diff --git a/tests/misc/logo.png b/tests/misc/logo.png
new file mode 100644
index 000000000..8805f2b98
Binary files /dev/null and b/tests/misc/logo.png differ
diff --git a/tests/test_image_onnx_embeddings.py b/tests/test_image_onnx_embeddings.py
index 2e5288118..5a9e64229 100644
--- a/tests/test_image_onnx_embeddings.py
+++ b/tests/test_image_onnx_embeddings.py
@@ -5,7 +5,6 @@
 
 import numpy as np
 import pytest
-import requests
 from PIL import Image
 
 from fastembed import ImageEmbedding
@@ -29,8 +28,15 @@
     "nomic-ai/nomic-embed-vision-v1.5": np.array(
         [0.0048, -0.0254, 0.0067, -0.0296, -0.0435, -0.0123, 0.0024, -0.0361, -0.0703, -0.0186]
     ),
-    "nomic-ai/nomic-embed-vision-v1.5-Q": np.array(
-        [-0.0011, -0.0477, 0.0024, -0.049, -0.0458, -0.0314, 0.017, -0.0383, -0.0537, -0.021]
+    # onnxruntime's avx2 int8 kernel saturates on this model's full-range int8 weights, while vnni
+    # and amx compute them exactly, ci runners come with either
+    "nomic-ai/nomic-embed-vision-v1.5-Q": (
+        np.array(  # x86-64 avx2
+            [-0.0006, -0.0446, 0.0069, -0.0553, -0.0459, -0.0261, 0.0058, -0.0463, -0.0606, -0.019]
+        ),
+        np.array(  # x86-64 vnni
+            [0.0020, -0.0376, 0.0094, -0.0402, -0.0457, -0.0216, 0.0100, -0.0378, -0.0650, -0.0205]
+        ),
     ),
     "google/siglip2-base-patch16-224": np.array(
         [-0.02095927, -0.0075177, -0.00144479, -0.0080948, 0.05031789]
@@ -82,21 +88,26 @@ def test_embedding(model_cache, model_name: str) -> None:
         dim = model_desc.dim
 
         with model_cache(model_desc.model) as model:
+            # quantized models scale activations over the whole batch, so the first embedding
+            # depends on every image here, keep them local files
             images = [
                 TEST_MISC_DIR / "image.jpeg",
                 str(TEST_MISC_DIR / "small_image.jpeg"),
                 Image.open((TEST_MISC_DIR / "small_image.jpeg")),
-                Image.open(BytesIO(requests.get("https://qdrant.tech/img/logo.png").content)),
+                Image.open(BytesIO((TEST_MISC_DIR / "logo.png").read_bytes())),
             ]
             embeddings = list(model.embed(images))
             embeddings = np.stack(embeddings, axis=0)
             assert embeddings.shape == (len(images), dim)
 
             canonical_vector = CANONICAL_VECTOR_VALUES[model_desc.model]
-
-            assert np.allclose(
-                embeddings[0, : canonical_vector.shape[0]], canonical_vector, atol=1e-3
-            ), model_desc.model
+            canonical_variants = (
+                canonical_vector if isinstance(canonical_vector, tuple) else (canonical_vector,)
+            )
+            prefix = embeddings[0, : canonical_variants[0].shape[0]]
+            assert any(
+                np.allclose(prefix, expected, atol=1e-3) for expected in canonical_variants
+            ), f"{model_desc.model}: {prefix}"
 
             assert np.allclose(embeddings[1], embeddings[2]), model_desc.model
 
diff --git a/tests/test_text_cross_encoder.py b/tests/test_text_cross_encoder.py
index 85988ca8f..b00cf862b 100644
--- a/tests/test_text_cross_encoder.py
+++ b/tests/test_text_cross_encoder.py
@@ -13,8 +13,9 @@
     "BAAI/bge-reranker-base": np.array([6.15733337, -3.65939403]),
     "BAAI/bge-reranker-v2-m3": np.array([8.78182220, -5.46485329]),
     "BAAI/bge-reranker-v2-m3-int8": (
-        np.array([8.59202957, -5.39362288]),  # ONNX Runtime 1.27.0
-        np.array([8.51154804, -5.56898642]),  # ONNX Runtime 1.30.0
+        np.array([8.59202957, -5.39362288]),  # arm64, ONNX Runtime 1.27.0
+        np.array([8.51154804, -5.56898642]),  # arm64, ONNX Runtime 1.30.0
+        np.array([8.85104847, -5.73784494]),  # x86-64 avx2 and vnni, ONNX Runtime 1.30.0
     ),
     "jinaai/jina-reranker-v1-tiny-en": np.array([2.5911, 0.1122]),
     "jinaai/jina-reranker-v1-turbo-en": np.array([1.8295, -2.8908]),
diff --git a/tests/test_text_onnx_embeddings.py b/tests/test_text_onnx_embeddings.py
index 632bb70cd..1d8e243e5 100644
--- a/tests/test_text_onnx_embeddings.py
+++ b/tests/test_text_onnx_embeddings.py
@@ -56,8 +56,11 @@
     "nomic-ai/nomic-embed-text-v1.5": np.array(
         [-0.15407836, -0.03053198, -3.9138033, 0.1910364, 0.13224715]
     ),
-    "nomic-ai/nomic-embed-text-v1.5-Q": np.array(
-        [0.0802303, 0.3700881, -4.3053818, 0.4431803, -0.271572]
+    # onnxruntime's avx2 int8 kernel saturates on this model's full-range int8 weights, while vnni
+    # and amx compute them exactly, ci runners come with either
+    "nomic-ai/nomic-embed-text-v1.5-Q": (
+        np.array([0.0802303, 0.3700881, -4.3053818, 0.4431803, -0.271572]),  # x86-64 avx2
+        np.array([0.12422204, 0.14779393, -3.8240886, 0.47303754, -0.14925367]),  # x86-64 vnni
     ),
     "thenlper/gte-large": np.array(
         [-0.00986551, -0.00018734, 0.00605892, -0.03289612, -0.0387564],
@@ -188,9 +191,13 @@ def test_embedding(model_cache, model_name: str) -> None:
             assert embeddings.shape == (2, dim)
 
             canonical_vector = CANONICAL_VECTOR_VALUES[model_desc.model]
-            assert np.allclose(
-                embeddings[0, : canonical_vector.shape[0]], canonical_vector, atol=1e-3
-            ), model_desc.model
+            canonical_variants = (
+                canonical_vector if isinstance(canonical_vector, tuple) else (canonical_vector,)
+            )
+            prefix = embeddings[0, : canonical_variants[0].shape[0]]
+            assert any(
+                np.allclose(prefix, expected, atol=1e-3) for expected in canonical_variants
+            ), f"{model_desc.model}: {prefix}"
 
 
 def test_query_embedding(model_cache) -> None: