Quick Answer: Intel Arc Pro A60 offers 12GB VRAM and starts around current market pricing. It delivers approximately 80 tokens/sec on ibm-research/PowerMoE-3b. It typically draws 130W 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 |
|---|---|---|---|
| ibm-research/PowerMoE-3b | Q4 | 80.43 tok/sEstimated Auto-generated benchmark | 2GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 80.15 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B | Q4 | 79.80 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 79.60 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 78.80 tok/sEstimated Auto-generated benchmark | 1GB |
| tencent/HunyuanOCR | Q4 | 78.68 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 78.45 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 78.04 tok/sEstimated Auto-generated benchmark | 2GB |
| google-bert/bert-base-uncased | Q4 | 77.18 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 77.16 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B | Q4 | 77.11 tok/sEstimated Auto-generated benchmark | 2GB |
| allenai/OLMo-2-0425-1B | Q4 | 77.06 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 75.66 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/gemma-3-1b-it | Q4 | 75.47 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-3-1b-it | Q4 | 74.70 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 74.02 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 73.62 tok/sEstimated Auto-generated benchmark | 1GB |
| bigcode/starcoder2-3b | Q4 | 73.43 tok/sEstimated Auto-generated benchmark | 2GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 73.42 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 73.28 tok/sEstimated Auto-generated benchmark | 2GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 73.22 tok/sEstimated Auto-generated benchmark | 2GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 73.15 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2b | Q4 | 72.04 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 71.79 tok/sEstimated Auto-generated benchmark | 1GB |
| google-t5/t5-3b | Q4 | 71.54 tok/sEstimated Auto-generated benchmark | 2GB |
| google/embeddinggemma-300m | Q4 | 69.60 tok/sEstimated Auto-generated benchmark | 1GB |
| nari-labs/Dia2-2B | Q4 | 69.39 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B | Q4 | 68.11 tok/sEstimated Auto-generated benchmark | 2GB |
| EleutherAI/gpt-neo-125m | Q4 | 67.39 tok/sEstimated Auto-generated benchmark | 4GB |
| black-forest-labs/FLUX.2-dev | Q4 | 67.33 tok/sEstimated Auto-generated benchmark | 4GB |
| google/gemma-2-2b-it | Q4 | 67.30 tok/sEstimated Auto-generated benchmark | 1GB |
| black-forest-labs/FLUX.1-dev | Q4 | 67.29 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-8B-Base | Q4 | 67.25 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-Embedding-4B | Q4 | 67.20 tok/sEstimated Auto-generated benchmark | 2GB |
| GSAI-ML/LLaDA-8B-Instruct | Q4 | 67.15 tok/sEstimated Auto-generated benchmark | 4GB |
| inference-net/Schematron-3B | Q4 | 67.09 tok/sEstimated Auto-generated benchmark | 2GB |
| rinna/japanese-gpt-neox-small | Q4 | 67.08 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/DialoGPT-small | Q4 | 67.00 tok/sEstimated Auto-generated benchmark | 4GB |
| LiquidAI/LFM2-1.2B | Q4 | 66.96 tok/sEstimated Auto-generated benchmark | 1GB |
| skt/kogpt2-base-v2 | Q4 | 66.78 tok/sEstimated Auto-generated benchmark | 4GB |
| unsloth/Meta-Llama-3.1-8B-Instruct | Q4 | 66.74 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-Reranker-0.6B | Q4 | 66.71 tok/sEstimated Auto-generated benchmark | 3GB |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-6bit | Q4 | 66.51 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/DeepSeek-R1-Distill-Llama-8B | Q4 | 66.42 tok/sEstimated Auto-generated benchmark | 4GB |
| ibm-granite/granite-3.3-8b-instruct | Q4 | 66.34 tok/sEstimated Auto-generated benchmark | 4GB |
| lmsys/vicuna-7b-v1.5 | Q4 | 66.26 tok/sEstimated Auto-generated benchmark | 4GB |
| openai-community/gpt2-medium | Q4 | 65.97 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Base | Q4 | 65.86 tok/sEstimated Auto-generated benchmark | 2GB |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-8bit | Q4 | 65.81 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-1.5B | Q4 | 65.68 tok/sEstimated Auto-generated benchmark | 3GB |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
| Model | Quantization | Verdict | Estimated speed | VRAM needed |
|---|---|---|---|---|
| dphn/dolphin-2.9.1-yi-1.5-34b | Q8 | Not supported | 14.35 tok/sEstimated | 35GB (have 12GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | FP16 | Not supported | 7.86 tok/sEstimated | 70GB (have 12GB) |
