diff --git a/examples/llm_qat/README.md b/examples/llm_qat/README.md
index cd88ae30498..a73447f9f03 100644
--- a/examples/llm_qat/README.md
+++ b/examples/llm_qat/README.md
@@ -309,7 +309,7 @@ The cached dataset is stored under `.dataset_cache/tokenized/` by default (confi
 
 ## Results
 
-\[Coming Soon\]
+See [Qwen3-8B evaluation results](evaluation/README.md) for the teacher, PTQ, and PTQ → QAD comparison.
 
 ## Native Fake-Quantized Evaluation
 
diff --git a/examples/llm_qat/evaluation/README.md b/examples/llm_qat/evaluation/README.md
new file mode 100644
index 00000000000..82b67d5616f
--- /dev/null
+++ b/examples/llm_qat/evaluation/README.md
@@ -0,0 +1,116 @@
+# Evaluation
+
+## Results
+
+Qwen3-8B was evaluated using NEL with vLLM on one H100 per evaluation. QAD starts from the PTQ checkpoint and uses the BF16 model as teacher.
+
+| Checkpoint | Deployed precision | MMLU accuracy ↑ (14,042 questions) | WikiText-2 perplexity ↓ (241,335 words) |
+|---|---|---:|---:|
+| Teacher (BF16) | BF16 | 74.94% | 13.485 |
+| PTQ (NVFP4) | NVFP4 weights / BF16 activations | 73.59% | 13.883 |
+| PTQ → QAD (NVFP4) | NVFP4 weights / BF16 activations | 73.76% | 13.684 |
+
+QAD improves both metrics over PTQ but remains below the BF16 teacher. The quantized checkpoints use Marlin's W4A16 fallback on H100; these results do not measure native NVFP4 W4A4 execution on Blackwell. KV cache is BF16 for all checkpoints.
+
+## Run evaluations
+
+From `examples/llm_qat/evaluation/`, configure access to your Slurm cluster:
+
+```bash
+pip install "nemo-evaluator-launcher[all]==0.2.6"
+
+export NEL_SLURM_HOST="<cluster-login-host>"
+export SLURM_USER="<cluster-username>"
+export NEL_SLURM_ACCOUNT="<slurm-account>"
+export NEL_SLURM_PARTITION="<slurm-partition>"
+export NEL_OUTPUT_DIR="/shared/path/to/evaluation-results"
+export MODEL_CHECKPOINT="/shared/path/to/checkpoint"
+
+# See execution.mounts.evaluation in nel_configs/h100_slurm_execution.yaml
+# for why the checkpoint mount is passed via -o.
+# Run MMLU and WikiText.
+nemo-evaluator-launcher run --config nel_config.yaml \
+    -o deployment.served_model_name=qwen3-8b-ptq \
+    -o "++execution.mounts.evaluation={$MODEL_CHECKPOINT: /checkpoint}"
+```
+
+Use the BF16 teacher or an exported Hugging Face PTQ/QAD checkpoint. The checkpoint directory must be accessible on the cluster and contain the model files, without symlinks to unmounted locations.
+
+For the teacher, download a self-contained directory on the cluster; no additional `/blobs` mounts are needed:
+
+```bash
+hf download Qwen/Qwen3-8B --local-dir /shared/path/to/qwen3-8b-teacher
+export MODEL_CHECKPOINT="/shared/path/to/qwen3-8b-teacher"
+```
+
+Add `--dry-run` to preview, or `-t lm-evaluation-harness.mmlu` / `-t lm-evaluation-harness.wikitext` to select one benchmark. NEL 0.2.6 launches one Slurm job per benchmark; results are saved under `NEL_OUTPUT_DIR`.
