34.56" y="16.912465" transform="rotate(-0 34.56 16.912465)">AutoQuant spends more precision on selected keys</text>
+  </g>
+  <g id="text_23">
+   <text style="font-size: 10px; font-family: 'Arial', 'DejaVu Sans', 'Bitstream Vera Sans', 'Computer Modern Sans Serif', 'Lucida Grande', 'Verdana', 'Geneva', 'Lucid', 'Helvetica', 'Avant Garde', sans-serif; text-anchor: start; fill: #4b5563" x="34.56" y="40.824" transform="rotate(-0 34.56 40.824)">Green: FP8 K / NVFP4 V    •    Gray: NVFP4 K / NVFP4 V    •    All 36 V caches remain NVFP4</text>
+  </g>
+ </g>
+ <defs>
+  <clipPath id="p29ec064f48">
+   <rect x="198.72" y="70.308" width="648" height="102.06"/>
+  </clipPath>
+ </defs>
+</svg>
diff --git a/docs/source/announcements/autoquant-kv.rst b/docs/source/announcements/autoquant-kv.rst
index 4920976708d..e9d2cd17012 100644
--- a/docs/source/announcements/autoquant-kv.rst
+++ b/docs/source/announcements/autoquant-kv.rst
@@ -163,7 +163,7 @@ RULER-128K**, compared with −6.54 for uniform NVFP4. These results isolate KV
 quantization: all model weights remain BF16. They do not measure the composed
 GEMM+KV recipe above.
 
-.. image:: assets/autoquant-kv-qwen3-8b-accuracy-memory.png
+.. image:: assets/autoquant-kv-qwen3-8b-accuracy-memory.svg
    :alt: AutoQuant 5.76 uses 1.62 GiB of modeled KV payload versus 4.50 GiB for BF16 at 32768 tokens; RULER accuracy changes by minus 1.04 points versus minus 6.54 for NVFP4.
    :width: 100%
 
@@ -194,7 +194,7 @@ The study searched FP8 K/V, FP8 K with NVFP4 V, and NVFP4 K/V. Both AQ maps keep
 all values in NVFP4; 17 of 36 layers use FP8 keys at 5.33 bits, and 26 at 5.76
 bits. Their requested budgets were 5.375 and 5.8125 bits.
 
-.. image:: assets/autoquant-kv-qwen3-8b-layer-allocation.png
+.. image:: assets/autoquant-kv-qwen3-8b-layer-allocation.svg
    :alt: AutoQuant selects FP8 keys in 17 of 36 layers at 5.33 bits and 26 layers at 5.76 bits; all value caches use NVFP4.
    :width: 100%
 

From 158c3ba6775957ad365cf838cca8650705070718 Mon Sep 17 00:00:00 2001
From: weimingc <17592131+meenchen@users.noreply.github.com>
Date: Thu, 8 Oct 2026 12:11:51 -0700
Subject: [PATCH 3/4] docs: remove AutoQuant KV download callout

Signed-off-by: weimingc <17592131+meenchen@users.noreply.github.com>
---
 docs/source/announcements/autoquant-kv.rst | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/docs/source/announcements/autoquant-kv.rst b/docs/source/announcements/autoquant-kv.rst
index e9d2cd17012..b4187cb3c2e 100644
--- a/docs/source/announcements/autoquant-kv.rst
+++ b/docs/source/announcements/autoquant-kv.rst
@@ -210,9 +210,5 @@ runtime overhead and weights; they are not total GPU-memory or throughput measur
    settings per benchmark. Dashes withhold unmatched AIME2025 controls.
    SciCode counts missing/unsuccessful generations as failures in 2,704 trials
    per run; Native 6.25 averages three runs. Differences are point estimates,
-   not significance claims. Download the
-   :download:`results CSV <assets/autoquant-kv-qwen3-8b-results.csv>` for scores,
-   standard errors, and response counts, and the
-   :download:`evaluation setup <assets/autoquant-kv-qwen3-8b-methods.json>` for
-   search data and benchmark settings. Study checkpoints and runtime are not
-   distributed with this post.
+   not significance claims. Study checkpoints and runtime are not distributed
+   with this post.

From 6f9d008be2343a9f9183daead81db16ff2069f41 Mon Sep 17 00:00:00 2001
From: weimingc <17592131+meenchen@users.noreply.github.com>
Date: Fri, 9 Oct 2026 09:26:07 -0700
Subject: [PATCH 4/4] docs: clarify KV search baseline and candidate support

Signed-off-by: weimingc <17592131+meenchen@users.noreply.github.com>
---
 .../assets/autoquant-kv-search.svg            |  4 ++--
 docs/source/announcements/autoquant-kv.rst    | 21 +++++++++++++------
 2 files changed, 17 insertions(+), 8 deletions(-)

