Quick Answer: Intel Arc A770 16GB offers 16GB VRAM and starts around $467.08. It delivers approximately 113 tokens/sec on deepseek-ai/DeepSeek-OCR. It typically draws 225W 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 |
|---|---|---|---|
| deepseek-ai/DeepSeek-OCR | Q4 | 112.76 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 110.87 tok/sEstimated Auto-generated benchmark | 2GB |
| google-bert/bert-base-uncased | Q4 | 110.45 tok/sEstimated Auto-generated benchmark | 1GB |
| nari-labs/Dia2-2B | Q4 | 109.18 tok/sEstimated Auto-generated benchmark | 2GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 108.47 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 108.22 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 108.15 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-research/PowerMoE-3b | Q4 | 107.34 tok/sEstimated Auto-generated benchmark | 2GB |
| LiquidAI/LFM2-1.2B | Q4 | 107.07 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 105.32 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B | Q4 | 103.85 tok/sEstimated Auto-generated benchmark | 2GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 103.23 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 102.72 tok/sEstimated Auto-generated benchmark | 1GB |
| allenai/OLMo-2-0425-1B | Q4 | 102.34 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-3-1b-it | Q4 | 101.01 tok/sEstimated Auto-generated benchmark | 1GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 100.43 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 98.87 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 97.39 tok/sEstimated Auto-generated benchmark | 1GB |
| tencent/HunyuanOCR | Q4 | 97.27 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/gemma-3-1b-it | Q4 | 96.88 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 96.22 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2b | Q4 | 96.17 tok/sEstimated Auto-generated benchmark | 1GB |
| bigcode/starcoder2-3b | Q4 | 95.54 tok/sEstimated Auto-generated benchmark | 2GB |
| inference-net/Schematron-3B | Q4 | 95.52 tok/sEstimated Auto-generated benchmark | 2GB |
| google/embeddinggemma-300m | Q4 | 95.29 tok/sEstimated Auto-generated benchmark | 1GB |
| HuggingFaceTB/SmolLM2-135M | Q4 | 94.86 tok/sEstimated Auto-generated benchmark | 4GB |
| openai-community/gpt2 | Q4 | 94.81 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-1.7B | Q4 | 94.73 tok/sEstimated Auto-generated benchmark | 4GB |
| google/gemma-2-2b-it | Q4 | 94.47 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-Math-1.5B | Q4 | 94.45 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen2-7B-Instruct | Q4 | 94.40 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-R1-Distill-Llama-8B | Q4 | 94.25 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/VibeVoice-1.5B | Q4 | 94.24 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen2.5-Coder-1.5B | Q4 | 94.21 tok/sEstimated Auto-generated benchmark | 3GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 94.16 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-1.5B | Q4 | 94.14 tok/sEstimated Auto-generated benchmark | 3GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 94.04 tok/sEstimated Auto-generated benchmark | 1GB |
| microsoft/DialoGPT-medium | Q4 | 93.68 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-2-7b-chat-hf | Q4 | 93.64 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-1.7B-Base | Q4 | 93.55 tok/sEstimated Auto-generated benchmark | 4GB |
| lmsys/vicuna-7b-v1.5 | Q4 | 93.54 tok/sEstimated Auto-generated benchmark | 4GB |
| ibm-granite/granite-3.3-8b-instruct | Q4 | 93.52 tok/sEstimated Auto-generated benchmark | 4GB |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-8bit | Q4 | 93.46 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B | Q4 | 93.39 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B | Q4 | 93.32 tok/sEstimated Auto-generated benchmark | 1GB |
| google-t5/t5-3b | Q4 | 93.19 tok/sEstimated Auto-generated benchmark | 2GB |
| MiniMaxAI/MiniMax-M2 | Q4 | 93.03 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/phi-4 | Q4 | 93.03 tok/sEstimated Auto-generated benchmark | 4GB |
| HuggingFaceTB/SmolLM-135M | Q4 | 92.97 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-3.1-8B | Q4 | 92.83 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 |
|---|---|---|---|---|
| XiaomiMiMo/MiMo-V2-Flash | Q4 | Not supported | 10.19 tok/sEstimated | 174GB (have 16GB) |
| XiaomiMiMo/MiMo-V2-Flash | Q8 | Not supported | 7.39 tok/sEstimated | 347GB (have 16GB) |
| XiaomiMiMo/MiMo-V2-Flash | FP16 | Not supported | 3.57 tok/sEstimated | 693GB (have 16GB) |
| zai-org/GLM-4.7 | Q4 | Not supported | 10.01 tok/sEstimated | 201GB (have 16GB) |
