Quick Answer: NVIDIA H100 PCIe 80GB offers 80GB VRAM and starts around current market pricing. It delivers approximately 414 tokens/sec on Qwen/Qwen2.5-3B-Instruct. It typically draws 350W 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 |
|---|---|---|---|
| Qwen/Qwen2.5-3B-Instruct | Q4 | 414.49 tok/sEstimated Auto-generated benchmark | 2GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 408.61 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2-2b-it | Q4 | 405.35 tok/sEstimated Auto-generated benchmark | 1GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 405.06 tok/sEstimated Auto-generated benchmark | 1GB |
| LiquidAI/LFM2-1.2B | Q4 | 401.13 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 399.11 tok/sEstimated Auto-generated benchmark | 2GB |
| google-bert/bert-base-uncased | Q4 | 398.51 tok/sEstimated Auto-generated benchmark | 1GB |
| allenai/OLMo-2-0425-1B | Q4 | 394.28 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/gemma-3-1b-it | Q4 | 394.22 tok/sEstimated Auto-generated benchmark | 1GB |
| inference-net/Schematron-3B | Q4 | 393.70 tok/sEstimated Auto-generated benchmark | 2GB |
| bigcode/starcoder2-3b | Q4 | 393.29 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B | Q4 | 392.49 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B | Q4 | 391.10 tok/sEstimated Auto-generated benchmark | 1GB |
| google-t5/t5-3b | Q4 | 386.78 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 384.08 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 378.12 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 377.46 tok/sEstimated Auto-generated benchmark | 1GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 377.27 tok/sEstimated Auto-generated benchmark | 2GB |
| tencent/HunyuanOCR | Q4 | 374.47 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 372.47 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-research/PowerMoE-3b | Q4 | 372.34 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2b | Q4 | 370.82 tok/sEstimated Auto-generated benchmark | 1GB |
| google/embeddinggemma-300m | Q4 | 364.43 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 359.22 tok/sEstimated Auto-generated benchmark | 2GB |
| nari-labs/Dia2-2B | Q4 | 350.22 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B | Q4 | 350.13 tok/sEstimated Auto-generated benchmark | 2GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 348.34 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-3-1b-it | Q4 | 346.03 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 345.77 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 344.29 tok/sEstimated Auto-generated benchmark | 2GB |
| distilbert/distilgpt2 | Q4 | 343.84 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-1.5B | Q4 | 343.81 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen2.5-1.5B-Instruct | Q4 | 343.31 tok/sEstimated Auto-generated benchmark | 3GB |
| Alibaba-NLP/gte-Qwen2-1.5B-instruct | Q4 | 342.39 tok/sEstimated Auto-generated benchmark | 3GB |
| openai-community/gpt2-medium | Q4 | 342.13 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-V3-0324 | Q4 | 341.97 tok/sEstimated Auto-generated benchmark | 4GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 341.57 tok/sEstimated Auto-generated benchmark | 1GB |
| HuggingFaceM4/tiny-random-LlamaForCausalLM | Q4 | 341.51 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Meta-Llama-3-8B-Instruct | Q4 | 341.03 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-3.1-8B | Q4 | 340.84 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct | Q4 | 339.74 tok/sEstimated Auto-generated benchmark | 4GB |
| rednote-hilab/dots.ocr | Q4 | 339.60 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-7B-Instruct | Q4 | 339.43 tok/sEstimated Auto-generated benchmark | 4GB |
| rinna/japanese-gpt-neox-small | Q4 | 338.98 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-V3.1 | Q4 | 337.19 tok/sEstimated Auto-generated benchmark | 4GB |
| HuggingFaceTB/SmolLM-135M | Q4 | 336.83 tok/sEstimated Auto-generated benchmark | 4GB |
| Tongyi-MAI/Z-Image-Turbo | Q4 | 336.09 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-0.5B | Q4 | 335.88 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen3-1.7B-Base | Q4 | 334.53 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2-1.5B-Instruct | Q4 | 334.26 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 |
|---|---|---|---|---|
| mlx-community/gpt-oss-20b-MXFP4-Q8 | FP16 | Fits comfortably | 62.03 tok/sEstimated | 41GB (have 80GB) |
| kaitchup/Phi-3-mini-4k-instruct-gptq-4bit | FP16 | Fits comfortably | 121.86 tok/sEstimated | 9GB (have 80GB) |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-8bit | Q4 | Fits comfortably | 312.27 tok/sEstimated | 4GB (have 80GB) |