+
+<details>
+<summary>MMLU accuracy by subject</summary>
+
+| Subject | Questions | Teacher (BF16) | PTQ (NVFP4) | PTQ → QAD (NVFP4) |
+|---|---:|---:|---:|---:|
+| Abstract algebra | 100 | 55.00% | 56.00% | 56.00% |
+| Anatomy | 135 | 70.37% | 68.89% | 68.89% |
+| Astronomy | 152 | 90.13% | 88.82% | 90.13% |
+| Business ethics | 100 | 78.00% | 77.00% | 79.00% |
+| Clinical knowledge | 265 | 78.49% | 80.38% | 79.62% |
+| College biology | 144 | 87.50% | 86.81% | 87.50% |
+| College chemistry | 100 | 56.00% | 59.00% | 56.00% |
+| College computer science | 100 | 73.00% | 72.00% | 68.00% |
+| College mathematics | 100 | 58.00% | 59.00% | 58.00% |
+| College medicine | 173 | 79.19% | 78.03% | 78.03% |
+| College physics | 102 | 64.71% | 59.80% | 63.73% |
+| Computer security | 100 | 81.00% | 81.00% | 80.00% |
+| Conceptual physics | 235 | 84.68% | 85.11% | 84.68% |
+| Econometrics | 114 | 65.79% | 64.91% | 68.42% |
+| Electrical engineering | 145 | 80.69% | 80.69% | 82.76% |
+| Elementary mathematics | 378 | 78.84% | 75.40% | 76.19% |
+| Formal logic | 126 | 67.46% | 66.67% | 60.32% |
+| Global facts | 100 | 52.00% | 44.00% | 43.00% |
+| High school biology | 310 | 90.97% | 91.29% | 91.61% |
+| High school chemistry | 203 | 75.86% | 72.91% | 71.92% |
+| High school computer science | 100 | 86.00% | 86.00% | 86.00% |
+| High school European history | 165 | 85.45% | 85.45% | 87.27% |
+| High school geography | 198 | 86.87% | 86.36% | 86.87% |
+| High school government and politics | 193 | 93.26% | 92.23% | 93.26% |
+| High school macroeconomics | 390 | 81.03% | 79.49% | 80.00% |
+| High school mathematics | 270 | 53.70% | 50.00% | 51.85% |
+| High school microeconomics | 238 | 93.70% | 91.60% | 92.02% |
+| High school physics | 151 | 72.85% | 69.54% | 70.20% |
+| High school psychology | 545 | 91.19% | 90.46% | 90.83% |
+| High school statistics | 216 | 78.24% | 75.93% | 75.46% |
+| High school US history | 204 | 86.27% | 85.29% | 83.82% |
+| High school world history | 237 | 86.50% | 85.65% | 85.65% |
+| Human aging | 223 | 74.44% | 75.34% | 73.99% |
+| Human sexuality | 131 | 83.21% | 88.55% | 84.73% |
+| International law | 121 | 73.55% | 73.55% | 73.55% |
+| Jurisprudence | 108 | 81.48% | 83.33% | 79.63% |
+| Logical fallacies | 163 | 82.21% | 80.37% | 82.21% |
+| Machine learning | 112 | 64.29% | 64.29% | 65.18% |
+| Management | 103 | 87.38% | 89.32% | 87.38% |
+| Marketing | 234 | 93.59% | 92.74% | 91.88% |
+| Medical genetics | 100 | 83.00% | 82.00% | 82.00% |
+| Miscellaneous | 783 | 85.82% | 83.78% | 83.40% |
+| Moral disputes | 346 | 76.01% | 72.25% | 73.99% |
+| Moral scenarios | 895 | 55.42% | 53.85% | 53.85% |
+| Nutrition | 306 | 81.05% | 81.70% | 80.07% |
+| Philosophy | 311 | 77.81% | 74.28% | 76.85% |
+| Prehistory | 324 | 83.95% | 82.72% | 83.02% |
+| Professional accounting | 282 | 59.22% | 58.51% | 57.09% |
+| Professional law | 1,534 | 51.17% | 49.02% | 50.26% |
+| Professional medicine | 272 | 81.99% | 80.51% | 80.88% |
+| Professional psychology | 612 | 78.59% | 76.31% | 76.14% |
+| Public relations | 110 | 70.00% | 68.18% | 70.00% |
+| Security studies | 245 | 79.59% | 80.00% | 80.41% |
+| Sociology | 201 | 86.57% | 84.58% | 84.08% |
+| US foreign policy | 100 | 84.00% | 83.00% | 85.00% |
+| Virology | 166 | 56.02% | 52.41% | 53.61% |
+| World religions | 171 | 87.13% | 85.38% | 84.21% |
+
+</details>
+
+## File structure
+
+- `nel_config.yaml`: entry point containing the MMLU and WikiText recipes.
+- `nel_configs/`: shared Slurm execution and vLLM deployment settings.
diff --git a/examples/llm_qat/evaluation/nel_config.yaml b/examples/llm_qat/evaluation/nel_config.yaml
new file mode 100644
index 00000000000..41f91a559fa
--- /dev/null
+++ b/examples/llm_qat/evaluation/nel_config.yaml
@@ -0,0 +1,61 @@
+# MMLU and WikiText recipes for nemo-evaluator-launcher==0.2.6.
