Quick Answer: RX 7600 XT offers 16GB VRAM and starts around $529.00. It delivers approximately 58 tokens/sec on unsloth/Llama-3.2-1B-Instruct. It typically draws 190W under load.
This GPU offers reliable throughput for local AI workloads. Pair it with the right model quantization to hit your desired tokens/sec, and monitor prices below to catch the best deal.
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| Model | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|
| unsloth/Llama-3.2-1B-Instruct | Q4 | 58.16 tok/sEstimated Auto-generated benchmark | 1GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 57.37 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 57.34 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-3-1b-it | Q4 | 57.09 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B | Q4 | 55.77 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 55.56 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 55.28 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 55.09 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 55.03 tok/sEstimated Auto-generated benchmark | 1GB |
| LiquidAI/LFM2-1.2B | Q4 | 54.04 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B | Q4 | 53.86 tok/sEstimated Auto-generated benchmark | 2GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 53.72 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 52.89 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 52.57 tok/sEstimated Auto-generated benchmark | 2GB |
| google-t5/t5-3b | Q4 | 52.55 tok/sEstimated Auto-generated benchmark | 2GB |
| allenai/OLMo-2-0425-1B | Q4 | 52.48 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 52.45 tok/sEstimated Auto-generated benchmark | 1GB |
| nari-labs/Dia2-2B | Q4 | 52.28 tok/sEstimated Auto-generated benchmark | 2GB |
| bigcode/starcoder2-3b | Q4 | 51.79 tok/sEstimated Auto-generated benchmark | 2GB |
| google/embeddinggemma-300m | Q4 | 51.38 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 50.85 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2-2b-it | Q4 | 50.67 tok/sEstimated Auto-generated benchmark | 1GB |
| inference-net/Schematron-3B | Q4 | 50.48 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B | Q4 | 50.35 tok/sEstimated Auto-generated benchmark | 2GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 49.75 tok/sEstimated Auto-generated benchmark | 1GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 48.83 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2b | Q4 | 48.81 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-research/PowerMoE-3b | Q4 | 48.62 tok/sEstimated Auto-generated benchmark | 2GB |
| tencent/HunyuanOCR | Q4 | 48.49 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen3-0.6B-Base | Q4 | 48.39 tok/sEstimated Auto-generated benchmark | 3GB |
| unsloth/gemma-3-1b-it | Q4 | 48.38 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen3-4B-Thinking-2507 | Q4 | 48.37 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-Guard-3-8B | Q4 | 48.35 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/Phi-3.5-vision-instruct | Q4 | 48.18 tok/sEstimated Auto-generated benchmark | 4GB |
| google-bert/bert-base-uncased | Q4 | 48.12 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/DeepSeek-V3 | Q4 | 48.04 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-7B | Q4 | 48.03 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-8B-Base | Q4 | 47.82 tok/sEstimated Auto-generated benchmark | 4GB |
| HuggingFaceTB/SmolLM2-135M | Q4 | 47.78 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Thinking-2507-FP8 | Q4 | 47.74 tok/sEstimated Auto-generated benchmark | 2GB |
| skt/kogpt2-base-v2 | Q4 | 47.74 tok/sEstimated Auto-generated benchmark | 4GB |
| zai-org/GLM-4.6-FP8 | Q4 | 47.56 tok/sEstimated Auto-generated benchmark | 4GB |
| sshleifer/tiny-gpt2 | Q4 | 47.43 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-Reranker-0.6B | Q4 | 47.40 tok/sEstimated Auto-generated benchmark | 3GB |
| ibm-granite/granite-3.3-8b-instruct | Q4 | 47.38 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Meta-Llama-3-8B-Instruct | Q4 | 47.36 tok/sEstimated Auto-generated benchmark | 4GB |
| rednote-hilab/dots.ocr | Q4 | 47.34 tok/sEstimated Auto-generated benchmark | 4GB |
| lmsys/vicuna-7b-v1.5 | Q4 | 47.33 tok/sEstimated Auto-generated benchmark | 4GB |
| GSAI-ML/LLaDA-8B-Base | Q4 | 47.26 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-R1-Distill-Llama-8B | Q4 | 47.23 tok/sEstimated Auto-generated benchmark | 4GB |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
| Model | Quantization | Verdict | Estimated speed | VRAM needed |
|---|---|---|---|---|
| mistralai/Mistral-Large-3-675B-Instruct-2512 | Q8 | Not supported | 3.83 tok/sEstimated | 755GB (have 16GB) |
