Quick Answer: Intel Arc A750 offers 8GB VRAM and starts around $199.99. It delivers approximately 107 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.
With 8GB VRAM, Intel Arc A750 can run models up to approximately 20B parameters using 4-bit quantization. This is suitable for 7B-13B models like Llama 3 8B, Mistral 7B, and Qwen 7B.
Consider RTX 4070 or RTX 4080 — Significant performance increase for AI workloads.
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| Model | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|
| deepseek-ai/DeepSeek-OCR | Q4 | 106.60 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/DeepSeek-OCR-2 | Q4 | 104.81 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 103.08 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B | Q4 | 102.55 tok/sEstimated Auto-generated benchmark | 2GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 102.42 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 102.28 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 102.06 tok/sEstimated Auto-generated benchmark | 2GB |
| google/embeddinggemma-300m | Q4 | 101.79 tok/sEstimated Auto-generated benchmark | 1GB |
| nari-labs/Dia2-2B | Q4 | 100.86 tok/sEstimated Auto-generated benchmark | 2GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 100.37 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 99.22 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/gemma-3-1b-it | Q4 | 98.55 tok/sEstimated Auto-generated benchmark | 1GB |
| bigcode/starcoder2-3b | Q4 | 98.26 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2b | Q4 | 98.22 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-3-1b-it | Q4 | 96.93 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B | Q4 | 96.86 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-research/PowerMoE-3b | Q4 | 96.13 tok/sEstimated Auto-generated benchmark | 2GB |
| LiquidAI/LFM2-1.2B | Q4 | 96.05 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 95.64 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 95.52 tok/sEstimated Auto-generated benchmark | 2GB |
| google-t5/t5-3b | Q4 | 95.31 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2-2b-it | Q4 | 95.22 tok/sEstimated Auto-generated benchmark | 1GB |
| inference-net/Schematron-3B | Q4 | 94.51 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 94.39 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen3-ASR-1.7B | Q4 | 94.01 tok/sEstimated Auto-generated benchmark | 2GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 93.86 tok/sEstimated Auto-generated benchmark | 2GB |
| nineninesix/kani-tts-2-en | Q4 | 93.81 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B | Q4 | 93.55 tok/sEstimated Auto-generated benchmark | 2GB |
| allenai/OLMo-2-0425-1B | Q4 | 91.76 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 90.05 tok/sEstimated Auto-generated benchmark | 1GB |
| tencent/HunyuanOCR | Q4 | 89.24 tok/sEstimated Auto-generated benchmark | 1GB |
| google-bert/bert-base-uncased | Q4 | 88.44 tok/sEstimated Auto-generated benchmark | 1GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 86.89 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 86.49 tok/sEstimated Auto-generated benchmark | 1GB |
| NousResearch/Meta-Llama-3.1-8B-Instruct | Q4 | 85.76 tok/sEstimated Auto-generated benchmark | 4GB |
| ibm-granite/granite-docling-258M | Q4 | 85.50 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Thinking-2507 | Q4 | 85.48 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct | Q4 | 85.41 tok/sEstimated Auto-generated benchmark | 4GB |
| openai-community/gpt2-xl | Q4 | 85.35 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2-7B-Instruct | Q4 | 85.24 tok/sEstimated Auto-generated benchmark | 4GB |
| Nanbeige/Nanbeige4.1-3B | Q4 | 85.20 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen3-Embedding-4B | Q4 | 85.10 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-Math-1.5B | Q4 | 84.97 tok/sEstimated Auto-generated benchmark | 3GB |
| huggyllama/llama-7b | Q4 | 84.86 tok/sEstimated Auto-generated benchmark | 4GB |
| trl-internal-testing/tiny-random-LlamaForCausalLM | Q4 | 84.79 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2-0.5B-Instruct | Q4 | 84.63 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen3-8B-FP8 | Q4 | 84.57 tok/sEstimated Auto-generated benchmark | 4GB |
| IlyaGusev/saiga_llama3_8b | Q4 | 84.46 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-7B-Instruct | Q4 | 84.33 tok/sEstimated Auto-generated benchmark | 4GB |
| rinna/japanese-gpt-neox-small | Q4 | 84.14 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 |
|---|---|---|---|---|
| openai-community/gpt2 | Q8 | Fits (tight) | 53.20 tok/sEstimated | 7GB (have 8GB) |
| openai-community/gpt2 | FP16 | Not supported | 29.83 tok/sEstimated | 15GB (have 8GB) |
