Quick Answer: Intel Arc A750 offers 8GB VRAM and starts around $199.99. It delivers approximately 105 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 | 104.52 tok/sEstimated Auto-generated benchmark | 2GB |
| facebook/sam3 | Q4 | 101.25 tok/sEstimated Auto-generated benchmark | 1GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 101.00 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 100.93 tok/sEstimated Auto-generated benchmark | 1GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 99.90 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 98.39 tok/sEstimated Auto-generated benchmark | 2GB |
| inference-net/Schematron-3B | Q4 | 98.31 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 96.85 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B | Q4 | 96.62 tok/sEstimated Auto-generated benchmark | 1GB |
| google-bert/bert-base-uncased | Q4 | 96.58 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2-2b-it | Q4 | 96.18 tok/sEstimated Auto-generated benchmark | 1GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 96.03 tok/sEstimated Auto-generated benchmark | 1GB |
| tencent/HunyuanOCR | Q4 | 95.41 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B | Q4 | 94.13 tok/sEstimated Auto-generated benchmark | 2GB |
| allenai/OLMo-2-0425-1B | Q4 | 92.23 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/gemma-3-1b-it | Q4 | 91.34 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 91.11 tok/sEstimated Auto-generated benchmark | 2GB |
| bigcode/starcoder2-3b | Q4 | 91.05 tok/sEstimated Auto-generated benchmark | 2GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 90.01 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B | Q4 | 88.89 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 88.51 tok/sEstimated Auto-generated benchmark | 1GB |
| google-t5/t5-3b | Q4 | 88.21 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 87.52 tok/sEstimated Auto-generated benchmark | 2GB |
| nari-labs/Dia2-2B | Q4 | 87.42 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-research/PowerMoE-3b | Q4 | 86.98 tok/sEstimated Auto-generated benchmark | 2GB |
| google/embeddinggemma-300m | Q4 | 86.51 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-3-1b-it | Q4 | 86.38 tok/sEstimated Auto-generated benchmark | 1GB |
| LiquidAI/LFM2-1.2B | Q4 | 86.25 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-Coder-1.5B | Q4 | 86.04 tok/sEstimated Auto-generated benchmark | 3GB |
| petals-team/StableBeluga2 | Q4 | 85.99 tok/sEstimated Auto-generated benchmark | 4GB |
| hmellor/tiny-random-LlamaForCausalLM | Q4 | 85.88 tok/sEstimated Auto-generated benchmark | 4GB |
| HuggingFaceTB/SmolLM2-135M | Q4 | 85.84 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Instruct-2507 | Q4 | 85.80 tok/sEstimated Auto-generated benchmark | 2GB |
| trl-internal-testing/tiny-LlamaForCausalLM-3.2 | Q4 | 85.74 tok/sEstimated Auto-generated benchmark | 4GB |
| zai-org/GLM-4.6-FP8 | Q4 | 85.69 tok/sEstimated Auto-generated benchmark | 4GB |
| google/gemma-2b | Q4 | 85.66 tok/sEstimated Auto-generated benchmark | 1GB |
| mistralai/Mistral-7B-Instruct-v0.1 | Q4 | 85.55 tok/sEstimated Auto-generated benchmark | 4GB |
| ibm-granite/granite-3.3-8b-instruct | Q4 | 85.55 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 85.53 tok/sEstimated Auto-generated benchmark | 1GB |
| microsoft/VibeVoice-1.5B | Q4 | 85.50 tok/sEstimated Auto-generated benchmark | 3GB |
| deepseek-ai/DeepSeek-V3.1 | Q4 | 85.23 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Base | Q4 | 85.23 tok/sEstimated Auto-generated benchmark | 2GB |
| sshleifer/tiny-gpt2 | Q4 | 85.22 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-1.7B | Q4 | 85.20 tok/sEstimated Auto-generated benchmark | 4GB |
| openai-community/gpt2-medium | Q4 | 85.12 tok/sEstimated Auto-generated benchmark | 4GB |
| black-forest-labs/FLUX.2-dev | Q4 | 85.07 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 84.83 tok/sEstimated Auto-generated benchmark | 1GB |
| GSAI-ML/LLaDA-8B-Base | Q4 | 84.74 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-0.5B | Q4 | 84.73 tok/sEstimated Auto-generated benchmark | 3GB |
| deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct | Q4 | 84.71 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/Qwen2-1.5B-Instruct | FP16 | Not supported | 31.59 tok/sEstimated | 11GB (have 8GB) |
| meta-llama/Llama-Guard-3-1B | Q4 | Fits comfortably | 85.53 tok/sEstimated | 1GB (have 8GB) |
| meta-llama/Llama-Guard-3-1B | Q8 | Fits comfortably | 68.95 tok/sEstimated | 1GB (have 8GB) |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-4bit | Q4 | Fits comfortably | 73.74 tok/sEstimated | 2GB (have 8GB) |
| unsloth/Llama-3.2-3B-Instruct | Q4 | Fits comfortably | 87.52 tok/sEstimated | 2GB (have 8GB) |
