Quick Answer: AMD Instinct MI210 offers 64GB VRAM and starts around current market pricing. It delivers approximately 313 tokens/sec on meta-llama/Llama-Guard-3-1B. It typically draws 300W 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 |
|---|---|---|---|
| meta-llama/Llama-Guard-3-1B | Q4 | 313.28 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/gemma-3-1b-it | Q4 | 311.17 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B | Q4 | 310.04 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 306.67 tok/sEstimated Auto-generated benchmark | 1GB |
| allenai/OLMo-2-0425-1B | Q4 | 306.48 tok/sEstimated Auto-generated benchmark | 1GB |
| google-t5/t5-3b | Q4 | 305.19 tok/sEstimated Auto-generated benchmark | 2GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 303.85 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-3-1b-it | Q4 | 303.02 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 299.41 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B | Q4 | 298.98 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 298.34 tok/sEstimated Auto-generated benchmark | 2GB |
| LiquidAI/LFM2-1.2B | Q4 | 294.14 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 291.87 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 291.52 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 284.59 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 281.97 tok/sEstimated Auto-generated benchmark | 2GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 280.16 tok/sEstimated Auto-generated benchmark | 2GB |
| google/embeddinggemma-300m | Q4 | 278.18 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B | Q4 | 276.71 tok/sEstimated Auto-generated benchmark | 2GB |
| nari-labs/Dia2-2B | Q4 | 275.82 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2-2b-it | Q4 | 275.13 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2b | Q4 | 272.63 tok/sEstimated Auto-generated benchmark | 1GB |
| bigcode/starcoder2-3b | Q4 | 272.50 tok/sEstimated Auto-generated benchmark | 2GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 268.81 tok/sEstimated Auto-generated benchmark | 1GB |
| tencent/HunyuanOCR | Q4 | 266.68 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-research/PowerMoE-3b | Q4 | 266.19 tok/sEstimated Auto-generated benchmark | 2GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 263.00 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 261.99 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-1.5B-Instruct | Q4 | 260.84 tok/sEstimated Auto-generated benchmark | 3GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 260.81 tok/sEstimated Auto-generated benchmark | 2GB |
| inference-net/Schematron-3B | Q4 | 260.77 tok/sEstimated Auto-generated benchmark | 2GB |
| microsoft/phi-2 | Q4 | 260.16 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-0.5B-Instruct | Q4 | 260.08 tok/sEstimated Auto-generated benchmark | 3GB |
| meta-llama/Llama-Guard-3-8B | Q4 | 258.95 tok/sEstimated Auto-generated benchmark | 4GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 258.79 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Meta-Llama-3-8B | Q4 | 258.52 tok/sEstimated Auto-generated benchmark | 4GB |
| ibm-granite/granite-3.3-8b-instruct | Q4 | 258.48 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-3.1-8B | Q4 | 257.69 tok/sEstimated Auto-generated benchmark | 4GB |
| parler-tts/parler-tts-large-v1 | Q4 | 257.67 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-8B-Base | Q4 | 257.20 tok/sEstimated Auto-generated benchmark | 4GB |
| google-bert/bert-base-uncased | Q4 | 256.96 tok/sEstimated Auto-generated benchmark | 1GB |
| microsoft/VibeVoice-1.5B | Q4 | 256.93 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen3-1.7B-Base | Q4 | 256.63 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-Math-1.5B | Q4 | 256.46 tok/sEstimated Auto-generated benchmark | 3GB |
| microsoft/Phi-3-mini-128k-instruct | Q4 | 256.17 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B | Q4 | 255.34 tok/sEstimated Auto-generated benchmark | 3GB |
| microsoft/DialoGPT-small | Q4 | 254.55 tok/sEstimated Auto-generated benchmark | 4GB |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-4bit | Q4 | 254.51 tok/sEstimated Auto-generated benchmark | 4GB |
| mistralai/Mistral-7B-v0.1 | Q4 | 254.10 tok/sEstimated Auto-generated benchmark | 4GB |
| ibm-granite/granite-docling-258M | Q4 | 254.07 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 |
|---|---|---|---|---|
| Qwen/Qwen3-0.6B | FP16 | Fits comfortably | 93.54 tok/sEstimated | 13GB (have 64GB) |
| Gensyn/Qwen2.5-0.5B-Instruct | Q4 | Fits comfortably | 218.84 tok/sEstimated | 3GB (have 64GB) |
| google/gemma-3-1b-it | Q4 | Fits comfortably | 303.02 tok/sEstimated | 1GB (have 64GB) |
| google/gemma-3-1b-it | Q8 | Fits comfortably | 206.78 tok/sEstimated | 1GB (have 64GB) |