diff --git a/docs/source/announcements/assets/autoquant-kv-search.svg b/docs/source/announcements/assets/autoquant-kv-search.svg
index 720fb58db9a..b523da783dc 100644
--- a/docs/source/announcements/assets/autoquant-kv-search.svg
+++ b/docs/source/announcements/assets/autoquant-kv-search.svg
@@ -1,6 +1,6 @@
 <svg xmlns="http://www.w3.org/2000/svg" width="640" height="796" viewBox="0 0 640 796" role="img" aria-labelledby="title desc">
 <title id="title">From layer sensitivity to a mixed-precision KV recipe</title>
-<desc id="desc">Illustrative search with four equal-width layers. Compare each isolated layer/candidate trial with a BF16 KV reference. NVFP4 sensitivities are 0.03, 0.32, 0.09, 0.02; FP8 sensitivities are 0.01, 0.02, 0.01, 0.01. Under a 5.375-bit budget, select NVFP4, FP8, NVFP4, NVFP4. Upgrading layer 2 gives the largest sensitivity reduction, 0.30.</desc>
+<desc id="desc">Illustrative search with four equal-width layers. GEMM settings stay fixed in the reference and every trial, whether BF16 or already quantized. Compare each isolated layer/candidate trial with a BF16 KV reference. NVFP4 sensitivities are 0.03, 0.32, 0.09, 0.02; FP8 sensitivities are 0.01, 0.02, 0.01, 0.01. Under a 5.375-bit budget, select NVFP4, FP8, NVFP4, NVFP4. Upgrading layer 2 gives the largest sensitivity reduction, 0.30.</desc>
 <defs><marker id="arrow" viewBox="0 0 10 10" refX="8" refY="5" markerWidth="5" markerHeight="5" orient="auto-start-reverse"><path d="M 0 0 L 10 5 L 0 10 Z" fill="#64748b"/></marker></defs>
 <style>
 text { font-family: Arial, Helvetica, sans-serif; fill: #172b3a; }
@@ -23,7 +23,7 @@ text { font-family: Arial, Helvetica, sans-serif; fill: #172b3a; }
 <circle cx="40" cy="114" r="17" fill="#172b3a"/>
 <text x="40" y="121" class="format" style="fill: #fff">1</text>
 <text x="68" y="122" class="heading" >Measure each layer / candidate</text>
-<text x="32" y="151" class="small" >Same prompts. Change one layer’s KV format.</text>
+<text x="32" y="151" class="small" >GEMM fixed; change one layer’s KV format.</text>
 <text x="212" y="178" class="layer" >L1</text>
 <text x="312" y="178" class="layer" >L2</text>
 <text x="412" y="178" class="layer" >L3</text>
diff --git a/docs/source/announcements/autoquant-kv.rst b/docs/source/announcements/autoquant-kv.rst
index b4187cb3c2e..bc9e4097eb9 100644
--- a/docs/source/announcements/autoquant-kv.rst
+++ b/docs/source/announcements/autoquant-kv.rst
@@ -32,14 +32,18 @@ an average-bit budget.
 **Prepare candidates.** The starter recipe uses constant scales and offers FP8
 K/V (8 bits) and NVFP4 K/V (4.5 bits including block scales). The case study
 also includes FP8 K with NVFP4 V (6.25 bits for equal K/V widths).
+NVFP4 K with FP8 V is excluded because the current export path does not
+support that pair; this study did not evaluate its accuracy.
 
-**Score each choice.** Compare each isolated layer/candidate trial against a
-reference with K/V quantization disabled in all eligible layers. KL divergence
-measures the change in output predictions; existing GEMM quantizers stay fixed.
-BF16 K/V is the reference, not an implicit solver choice.
+**Score each choice.** GEMM settings stay fixed in the reference and every
+trial: BF16 when starting from a BF16 model, or the existing quantized formats
+and scales. The reference disables K/V quantization in all eligible layers.
+Each trial changes only one layer's K/V pair; KL divergence measures the
+change in output predictions. BF16 K/V is the reference, not an implicit
+solver choice.
 
 .. figure:: assets/autoquant-kv-search.svg
-   :alt: Three-stage illustrative search: score isolated layer/candidate trials against BF16 KV, build a sensitivity map, and optimize under a 5.375-bit budget. NVFP4 scores are 0.03, 0.32, 0.09, and 0.02; FP8 scores are 0.01, 0.02, 0.01, and 0.01. Select FP8 for layer 2 and NVFP4 for the other three layers.
+   :alt: Three-stage illustrative search with GEMM settings fixed in the reference and every trial: score isolated layer/candidate trials against BF16 KV, build a sensitivity map, and optimize under a 5.375-bit budget. NVFP4 scores are 0.03, 0.32, 0.09, and 0.02; FP8 scores are 0.01, 0.02, 0.01, and 0.01. Select FP8 for layer 2 and NVFP4 for the other three layers.
    :width: 640px
    :align: center
 
@@ -132,6 +136,11 @@ scales when serving with a compatible runtime.
 Combining KV and GEMM AutoQuant
 *******************************
 
+**Score KV candidates on the GEMM configuration you will serve.** If GEMMs
+will be quantized, apply GEMM AutoQuant or fixed PTQ first. KV-only search is
+useful for BF16 deployments, controlled KV studies, or models whose GEMMs are
+already quantized.
+
 A composed recipe runs **GEMM AutoQuant first, then KV AutoQuant**. The shipped
 example uses gradient-based NVFP4/FP8 GEMM search, freezes its selected
 weight/activation quantizers and calibrated tensors, then runs forward-KL KV
@@ -148,7 +157,7 @@ files, plus a fresh export directory:
    --kv_auto_quantize_checkpoint kv_after_gemm_search.pth
 
 Changing the GEMM configuration requires recomputing KV sensitivity with a new
-checkpoint. Fixed GEMM PTQ can also precede KV search; the
+checkpoint. The
 `HF PTQ guide <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/hf_ptq#autoquantize>`_
 describes both compositions. Serving support must cover the combined weight
 and KV formats; an export marked ``kv_cache_deployment_supported: false``