+defaults:
+  - execution: slurm/default
+  - nel_configs/h100_slurm_execution@_global_
+  - deployment: vllm
+  - nel_configs/qwen3_8b_vllm_deployment@_global_
+  - _self_
+deployment:
+  # Override when launching to identify the checkpoint, e.g., -o deployment.served_model_name=qwen3-8b-ptq
+  served_model_name: qwen3-8b
+  port: 18100
+evaluation:
+  nemo_evaluator_config:
+    target:
+      api_endpoint:
+        api_key_name:
+        adapter_config:
+          use_reasoning: false
+          use_request_logging: true
+          use_response_logging: true
+          max_logged_requests: 2
+          max_logged_responses: 2
+  tasks:
+    - name: lm-evaluation-harness.mmlu
+      endpoint_type: completions
+      container: nvcr.io/nvidia/eval-factory/lm-evaluation-harness:26.03
+      nemo_evaluator_config:
+        config:
+          params:
+            parallelism: 1
+            request_timeout: 300
+            max_retries: 2
+            temperature:
+            top_p:
+            max_new_tokens:
+            task: mmlu
+            extra:
+              num_fewshot: 5
+              tokenizer: ${deployment.tokenizer_path}
+              tokenizer_backend: huggingface
+              tokenized_requests: true
+              args: --seed 42 --model_args "base_url=http://127.0.0.1:${deployment.port}/v1/completions,model=${deployment.served_model_name},tokenized_requests=True,tokenizer=${deployment.tokenizer_path},tokenizer_backend=huggingface,num_concurrent=${..parallelism},timeout=${..request_timeout},max_retries=${..max_retries},stream=False,max_length=32768"
+    - name: lm-evaluation-harness.wikitext
+      endpoint_type: completions
+      container: nvcr.io/nvidia/eval-factory/lm-evaluation-harness:26.03
+      nemo_evaluator_config:
+        config:
+          params:
+            parallelism: 2
+            request_timeout: 300
+            max_retries: 2
+            temperature:
+            top_p:
+            max_new_tokens:
+            task: wikitext
+            extra:
+              num_fewshot: 0
+              tokenizer: ${deployment.tokenizer_path}
+              tokenizer_backend: huggingface
+              tokenized_requests: true
+              args: --seed 42 --model_args "base_url=http://127.0.0.1:${deployment.port}/v1/completions,model=${deployment.served_model_name},tokenized_requests=True,tokenizer=${deployment.tokenizer_path},tokenizer_backend=huggingface,num_concurrent=${..parallelism},timeout=${..request_timeout},max_retries=${..max_retries},stream=False,max_length=2048"
diff --git a/examples/llm_qat/evaluation/nel_configs/h100_slurm_execution.yaml b/examples/llm_qat/evaluation/nel_configs/h100_slurm_execution.yaml
new file mode 100644
index 00000000000..f4c5296d222
--- /dev/null
+++ b/examples/llm_qat/evaluation/nel_configs/h100_slurm_execution.yaml
@@ -0,0 +1,23 @@
+# Shared single-GPU Slurm execution settings for H100 evaluation.
+# NEL mounts the server checkpoint automatically; pass the evaluator mount with -o.
+execution:
+  hostname: ${oc.env:NEL_SLURM_HOST}
+  username: ${oc.env:SLURM_USER}
+  account: ${oc.env:NEL_SLURM_ACCOUNT}
+  partition: ${oc.env:NEL_SLURM_PARTITION}
+  output_dir: ${oc.env:NEL_OUTPUT_DIR}
+  num_nodes: 1
+  ntasks_per_node: 1
+  gres: gpu:1
+  walltime: 04:00:00
+  sbatch_extra_flags:
+    exclusive: false
+    mem: 64G
+  mounts:
+    mount_home: false
+    deployment: {}
+    # NEL 0.2.6 validates this field as a dictionary before resolving oc.create,
+    # so the expression deriving the mount from MODEL_CHECKPOINT was rejected.
+    # The README therefore supplies a concrete mapping via the CLI instead:
+    # -o "++execution.mounts.evaluation={$MODEL_CHECKPOINT: /checkpoint}"
+    evaluation: {}
diff --git a/examples/llm_qat/evaluation/nel_configs/qwen3_8b_vllm_deployment.yaml b/examples/llm_qat/evaluation/nel_configs/qwen3_8b_vllm_deployment.yaml
new file mode 100644
index 00000000000..638ec64d6f9
--- /dev/null
+++ b/examples/llm_qat/evaluation/nel_configs/qwen3_8b_vllm_deployment.yaml
@@ -0,0 +1,20 @@
+# Shared vLLM deployment for Qwen3-8B teacher, PTQ, and PTQ → QAD.
+deployment:
+  image: vllm/vllm-openai:v0.28.0-cu129
+  checkpoint_path: ${oc.env:MODEL_CHECKPOINT}
+  tensor_parallel_size: 1
+  data_parallel_size: 1
+  command: >-
+    vllm serve /checkpoint
+    --served-model-name ${deployment.served_model_name}
+    --host 0.0.0.0
+    --port ${deployment.port}
+    --dtype bfloat16
+    --tensor-parallel-size ${deployment.tensor_parallel_size}
+    --data-parallel-size ${deployment.data_parallel_size}
+    --max-model-len 32768
+    --max-num-seqs 2
+    --gpu-memory-utilization 0.85
+    --seed 42
+    --kv-cache-dtype auto
+  tokenizer_path: /checkpoint