| unsloth/Llama-3.2-3B-Instruct | Q8 | Fits comfortably | 63.91 tok/sEstimated | 3GB (have 8GB) |
| deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct | Q4 | Fits comfortably | 84.71 tok/sEstimated | 4GB (have 8GB) |
| deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct | Q8 | Fits (tight) | 49.93 tok/sEstimated | 7GB (have 8GB) |
| Qwen/Qwen2.5-Coder-7B-Instruct | FP16 | Not supported | 28.16 tok/sEstimated | 15GB (have 8GB) |
| RedHatAI/Llama-3.2-90B-Vision-Instruct-FP8-dynamic | FP16 | Not supported | 6.48 tok/sEstimated | 176GB (have 8GB) |
| google/gemma-2b | Q8 | Fits comfortably | 60.22 tok/sEstimated | 2GB (have 8GB) |
| google/gemma-2b | FP16 | Fits comfortably | 33.55 tok/sEstimated | 4GB (have 8GB) |
| unsloth/Llama-3.2-1B-Instruct | FP16 | Fits comfortably | 32.59 tok/sEstimated | 2GB (have 8GB) |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-4bit | Q4 | Fits comfortably | 72.60 tok/sEstimated | 4GB (have 8GB) |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-4bit | Q8 | Not supported | 52.15 tok/sEstimated | 9GB (have 8GB) |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-4bit | FP16 | Not supported | 28.50 tok/sEstimated | 17GB (have 8GB) |
| Qwen/Qwen3-30B-A3B-Instruct-2507-FP8 | Q4 | Not supported | 45.54 tok/sEstimated | 15GB (have 8GB) |
| Qwen/Qwen3-30B-A3B-Instruct-2507-FP8 | Q8 | Not supported | 27.51 tok/sEstimated | 31GB (have 8GB) |
| Qwen/Qwen3-4B-Thinking-2507 | FP16 | Not supported | 31.70 tok/sEstimated | 9GB (have 8GB) |
| HuggingFaceH4/zephyr-7b-beta | FP16 | Not supported | 29.65 tok/sEstimated | 15GB (have 8GB) |
| llamafactory/tiny-random-Llama-3 | Q4 | Fits comfortably | 77.93 tok/sEstimated | 4GB (have 8GB) |
| llamafactory/tiny-random-Llama-3 | Q8 | Fits (tight) | 54.10 tok/sEstimated | 7GB (have 8GB) |
| llamafactory/tiny-random-Llama-3 | FP16 | Not supported | 26.99 tok/sEstimated | 15GB (have 8GB) |
| microsoft/Phi-4-mini-instruct | Q4 | Fits comfortably | 81.00 tok/sEstimated | 4GB (have 8GB) |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-8bit | Q4 | Fits comfortably | 73.20 tok/sEstimated | 4GB (have 8GB) |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-8bit | Q8 | Not supported | 55.40 tok/sEstimated | 9GB (have 8GB) |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-8bit | FP16 | Not supported | 32.71 tok/sEstimated | 17GB (have 8GB) |
| Qwen/Qwen3-235B-A22B | Q4 | Not supported | 9.40 tok/sEstimated | 115GB (have 8GB) |
| Qwen/Qwen3-4B-Thinking-2507-FP8 | FP16 | Not supported | 31.83 tok/sEstimated | 9GB (have 8GB) |
| google/gemma-3-270m-it | Q4 | Fits comfortably | 71.65 tok/sEstimated | 4GB (have 8GB) |
| google/gemma-3-270m-it | Q8 | Fits (tight) | 51.29 tok/sEstimated | 7GB (have 8GB) |
| bigcode/starcoder2-3b | FP16 | Fits comfortably | 38.56 tok/sEstimated | 6GB (have 8GB) |
| unsloth/gemma-3-1b-it | Q4 | Fits comfortably | 91.34 tok/sEstimated | 1GB (have 8GB) |
| Qwen/Qwen2-0.5B-Instruct | Q8 | Fits comfortably | 59.21 tok/sEstimated | 5GB (have 8GB) |
| Qwen/Qwen2-0.5B-Instruct | FP16 | Not supported | 29.93 tok/sEstimated | 11GB (have 8GB) |
| lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-8bit | Q4 | Not supported | 43.28 tok/sEstimated | 15GB (have 8GB) |
| BSC-LT/salamandraTA-7b-instruct | Q4 | Fits comfortably | 82.06 tok/sEstimated | 4GB (have 8GB) |
| BSC-LT/salamandraTA-7b-instruct | Q8 | Fits (tight) | 51.44 tok/sEstimated | 7GB (have 8GB) |
| unsloth/gpt-oss-20b-unsloth-bnb-4bit | Q4 | Not supported | 44.45 tok/sEstimated | 10GB (have 8GB) |
| unsloth/gpt-oss-20b-unsloth-bnb-4bit | Q8 | Not supported | 31.94 tok/sEstimated | 20GB (have 8GB) |
| apple/OpenELM-1_1B-Instruct | Q8 | Fits comfortably | 70.94 tok/sEstimated | 1GB (have 8GB) |
| apple/OpenELM-1_1B-Instruct | FP16 | Fits comfortably | 36.47 tok/sEstimated | 2GB (have 8GB) |
| AI-MO/Kimina-Prover-72B | Q4 | Not supported | 15.02 tok/sEstimated | 35GB (have 8GB) |
| lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-4bit | FP16 | Not supported | 14.83 tok/sEstimated | 61GB (have 8GB) |
| Qwen/Qwen3-30B-A3B-Thinking-2507 | Q4 | Not supported | 45.49 tok/sEstimated | 15GB (have 8GB) |
| mistralai/Mixtral-8x22B-Instruct-v0.1 | Q8 | Not supported | 14.08 tok/sEstimated | 138GB (have 8GB) |
| mistralai/Mixtral-8x22B-Instruct-v0.1 | FP16 | Not supported | 8.32 tok/sEstimated | 275GB (have 8GB) |
| Qwen/Qwen3-30B-A3B-Thinking-2507 | Q8 | Not supported | 29.22 tok/sEstimated | 31GB (have 8GB) |
| Qwen/Qwen3-30B-A3B-Thinking-2507 | FP16 | Not supported | 16.29 tok/sEstimated | 61GB (have 8GB) |
| google/gemma-2-2b-it | FP16 | Fits comfortably | 34.87 tok/sEstimated | 4GB (have 8GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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