| Qwen/Qwen2.5-7B-Instruct | Q4 | Fits comfortably | 84.33 tok/sEstimated | 4GB (have 8GB) |
| Qwen/Qwen2.5-7B-Instruct | Q8 | Fits (tight) | 59.23 tok/sEstimated | 7GB (have 8GB) |
| Qwen/Qwen2.5-7B-Instruct | FP16 | Not supported | 30.91 tok/sEstimated | 15GB (have 8GB) |
| Qwen/Qwen3-0.6B | Q4 | Fits comfortably | 76.01 tok/sEstimated | 3GB (have 8GB) |
| Qwen/Qwen3-0.6B | Q8 | Fits comfortably | 51.82 tok/sEstimated | 6GB (have 8GB) |
| Qwen/Qwen3-0.6B | FP16 | Not supported | 31.68 tok/sEstimated | 13GB (have 8GB) |
| Gensyn/Qwen2.5-0.5B-Instruct | Q4 | Fits comfortably | 79.01 tok/sEstimated | 3GB (have 8GB) |
| Gensyn/Qwen2.5-0.5B-Instruct | Q8 | Fits comfortably | 53.43 tok/sEstimated | 5GB (have 8GB) |
| Gensyn/Qwen2.5-0.5B-Instruct | FP16 | Not supported | 27.32 tok/sEstimated | 11GB (have 8GB) |
| meta-llama/Llama-3.1-8B-Instruct | Q4 | Fits comfortably | 75.52 tok/sEstimated | 4GB (have 8GB) |
| meta-llama/Llama-3.1-8B-Instruct | Q8 | Not supported | 51.87 tok/sEstimated | 9GB (have 8GB) |
| meta-llama/Llama-3.1-8B-Instruct | FP16 | Not supported | 30.91 tok/sEstimated | 17GB (have 8GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | Q4 | Not supported | 27.15 tok/sEstimated | 17GB (have 8GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | Q8 | Not supported | 18.31 tok/sEstimated | 35GB (have 8GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | FP16 | Not supported | 9.95 tok/sEstimated | 70GB (have 8GB) |
| openai/gpt-oss-20b | Q4 | Not supported | 42.69 tok/sEstimated | 10GB (have 8GB) |
| openai/gpt-oss-20b | Q8 | Not supported | 28.63 tok/sEstimated | 20GB (have 8GB) |
| openai/gpt-oss-20b | FP16 | Not supported | 17.39 tok/sEstimated | 41GB (have 8GB) |
| google/gemma-3-1b-it | Q4 | Fits comfortably | 96.93 tok/sEstimated | 1GB (have 8GB) |
| google/gemma-3-1b-it | Q8 | Fits comfortably | 61.81 tok/sEstimated | 1GB (have 8GB) |
| google/gemma-3-1b-it | FP16 | Fits comfortably | 37.24 tok/sEstimated | 2GB (have 8GB) |
| Qwen/Qwen3-Embedding-0.6B | Q4 | Fits comfortably | 79.67 tok/sEstimated | 3GB (have 8GB) |
| Qwen/Qwen3-Embedding-0.6B | Q8 | Fits comfortably | 51.18 tok/sEstimated | 6GB (have 8GB) |
| Qwen/Qwen3-Embedding-0.6B | FP16 | Not supported | 27.08 tok/sEstimated | 13GB (have 8GB) |
| Qwen/Qwen2.5-1.5B-Instruct | Q4 | Fits comfortably | 82.60 tok/sEstimated | 3GB (have 8GB) |
| Qwen/Qwen2.5-1.5B-Instruct | Q8 | Fits comfortably | 52.66 tok/sEstimated | 5GB (have 8GB) |
| Qwen/Qwen2.5-1.5B-Instruct | FP16 | Not supported | 27.32 tok/sEstimated | 11GB (have 8GB) |
| facebook/opt-125m | Q4 | Fits comfortably | 75.37 tok/sEstimated | 4GB (have 8GB) |
| facebook/opt-125m | Q8 | Fits (tight) | 55.71 tok/sEstimated | 7GB (have 8GB) |
| facebook/opt-125m | FP16 | Not supported | 27.98 tok/sEstimated | 15GB (have 8GB) |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | Fits comfortably | 100.37 tok/sEstimated | 1GB (have 8GB) |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q8 | Fits comfortably | 67.64 tok/sEstimated | 1GB (have 8GB) |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | FP16 | Fits comfortably | 33.98 tok/sEstimated | 2GB (have 8GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q4 | Fits comfortably | 74.99 tok/sEstimated | 4GB (have 8GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q8 | Fits (tight) | 60.27 tok/sEstimated | 7GB (have 8GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | FP16 | Not supported | 32.71 tok/sEstimated | 15GB (have 8GB) |
| Qwen/Qwen3-4B-Instruct-2507 | Q4 | Fits comfortably | 79.64 tok/sEstimated | 2GB (have 8GB) |
| Qwen/Qwen3-4B-Instruct-2507 | Q8 | Fits comfortably | 52.50 tok/sEstimated | 4GB (have 8GB) |
| Qwen/Qwen3-4B-Instruct-2507 | FP16 | Not supported | 31.57 tok/sEstimated | 9GB (have 8GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | Fits comfortably | 86.49 tok/sEstimated | 1GB (have 8GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q8 | Fits comfortably | 67.67 tok/sEstimated | 1GB (have 8GB) |
| meta-llama/Llama-3.2-1B-Instruct | FP16 | Fits comfortably | 37.03 tok/sEstimated | 2GB (have 8GB) |
| openai/gpt-oss-120b | Q4 | Not supported | 14.44 tok/sEstimated | 59GB (have 8GB) |
| openai/gpt-oss-120b | Q8 | Not supported | 11.76 tok/sEstimated | 117GB (have 8GB) |
| openai/gpt-oss-120b | FP16 | Not supported | 5.95 tok/sEstimated | 235GB (have 8GB) |
| Qwen/Qwen2.5-3B-Instruct | Q4 | Fits comfortably | 94.39 tok/sEstimated | 2GB (have 8GB) |
| Qwen/Qwen2.5-3B-Instruct | Q8 | Fits comfortably | 68.30 tok/sEstimated | 3GB (have 8GB) |
| openai-community/gpt2 | Q4 | Fits comfortably | 81.83 tok/sEstimated | 4GB (have 8GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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