Quick Answer: NVIDIA H100 SXM5 80GB offers 80GB VRAM and starts around current market pricing. It delivers approximately 670 tokens/sec on deepseek-ai/DeepSeek-OCR. It typically draws 700W 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 | 670.22 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 653.05 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-3-1b-it | Q4 | 647.60 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B | Q4 | 644.71 tok/sEstimated Auto-generated benchmark | 2GB |
| google/embeddinggemma-300m | Q4 | 633.03 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B | Q4 | 630.59 tok/sEstimated Auto-generated benchmark | 1GB |
| inference-net/Schematron-3B | Q4 | 630.53 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 628.40 tok/sEstimated Auto-generated benchmark | 2GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 627.50 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2-2b-it | Q4 | 621.86 tok/sEstimated Auto-generated benchmark | 1GB |
| LiquidAI/LFM2-1.2B | Q4 | 615.05 tok/sEstimated Auto-generated benchmark | 1GB |
| nari-labs/Dia2-2B | Q4 | 607.29 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 607.11 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2b | Q4 | 606.87 tok/sEstimated Auto-generated benchmark | 1GB |
| google-t5/t5-3b | Q4 | 603.53 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/gemma-3-1b-it | Q4 | 596.91 tok/sEstimated Auto-generated benchmark | 1GB |
| allenai/OLMo-2-0425-1B | Q4 | 596.15 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 583.27 tok/sEstimated Auto-generated benchmark | 1GB |
| tencent/HunyuanOCR | Q4 | 581.63 tok/sEstimated Auto-generated benchmark | 1GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 578.06 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 575.00 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 573.32 tok/sEstimated Auto-generated benchmark | 1GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 572.38 tok/sEstimated Auto-generated benchmark | 1GB |
| google-bert/bert-base-uncased | Q4 | 566.34 tok/sEstimated Auto-generated benchmark | 1GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 566.26 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 557.19 tok/sEstimated Auto-generated benchmark | 2GB |
| bigcode/starcoder2-3b | Q4 | 555.15 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 553.57 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 544.41 tok/sEstimated Auto-generated benchmark | 1GB |
| skt/kogpt2-base-v2 | Q4 | 544.41 tok/sEstimated Auto-generated benchmark | 4GB |
| IlyaGusev/saiga_llama3_8b | Q4 | 543.79 tok/sEstimated Auto-generated benchmark | 4GB |
| petals-team/StableBeluga2 | Q4 | 543.44 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-1.7B | Q4 | 541.60 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B | Q4 | 541.33 tok/sEstimated Auto-generated benchmark | 2GB |
| MiniMaxAI/MiniMax-M2 | Q4 | 540.89 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-V3.1 | Q4 | 540.07 tok/sEstimated Auto-generated benchmark | 4GB |
| parler-tts/parler-tts-large-v1 | Q4 | 539.44 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-R1-0528 | Q4 | 537.75 tok/sEstimated Auto-generated benchmark | 4GB |
| NousResearch/Meta-Llama-3.1-8B-Instruct | Q4 | 536.88 tok/sEstimated Auto-generated benchmark | 4GB |
| openai-community/gpt2-xl | Q4 | 535.87 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-3B | Q4 | 535.79 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-research/PowerMoE-3b | Q4 | 535.47 tok/sEstimated Auto-generated benchmark | 2GB |
| rinna/japanese-gpt-neox-small | Q4 | 535.21 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-0.5B-Instruct | Q4 | 534.22 tok/sEstimated Auto-generated benchmark | 3GB |
| meta-llama/Meta-Llama-3-8B | Q4 | 533.06 tok/sEstimated Auto-generated benchmark | 4GB |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-8bit | Q4 | 530.64 tok/sEstimated Auto-generated benchmark | 4GB |
| HuggingFaceTB/SmolLM-135M | Q4 | 527.64 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-Coder-1.5B | Q4 | 527.56 tok/sEstimated Auto-generated benchmark | 3GB |
| mistralai/Mistral-7B-v0.1 | Q4 | 527.42 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-Math-1.5B | Q4 | 525.55 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 |
|---|---|---|---|---|
| Qwen/Qwen3-0.6B | Q4 | Fits comfortably | 447.22 tok/sEstimated | 3GB (have 80GB) |
| Qwen/Qwen2.5-7B-Instruct | Q4 | Fits comfortably | 506.86 tok/sEstimated | 4GB (have 80GB) |
| Qwen/Qwen2.5-7B-Instruct | Q8 | Fits comfortably | 353.52 tok/sEstimated | 7GB (have 80GB) |
| Gensyn/Qwen2.5-0.5B-Instruct | FP16 | Fits comfortably | 193.75 tok/sEstimated | 11GB (have 80GB) |