| unsloth/gpt-oss-20b-unsloth-bnb-4bit | Q8 | Fits comfortably | 110.31 tok/sEstimated | 20GB (have 80GB) |
| Qwen/QwQ-32B-Preview | Q4 | Fits comfortably | 103.33 tok/sEstimated | 17GB (have 80GB) |
| Qwen/Qwen2.5-32B-Instruct | Q4 | Fits comfortably | 101.90 tok/sEstimated | 17GB (have 80GB) |
| openai/gpt-oss-safeguard-20b | FP16 | Fits comfortably | 64.08 tok/sEstimated | 44GB (have 80GB) |
| moonshotai/Kimi-K2-Thinking | Q8 | Not supported | 81.64 tok/sEstimated | 978GB (have 80GB) |
| black-forest-labs/FLUX.1-dev | FP16 | Fits comfortably | 118.74 tok/sEstimated | 16GB (have 80GB) |
| google/embeddinggemma-300m | Q4 | Fits comfortably | 364.43 tok/sEstimated | 1GB (have 80GB) |
| WeiboAI/VibeThinker-1.5B | Q4 | Fits comfortably | 408.61 tok/sEstimated | 1GB (have 80GB) |
| Qwen/Qwen2.5-7B-Instruct | FP16 | Fits comfortably | 109.05 tok/sEstimated | 15GB (have 80GB) |
| Qwen/Qwen3-0.6B | Q4 | Fits comfortably | 288.51 tok/sEstimated | 3GB (have 80GB) |
| Qwen/Qwen3-0.6B | Q8 | Fits comfortably | 210.96 tok/sEstimated | 6GB (have 80GB) |
| bigscience/bloomz-560m | FP16 | Fits comfortably | 125.43 tok/sEstimated | 15GB (have 80GB) |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q8 | Fits comfortably | 287.71 tok/sEstimated | 3GB (have 80GB) |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | FP16 | Fits comfortably | 151.30 tok/sEstimated | 6GB (have 80GB) |
| openai-community/gpt2 | Q4 | Fits comfortably | 310.90 tok/sEstimated | 4GB (have 80GB) |
| openai-community/gpt2 | Q8 | Fits comfortably | 233.70 tok/sEstimated | 7GB (have 80GB) |
| openai-community/gpt2 | FP16 | Fits comfortably | 119.66 tok/sEstimated | 15GB (have 80GB) |
| Qwen/Qwen2.5-7B-Instruct | Q8 | Fits comfortably | 201.73 tok/sEstimated | 7GB (have 80GB) |
| Qwen/Qwen3-0.6B | FP16 | Fits comfortably | 115.77 tok/sEstimated | 13GB (have 80GB) |
| meta-llama/Llama-3.1-8B-Instruct | Q8 | Fits comfortably | 226.88 tok/sEstimated | 9GB (have 80GB) |
| meta-llama/Meta-Llama-3-8B-Instruct | Q4 | Fits comfortably | 341.03 tok/sEstimated | 4GB (have 80GB) |
| meta-llama/Meta-Llama-3-8B-Instruct | Q8 | Fits comfortably | 226.54 tok/sEstimated | 9GB (have 80GB) |
| meta-llama/Meta-Llama-3-8B-Instruct | FP16 | Fits comfortably | 114.89 tok/sEstimated | 17GB (have 80GB) |
| Qwen/Qwen3-Embedding-0.6B | Q8 | Fits comfortably | 241.43 tok/sEstimated | 6GB (have 80GB) |
| Qwen/Qwen3-Embedding-0.6B | FP16 | Fits comfortably | 110.48 tok/sEstimated | 13GB (have 80GB) |
| Qwen/Qwen2.5-1.5B-Instruct | Q4 | Fits comfortably | 343.31 tok/sEstimated | 3GB (have 80GB) |
| Qwen/Qwen2.5-1.5B-Instruct | Q8 | Fits comfortably | 210.08 tok/sEstimated | 5GB (have 80GB) |
| Qwen/Qwen2.5-1.5B-Instruct | FP16 | Fits comfortably | 118.35 tok/sEstimated | 11GB (have 80GB) |
| facebook/opt-125m | Q4 | Fits comfortably | 307.69 tok/sEstimated | 4GB (have 80GB) |
| facebook/opt-125m | Q8 | Fits comfortably | 210.56 tok/sEstimated | 7GB (have 80GB) |
| facebook/opt-125m | FP16 | Fits comfortably | 112.80 tok/sEstimated | 15GB (have 80GB) |
| Qwen/Qwen3-4B-Instruct-2507 | FP16 | Fits comfortably | 129.28 tok/sEstimated | 9GB (have 80GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | Fits comfortably | 377.46 tok/sEstimated | 1GB (have 80GB) |
| openai/gpt-oss-120b | Q4 | Fits comfortably | 59.12 tok/sEstimated | 59GB (have 80GB) |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | Fits comfortably | 377.27 tok/sEstimated | 2GB (have 80GB) |
| mistralai/Mistral-7B-Instruct-v0.2 | Q4 | Fits comfortably | 294.20 tok/sEstimated | 4GB (have 80GB) |
| mistralai/Mistral-7B-Instruct-v0.2 | Q8 | Fits comfortably | 205.23 tok/sEstimated | 7GB (have 80GB) |
| mistralai/Mistral-7B-Instruct-v0.2 | FP16 | Fits comfortably | 112.13 tok/sEstimated | 15GB (have 80GB) |
| Qwen/Qwen3-8B | Q4 | Fits comfortably | 322.19 tok/sEstimated | 4GB (have 80GB) |
| Qwen/Qwen3-8B | Q8 | Fits comfortably | 238.17 tok/sEstimated | 9GB (have 80GB) |
| meta-llama/Llama-3.2-1B | Q4 | Fits comfortably | 391.10 tok/sEstimated | 1GB (have 80GB) |
| Qwen/Qwen2.5-0.5B-Instruct | Q8 | Fits comfortably | 214.29 tok/sEstimated | 5GB (have 80GB) |
| Qwen/Qwen2.5-0.5B-Instruct | FP16 | Fits comfortably | 119.33 tok/sEstimated | 11GB (have 80GB) |
| Qwen/Qwen3-32B | Q4 | Fits comfortably | 103.00 tok/sEstimated | 16GB (have 80GB) |
| Qwen/Qwen3-32B | Q8 | Fits comfortably | 72.37 tok/sEstimated | 33GB (have 80GB) |
| allenai/OLMo-2-0425-1B | Q4 | Fits comfortably | 394.28 tok/sEstimated | 1GB (have 80GB) |
| meta-llama/Llama-3.2-1B | FP16 | Fits comfortably | 138.71 tok/sEstimated | 2GB (have 80GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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