| zai-org/GLM-4.7 | Q8 | Not supported | 7.96 tok/sEstimated | 401GB (have 16GB) |
| zai-org/GLM-4.7 | FP16 | Not supported | 4.29 tok/sEstimated | 801GB (have 16GB) |
| Qwen/Qwen3-0.6B | Q4 | Fits comfortably | 81.68 tok/sEstimated | 3GB (have 16GB) |
| openai-community/gpt2 | FP16 | Fits (tight) | 35.32 tok/sEstimated | 15GB (have 16GB) |
| Qwen/Qwen2.5-7B-Instruct | Q4 | Fits comfortably | 83.32 tok/sEstimated | 4GB (have 16GB) |
| Qwen/Qwen2.5-7B-Instruct | Q8 | Fits comfortably | 61.90 tok/sEstimated | 7GB (have 16GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | Q8 | Not supported | 19.65 tok/sEstimated | 35GB (have 16GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | FP16 | Not supported | 11.21 tok/sEstimated | 70GB (have 16GB) |
| Qwen/Qwen2.5-1.5B-Instruct | Q4 | Fits comfortably | 79.95 tok/sEstimated | 3GB (have 16GB) |
| facebook/opt-125m | Q8 | Fits comfortably | 65.42 tok/sEstimated | 7GB (have 16GB) |
| facebook/opt-125m | FP16 | Fits (tight) | 30.42 tok/sEstimated | 15GB (have 16GB) |
| facebook/opt-125m | Q4 | Fits comfortably | 87.95 tok/sEstimated | 4GB (have 16GB) |
| Qwen/Qwen3-4B-Instruct-2507 | Q8 | Fits comfortably | 56.15 tok/sEstimated | 4GB (have 16GB) |
| Qwen/Qwen3-4B-Instruct-2507 | FP16 | Fits comfortably | 35.54 tok/sEstimated | 9GB (have 16GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | Fits comfortably | 97.39 tok/sEstimated | 1GB (have 16GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q8 | Fits comfortably | 77.20 tok/sEstimated | 1GB (have 16GB) |
| openai/gpt-oss-120b | Q4 | Not supported | 18.62 tok/sEstimated | 59GB (have 16GB) |
| openai/gpt-oss-120b | Q8 | Not supported | 10.91 tok/sEstimated | 117GB (have 16GB) |
| openai/gpt-oss-120b | FP16 | Not supported | 5.98 tok/sEstimated | 235GB (have 16GB) |
| Qwen/Qwen2.5-3B-Instruct | Q4 | Fits comfortably | 98.87 tok/sEstimated | 2GB (have 16GB) |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q8 | Fits comfortably | 65.71 tok/sEstimated | 3GB (have 16GB) |
| mistralai/Mistral-7B-Instruct-v0.2 | Q8 | Fits comfortably | 59.95 tok/sEstimated | 7GB (have 16GB) |
| mistralai/Mistral-7B-Instruct-v0.2 | FP16 | Fits (tight) | 35.43 tok/sEstimated | 15GB (have 16GB) |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | FP16 | Fits comfortably | 40.83 tok/sEstimated | 6GB (have 16GB) |
| mistralai/Mistral-7B-Instruct-v0.2 | Q4 | Fits comfortably | 80.58 tok/sEstimated | 4GB (have 16GB) |
| RedHatAI/Llama-3.3-70B-Instruct-FP8-dynamic | Q4 | Not supported | 28.56 tok/sEstimated | 34GB (have 16GB) |
| RedHatAI/Llama-3.3-70B-Instruct-FP8-dynamic | Q8 | Not supported | 23.08 tok/sEstimated | 68GB (have 16GB) |
| RedHatAI/Llama-3.3-70B-Instruct-FP8-dynamic | FP16 | Not supported | 12.27 tok/sEstimated | 137GB (have 16GB) |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | Fits comfortably | 110.87 tok/sEstimated | 2GB (have 16GB) |
| microsoft/Phi-3-mini-4k-instruct | Q4 | Fits comfortably | 82.02 tok/sEstimated | 4GB (have 16GB) |
| microsoft/Phi-3-mini-4k-instruct | FP16 | Fits (tight) | 35.28 tok/sEstimated | 15GB (have 16GB) |
| openai-community/gpt2-large | Q4 | Fits comfortably | 87.11 tok/sEstimated | 4GB (have 16GB) |
| microsoft/Phi-3-mini-4k-instruct | Q8 | Fits comfortably | 58.45 tok/sEstimated | 7GB (have 16GB) |
| Qwen/Qwen3-1.7B | Q8 | Fits comfortably | 58.21 tok/sEstimated | 7GB (have 16GB) |
| Qwen/Qwen3-1.7B | FP16 | Fits (tight) | 31.34 tok/sEstimated | 15GB (have 16GB) |
| Qwen/Qwen3-30B-A3B-Instruct-2507 | FP16 | Not supported | 18.98 tok/sEstimated | 61GB (have 16GB) |
| google-t5/t5-3b | Q4 | Fits comfortably | 93.19 tok/sEstimated | 2GB (have 16GB) |
| google-t5/t5-3b | Q8 | Fits comfortably | 72.73 tok/sEstimated | 3GB (have 16GB) |
| google-t5/t5-3b | FP16 | Fits comfortably | 37.60 tok/sEstimated | 6GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B | FP16 | Fits comfortably | 33.07 tok/sEstimated | 11GB (have 16GB) |
| mlx-community/gpt-oss-20b-MXFP4-Q8 | Q4 | Fits comfortably | 45.20 tok/sEstimated | 10GB (have 16GB) |
| mlx-community/gpt-oss-20b-MXFP4-Q8 | Q8 | Not supported | 35.15 tok/sEstimated | 20GB (have 16GB) |
| kaitchup/Phi-3-mini-4k-instruct-gptq-4bit | Q8 | Fits comfortably | 65.81 tok/sEstimated | 4GB (have 16GB) |
| kaitchup/Phi-3-mini-4k-instruct-gptq-4bit | FP16 | Fits comfortably | 32.65 tok/sEstimated | 9GB (have 16GB) |
| Qwen/Qwen2.5-1.5B | Q4 | Fits comfortably | 94.14 tok/sEstimated | 3GB (have 16GB) |
| WeiboAI/VibeThinker-1.5B | FP16 | Fits comfortably | 42.45 tok/sEstimated | 4GB (have 16GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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