| google/gemma-3-1b-it | FP16 | Fits comfortably | 117.47 tok/sEstimated | 2GB (have 64GB) |
| Qwen/Qwen3-4B-Instruct-2507 | FP16 | Fits comfortably | 86.15 tok/sEstimated | 9GB (have 64GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | Fits comfortably | 261.99 tok/sEstimated | 1GB (have 64GB) |
| Qwen/Qwen3-8B | Q8 | Fits comfortably | 168.98 tok/sEstimated | 9GB (have 64GB) |
| Qwen/Qwen3-8B | FP16 | Fits comfortably | 88.42 tok/sEstimated | 17GB (have 64GB) |
| meta-llama/Meta-Llama-3-8B | FP16 | Fits comfortably | 98.52 tok/sEstimated | 17GB (have 64GB) |
| Qwen/Qwen2.5-7B | Q4 | Fits comfortably | 235.85 tok/sEstimated | 4GB (have 64GB) |
| Qwen/Qwen2.5-7B | Q8 | Fits comfortably | 154.48 tok/sEstimated | 7GB (have 64GB) |
| Qwen/Qwen2.5-7B | FP16 | Fits comfortably | 91.71 tok/sEstimated | 15GB (have 64GB) |
| Qwen/Qwen3-0.6B-Base | Q8 | Fits comfortably | 173.51 tok/sEstimated | 6GB (have 64GB) |
| Qwen/Qwen3-0.6B-Base | FP16 | Fits comfortably | 92.22 tok/sEstimated | 13GB (have 64GB) |
| Qwen/Qwen3-30B-A3B | Q8 | Fits comfortably | 82.87 tok/sEstimated | 31GB (have 64GB) |
| Qwen/Qwen3-30B-A3B | FP16 | Fits comfortably | 47.62 tok/sEstimated | 61GB (have 64GB) |
| microsoft/Phi-3.5-vision-instruct | Q8 | Fits comfortably | 175.09 tok/sEstimated | 7GB (have 64GB) |
| Qwen/Qwen2-7B-Instruct | Q8 | Fits comfortably | 172.25 tok/sEstimated | 7GB (have 64GB) |
| Qwen/Qwen2-7B-Instruct | FP16 | Fits comfortably | 88.63 tok/sEstimated | 15GB (have 64GB) |
| Qwen/Qwen3-4B-Base | Q4 | Fits comfortably | 225.62 tok/sEstimated | 2GB (have 64GB) |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-4bit | Q4 | Fits comfortably | 241.68 tok/sEstimated | 2GB (have 64GB) |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-4bit | Q8 | Fits comfortably | 168.39 tok/sEstimated | 4GB (have 64GB) |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-4bit | FP16 | Fits comfortably | 85.74 tok/sEstimated | 9GB (have 64GB) |
| unsloth/Llama-3.2-3B-Instruct | Q4 | Fits comfortably | 298.34 tok/sEstimated | 2GB (have 64GB) |
| unsloth/Llama-3.2-3B-Instruct | Q8 | Fits comfortably | 186.17 tok/sEstimated | 3GB (have 64GB) |
| OpenPipe/Qwen3-14B-Instruct | FP16 | Fits comfortably | 73.55 tok/sEstimated | 29GB (have 64GB) |
| openai-community/gpt2-xl | Q4 | Fits comfortably | 218.65 tok/sEstimated | 4GB (have 64GB) |
| openai-community/gpt2-xl | Q8 | Fits comfortably | 167.09 tok/sEstimated | 7GB (have 64GB) |
| openai-community/gpt2-xl | FP16 | Fits comfortably | 97.89 tok/sEstimated | 15GB (have 64GB) |
| microsoft/Phi-3-mini-128k-instruct | Q4 | Fits comfortably | 256.17 tok/sEstimated | 4GB (have 64GB) |
| microsoft/Phi-3-mini-128k-instruct | Q8 | Fits comfortably | 150.93 tok/sEstimated | 7GB (have 64GB) |
| GSAI-ML/LLaDA-8B-Instruct | Q4 | Fits comfortably | 227.64 tok/sEstimated | 4GB (have 64GB) |
| GSAI-ML/LLaDA-8B-Instruct | Q8 | Fits comfortably | 163.69 tok/sEstimated | 9GB (have 64GB) |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-4bit | Q8 | Fits comfortably | 166.47 tok/sEstimated | 9GB (have 64GB) |
| skt/kogpt2-base-v2 | Q8 | Fits comfortably | 180.06 tok/sEstimated | 7GB (have 64GB) |
| skt/kogpt2-base-v2 | FP16 | Fits comfortably | 91.11 tok/sEstimated | 15GB (have 64GB) |
| ibm-granite/granite-docling-258M | Q4 | Fits comfortably | 254.07 tok/sEstimated | 4GB (have 64GB) |
| Qwen/Qwen3-Next-80B-A3B-Thinking | Q4 | Fits comfortably | 50.19 tok/sEstimated | 39GB (have 64GB) |
| Qwen/Qwen3-Next-80B-A3B-Thinking | Q8 | Not supported | 34.41 tok/sEstimated | 78GB (have 64GB) |
| Qwen/Qwen3-Next-80B-A3B-Thinking | FP16 | Not supported | 18.47 tok/sEstimated | 156GB (have 64GB) |
| meta-llama/Llama-2-13b-chat-hf | Q4 | Fits comfortably | 173.60 tok/sEstimated | 7GB (have 64GB) |
| NousResearch/Meta-Llama-3.1-8B-Instruct | FP16 | Fits comfortably | 98.95 tok/sEstimated | 17GB (have 64GB) |
| apple/OpenELM-1_1B-Instruct | Q4 | Fits comfortably | 263.00 tok/sEstimated | 1GB (have 64GB) |
| lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-5bit | Q4 | Fits comfortably | 125.81 tok/sEstimated | 15GB (have 64GB) |
| lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-5bit | Q8 | Fits comfortably | 94.83 tok/sEstimated | 31GB (have 64GB) |
| lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-5bit | FP16 | Fits comfortably | 49.67 tok/sEstimated | 61GB (have 64GB) |
| Alibaba-NLP/gte-Qwen2-1.5B-instruct | Q4 | Fits comfortably | 230.57 tok/sEstimated | 3GB (have 64GB) |
| Qwen/QwQ-32B-Preview | Q8 | Fits comfortably | 54.52 tok/sEstimated | 34GB (have 64GB) |
| Qwen/Qwen3-0.6B | Q8 | Fits comfortably | 175.34 tok/sEstimated | 6GB (have 64GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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