| mistralai/Mistral-Large-3-675B-Instruct-2512 | FP16 | Not supported | 2.16 tok/sEstimated | 1509GB (have 16GB) |
| EssentialAI/rnj-1 | Q4 | Fits comfortably | 31.04 tok/sEstimated | 5GB (have 16GB) |
| EssentialAI/rnj-1 | Q8 | Fits comfortably | 23.21 tok/sEstimated | 10GB (have 16GB) |
| EssentialAI/rnj-1 | FP16 | Not supported | 11.41 tok/sEstimated | 19GB (have 16GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | Q4 | Not supported | 14.10 tok/sEstimated | 17GB (have 16GB) |
| openai/gpt-oss-20b | FP16 | Not supported | 9.54 tok/sEstimated | 41GB (have 16GB) |
| google/gemma-3-1b-it | Q4 | Fits comfortably | 57.09 tok/sEstimated | 1GB (have 16GB) |
| google/gemma-3-1b-it | Q8 | Fits comfortably | 35.67 tok/sEstimated | 1GB (have 16GB) |
| google/gemma-3-1b-it | FP16 | Fits comfortably | 20.49 tok/sEstimated | 2GB (have 16GB) |
| Qwen/Qwen3-Embedding-0.6B | Q4 | Fits comfortably | 45.40 tok/sEstimated | 3GB (have 16GB) |
| facebook/opt-125m | Q8 | Fits comfortably | 30.65 tok/sEstimated | 7GB (have 16GB) |
| facebook/opt-125m | FP16 | Fits (tight) | 16.03 tok/sEstimated | 15GB (have 16GB) |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | FP16 | Fits comfortably | 20.37 tok/sEstimated | 6GB (have 16GB) |
| mistralai/Mistral-7B-Instruct-v0.2 | Q4 | Fits comfortably | 43.77 tok/sEstimated | 4GB (have 16GB) |
| mistralai/Mistral-7B-Instruct-v0.2 | Q8 | Fits comfortably | 28.13 tok/sEstimated | 7GB (have 16GB) |
| mistralai/Mistral-7B-Instruct-v0.2 | FP16 | Fits (tight) | 16.48 tok/sEstimated | 15GB (have 16GB) |
| Qwen/Qwen3-8B | Q4 | Fits comfortably | 41.97 tok/sEstimated | 4GB (have 16GB) |
| Qwen/Qwen3-8B | Q8 | Fits comfortably | 29.01 tok/sEstimated | 9GB (have 16GB) |
| Qwen/Qwen3-8B | FP16 | Not supported | 16.92 tok/sEstimated | 17GB (have 16GB) |
| inference-net/Schematron-3B | Q4 | Fits comfortably | 50.48 tok/sEstimated | 2GB (have 16GB) |
| inference-net/Schematron-3B | Q8 | Fits comfortably | 33.89 tok/sEstimated | 3GB (have 16GB) |
| inference-net/Schematron-3B | FP16 | Fits comfortably | 19.83 tok/sEstimated | 6GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-32B | Q4 | Fits (tight) | 14.77 tok/sEstimated | 16GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-32B | Q8 | Not supported | 11.73 tok/sEstimated | 33GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-32B | FP16 | Not supported | 5.65 tok/sEstimated | 66GB (have 16GB) |
| distilbert/distilgpt2 | Q4 | Fits comfortably | 44.33 tok/sEstimated | 4GB (have 16GB) |
| distilbert/distilgpt2 | Q8 | Fits comfortably | 32.73 tok/sEstimated | 7GB (have 16GB) |
| distilbert/distilgpt2 | FP16 | Fits (tight) | 15.50 tok/sEstimated | 15GB (have 16GB) |
| RedHatAI/Llama-3.3-70B-Instruct-FP8-dynamic | Q4 | Not supported | 15.29 tok/sEstimated | 34GB (have 16GB) |
| RedHatAI/Llama-3.3-70B-Instruct-FP8-dynamic | Q8 | Not supported | 10.93 tok/sEstimated | 68GB (have 16GB) |
| RedHatAI/Llama-3.3-70B-Instruct-FP8-dynamic | FP16 | Not supported | 5.55 tok/sEstimated | 137GB (have 16GB) |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | Fits comfortably | 52.57 tok/sEstimated | 2GB (have 16GB) |
| meta-llama/Llama-3.2-3B-Instruct | Q8 | Fits comfortably | 35.65 tok/sEstimated | 3GB (have 16GB) |
| meta-llama/Llama-3.2-3B-Instruct | FP16 | Fits comfortably | 21.82 tok/sEstimated | 6GB (have 16GB) |
| vikhyatk/moondream2 | Q4 | Fits comfortably | 43.18 tok/sEstimated | 4GB (have 16GB) |
| vikhyatk/moondream2 | Q8 | Fits comfortably | 32.37 tok/sEstimated | 7GB (have 16GB) |
| vikhyatk/moondream2 | FP16 | Fits (tight) | 16.30 tok/sEstimated | 15GB (have 16GB) |
| petals-team/StableBeluga2 | Q4 | Fits comfortably | 46.29 tok/sEstimated | 4GB (have 16GB) |
| petals-team/StableBeluga2 | Q8 | Fits comfortably | 29.38 tok/sEstimated | 7GB (have 16GB) |
| petals-team/StableBeluga2 | FP16 | Fits (tight) | 17.73 tok/sEstimated | 15GB (have 16GB) |
| meta-llama/Llama-3.2-1B | Q4 | Fits comfortably | 55.77 tok/sEstimated | 1GB (have 16GB) |
| meta-llama/Llama-3.2-1B | Q8 | Fits comfortably | 35.56 tok/sEstimated | 1GB (have 16GB) |
| meta-llama/Llama-3.2-1B | FP16 | Fits comfortably | 19.17 tok/sEstimated | 2GB (have 16GB) |
| meta-llama/Meta-Llama-3-8B | Q4 | Fits comfortably | 42.06 tok/sEstimated | 4GB (have 16GB) |
| meta-llama/Meta-Llama-3-8B | Q8 | Fits comfortably | 31.23 tok/sEstimated | 9GB (have 16GB) |
| meta-llama/Meta-Llama-3-8B | FP16 | Not supported | 16.57 tok/sEstimated | 17GB (have 16GB) |
| Qwen/Qwen2.5-7B | Q4 | Fits comfortably | 48.03 tok/sEstimated | 4GB (have 16GB) |
| Qwen/Qwen2.5-7B | Q8 | Fits comfortably | 29.93 tok/sEstimated | 7GB (have 16GB) |
| mistralai/Mistral-Large-3-675B-Instruct-2512 | Q4 | Not supported | 5.57 tok/sEstimated | 378GB (have 16GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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