| Qwen/Qwen3-Embedding-0.6B | FP16 | Not supported | 21.30 tok/sEstimated | 13GB (have 12GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q4 | Fits comfortably | 63.77 tok/sEstimated | 4GB (have 12GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | FP16 | Not supported | 22.16 tok/sEstimated | 15GB (have 12GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | Fits comfortably | 71.79 tok/sEstimated | 1GB (have 12GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q8 | Fits comfortably | 49.11 tok/sEstimated | 1GB (have 12GB) |
| meta-llama/Llama-3.2-1B-Instruct | FP16 | Fits comfortably | 26.63 tok/sEstimated | 2GB (have 12GB) |
| openai/gpt-oss-120b | Q4 | Not supported | 11.82 tok/sEstimated | 59GB (have 12GB) |
| bigscience/bloomz-560m | Q8 | Fits comfortably | 43.34 tok/sEstimated | 7GB (have 12GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q8 | Fits comfortably | 42.63 tok/sEstimated | 7GB (have 12GB) |
| bigscience/bloomz-560m | FP16 | Not supported | 21.19 tok/sEstimated | 15GB (have 12GB) |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q8 | Fits comfortably | 46.89 tok/sEstimated | 3GB (have 12GB) |
| mistralai/Mistral-7B-Instruct-v0.2 | Q4 | Fits comfortably | 61.93 tok/sEstimated | 4GB (have 12GB) |
| mistralai/Mistral-7B-Instruct-v0.2 | Q8 | Fits comfortably | 46.82 tok/sEstimated | 7GB (have 12GB) |
| inference-net/Schematron-3B | Q4 | Fits comfortably | 67.09 tok/sEstimated | 2GB (have 12GB) |
| meta-llama/Meta-Llama-3-8B-Instruct | FP16 | Not supported | 23.23 tok/sEstimated | 17GB (have 12GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B | Q4 | Fits comfortably | 63.33 tok/sEstimated | 3GB (have 12GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B | Q8 | Fits comfortably | 44.12 tok/sEstimated | 5GB (have 12GB) |
| Qwen/Qwen3-14B | FP16 | Not supported | 17.18 tok/sEstimated | 29GB (have 12GB) |
| unsloth/Meta-Llama-3.1-8B-Instruct | Q8 | Fits comfortably | 44.77 tok/sEstimated | 9GB (have 12GB) |
| meta-llama/Llama-3.2-3B-Instruct | FP16 | Fits comfortably | 29.48 tok/sEstimated | 6GB (have 12GB) |
| vikhyatk/moondream2 | Q4 | Fits comfortably | 62.57 tok/sEstimated | 4GB (have 12GB) |
| vikhyatk/moondream2 | FP16 | Not supported | 22.09 tok/sEstimated | 15GB (have 12GB) |
| petals-team/StableBeluga2 | Q4 | Fits comfortably | 56.71 tok/sEstimated | 4GB (have 12GB) |
| microsoft/Phi-3-mini-4k-instruct | Q4 | Fits comfortably | 58.58 tok/sEstimated | 4GB (have 12GB) |
| unsloth/gpt-oss-20b-BF16 | Q4 | Fits comfortably | 32.88 tok/sEstimated | 10GB (have 12GB) |
| HuggingFaceTB/SmolLM-135M | Q4 | Fits comfortably | 56.46 tok/sEstimated | 4GB (have 12GB) |
| HuggingFaceTB/SmolLM-135M | FP16 | Not supported | 25.38 tok/sEstimated | 15GB (have 12GB) |
| Qwen/Qwen2.5-Math-1.5B | Q4 | Fits comfortably | 57.88 tok/sEstimated | 3GB (have 12GB) |
| trl-internal-testing/tiny-random-LlamaForCausalLM | Q4 | Fits comfortably | 61.17 tok/sEstimated | 4GB (have 12GB) |
| trl-internal-testing/tiny-random-LlamaForCausalLM | FP16 | Not supported | 25.44 tok/sEstimated | 15GB (have 12GB) |
| openai-community/gpt2-medium | Q4 | Fits comfortably | 65.97 tok/sEstimated | 4GB (have 12GB) |
| openai-community/gpt2-medium | Q8 | Fits comfortably | 39.53 tok/sEstimated | 7GB (have 12GB) |
| openai-community/gpt2-medium | FP16 | Not supported | 23.93 tok/sEstimated | 15GB (have 12GB) |
| Qwen/Qwen3-0.6B-Base | Q4 | Fits comfortably | 57.14 tok/sEstimated | 3GB (have 12GB) |
| Qwen/Qwen3-0.6B-Base | Q8 | Fits comfortably | 41.92 tok/sEstimated | 6GB (have 12GB) |
| Qwen/Qwen3-4B-Base | Q8 | Fits comfortably | 38.92 tok/sEstimated | 4GB (have 12GB) |
| Qwen/Qwen3-4B-Base | FP16 | Fits comfortably | 22.44 tok/sEstimated | 9GB (have 12GB) |
| microsoft/Phi-3.5-mini-instruct | Q4 | Fits comfortably | 59.90 tok/sEstimated | 4GB (have 12GB) |
| google/gemma-2-2b-it | Q4 | Fits comfortably | 67.30 tok/sEstimated | 1GB (have 12GB) |
| google/gemma-2-2b-it | FP16 | Fits comfortably | 26.10 tok/sEstimated | 4GB (have 12GB) |
| Qwen/Qwen2-1.5B-Instruct | Q4 | Fits comfortably | 64.89 tok/sEstimated | 3GB (have 12GB) |
| meta-llama/Llama-Guard-3-1B | Q4 | Fits comfortably | 73.62 tok/sEstimated | 1GB (have 12GB) |
| meta-llama/Llama-Guard-3-1B | Q8 | Fits comfortably | 50.96 tok/sEstimated | 1GB (have 12GB) |
| meta-llama/Llama-Guard-3-1B | FP16 | Fits comfortably | 30.64 tok/sEstimated | 2GB (have 12GB) |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-6bit | Q8 | Fits comfortably | 46.64 tok/sEstimated | 4GB (have 12GB) |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-6bit | FP16 | Fits comfortably | 20.97 tok/sEstimated | 9GB (have 12GB) |
| RedHatAI/Meta-Llama-3.1-70B-Instruct-quantized.w4a16 | Q4 | Not supported | 21.51 tok/sEstimated | 34GB (have 12GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | Q4 | Not supported | 19.93 tok/sEstimated | 17GB (have 12GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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