| meta-llama/Llama-3.1-8B-Instruct | Q8 | Fits comfortably | 354.14 tok/sEstimated | 9GB (have 80GB) |
| mistralai/Mistral-Large-3-675B-Instruct-2512 | Q4 | Not supported | 54.56 tok/sEstimated | 378GB (have 80GB) |
| mistralai/Mistral-Large-3-675B-Instruct-2512 | Q8 | Not supported | 44.75 tok/sEstimated | 755GB (have 80GB) |
| meta-llama/Llama-3.2-3B-Instruct | FP16 | Fits comfortably | 169.51 tok/sEstimated | 7GB (have 80GB) |
| mistralai/Mixtral-8x22B-Instruct-v0.1 | Q4 | Fits comfortably | 118.60 tok/sEstimated | 69GB (have 80GB) |
| openai/gpt-oss-20b | Q4 | Fits comfortably | 298.25 tok/sEstimated | 10GB (have 80GB) |
| openai/gpt-oss-20b | Q8 | Fits comfortably | 182.01 tok/sEstimated | 20GB (have 80GB) |
| mistralai/Mixtral-8x22B-Instruct-v0.1 | Q8 | Not supported | 87.81 tok/sEstimated | 138GB (have 80GB) |
| openai/gpt-oss-20b | FP16 | Fits comfortably | 96.07 tok/sEstimated | 41GB (have 80GB) |
| mistralai/Mistral-Large-3-675B-Instruct-2512 | FP16 | Not supported | 23.68 tok/sEstimated | 1509GB (have 80GB) |
| EssentialAI/rnj-1 | Q4 | Fits comfortably | 383.69 tok/sEstimated | 5GB (have 80GB) |
| EssentialAI/rnj-1 | Q8 | Fits comfortably | 254.85 tok/sEstimated | 10GB (have 80GB) |
| EssentialAI/rnj-1 | FP16 | Fits comfortably | 130.61 tok/sEstimated | 19GB (have 80GB) |
| mistralai/Mixtral-8x22B-Instruct-v0.1 | FP16 | Not supported | 50.60 tok/sEstimated | 275GB (have 80GB) |
| mistralai/Mistral-Large-Instruct-2411 | Q8 | Not supported | 75.28 tok/sEstimated | 120GB (have 80GB) |
| mistralai/Mistral-Large-Instruct-2411 | FP16 | Not supported | 39.33 tok/sEstimated | 240GB (have 80GB) |
| google/gemma-3-1b-it | Q4 | Fits comfortably | 647.60 tok/sEstimated | 1GB (have 80GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q8 | Fits comfortably | 367.83 tok/sEstimated | 7GB (have 80GB) |
| google/gemma-3-1b-it | Q8 | Fits comfortably | 418.26 tok/sEstimated | 1GB (have 80GB) |
| google/gemma-3-1b-it | FP16 | Fits comfortably | 239.76 tok/sEstimated | 2GB (have 80GB) |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | Fits comfortably | 578.06 tok/sEstimated | 1GB (have 80GB) |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q8 | Fits comfortably | 425.32 tok/sEstimated | 1GB (have 80GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q4 | Fits comfortably | 514.69 tok/sEstimated | 4GB (have 80GB) |
| mistralai/Mistral-Small-Instruct-2409 | Q4 | Fits comfortably | 281.19 tok/sEstimated | 11GB (have 80GB) |
| deepseek-ai/DeepSeek-V2.5 | Q8 | Not supported | 128.34 tok/sEstimated | 656GB (have 80GB) |
| black-forest-labs/FLUX.2-dev | Q4 | Fits comfortably | 461.88 tok/sEstimated | 4GB (have 80GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | FP16 | Fits comfortably | 194.66 tok/sEstimated | 15GB (have 80GB) |
| Qwen/Qwen3-4B-Instruct-2507 | Q4 | Fits comfortably | 457.34 tok/sEstimated | 2GB (have 80GB) |
| Qwen/Qwen3-4B-Instruct-2507 | Q8 | Fits comfortably | 341.70 tok/sEstimated | 4GB (have 80GB) |
| Qwen/Qwen3-4B-Instruct-2507 | FP16 | Fits comfortably | 171.26 tok/sEstimated | 9GB (have 80GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | Fits comfortably | 573.32 tok/sEstimated | 1GB (have 80GB) |
| Qwen/Qwen-Image-Edit-2509 | Q4 | Fits comfortably | 520.27 tok/sEstimated | 4GB (have 80GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q8 | Fits comfortably | 403.51 tok/sEstimated | 1GB (have 80GB) |
| meta-llama/Llama-3.2-1B-Instruct | FP16 | Fits comfortably | 236.24 tok/sEstimated | 2GB (have 80GB) |
| openai/gpt-oss-120b | Q4 | Fits comfortably | 108.54 tok/sEstimated | 59GB (have 80GB) |
| Qwen/Qwen2.5-3B-Instruct | Q8 | Fits comfortably | 439.02 tok/sEstimated | 3GB (have 80GB) |
| Qwen/Qwen2.5-3B-Instruct | FP16 | Fits comfortably | 214.93 tok/sEstimated | 6GB (have 80GB) |
| bigscience/bloomz-560m | Q4 | Fits comfortably | 468.04 tok/sEstimated | 4GB (have 80GB) |
| bigscience/bloomz-560m | Q8 | Fits comfortably | 358.24 tok/sEstimated | 7GB (have 80GB) |
| bigscience/bloomz-560m | FP16 | Fits comfortably | 171.46 tok/sEstimated | 15GB (have 80GB) |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | Fits comfortably | 627.50 tok/sEstimated | 2GB (have 80GB) |
| tencent/HunyuanVideo-1.5 | Q4 | Fits comfortably | 446.22 tok/sEstimated | 4GB (have 80GB) |
| tencent/HunyuanVideo-1.5 | Q8 | Fits comfortably | 379.25 tok/sEstimated | 8GB (have 80GB) |
| meta-llama/Llama-3.1-70B-Instruct | Q4 | Fits comfortably | 92.80 tok/sEstimated | 34GB (have 80GB) |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | Fits comfortably | 513.93 tok/sEstimated | 2GB (have 80GB) |
| Qwen/Qwen2.5-7B-Instruct | FP16 | Fits comfortably | 187.03 tok/sEstimated | 15GB (have 80GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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