NVIDIA H100 80GB
The NVIDIA H100 80GB has 80 GB VRAM and 3350 GB/s memory bandwidth. It can run 65 of our 97 tracked models natively in VRAM at 8k context.
With 80 GB HBM3, the NVIDIA H100 80GB is a datacenter-tier GPU that can run 65 models natively. Runs 70B at Q4 native with headroom to spare in 80GB, though the largest frontier-class models need a bigger card or a multi-GPU pool. ~50-80 t/s for 7B, ~20-30 t/s for 70B Q4.
The NVIDIA H100 80GB is the Hopper-generation datacenter GPU with 80GB HBM3 at an industry-leading 3,350 GB/s bandwidth. With FP8 Transformer Engine and 4th-gen NVLink, it delivers the fastest single-GPU LLM inference available: running 70B models at Q8 and 405B models across multi-GPU NVLink clusters. The de facto standard for production LLM serving.
NVIDIA H100 80GB: March 2022 Hopper architecture with 80GB HBM3 at 3350 GB/s, datacenter flagship.
Runs 70B at Q4 native with headroom to spare in 80GB, though the largest frontier-class models need a bigger card or a multi-GPU pool. ~50-80 t/s for 7B, ~20-30 t/s for 70B Q4.
Best-in-class throughput with vLLM and TensorRT-LLM. Excellent multi-GPU NVLink scaling. Cloud-only for most users.
| Vendor | NVIDIA |
| Architecture | Hopper |
| VRAM | 80 GB |
| Memory type | HBM3 |
| Memory bandwidth | 3350 GB/s |
| Compute backend | CUDA |
| Tier | Datacenter |
| Released | 2022 |
| Models (native) | 65 / 97 |
| Models (offload) | 6 / 97 |
Cloud GPU Rental
Don't want to buy a NVIDIA H100 80GB? RunPod is a cloud GPU rental service: rent one by the hour instead, no contract, no upfront hardware cost.
Pay by the hour · no contract · pods start in about a minute.
Rent a NVIDIA H100 80GB on RunPod ↗ (+$5 signup credit)Affiliate link: CanItRun may earn a commission. Doesn't affect the fit calculation above.
Popular models for this GPU
Models this GPU runs natively in VRAM (65)
- Mixtral 8x22B Instruct v0.1141B · MMLU-Pro 40.0Q2_K · ~42.4 t/s
- Mistral Medium 3.5 128B128B · MMLU-Pro N/AQ3_K_M · ~33.7 t/s
- Qwen 3.5 122B-A10B (MoE)122B · MMLU-Pro 86.7Q3_K_M · ~134.1 t/s
- Nemotron 3 Super 120B120B · MMLU-Pro 83.7Q3_K_M · ~109 t/s
- GPT-OSS 120B117B · MMLU-Pro 80.7Q3_K_M · ~256.7 t/s
Show 60 more
- Llama 4 Scout 109B109B · MMLU-Pro 74.3Q3_K_M · ~72.7 t/s
- GLM-4.5 Air 106B106B · MMLU-Pro 81.4Q4_K_M · ~84.1 t/s
- GLM-4.6V 106B106B · MMLU-Pro 79.9Q4_K_M · ~84.1 t/s
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1Q6_K · ~35.2 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9Q6_K · ~36.2 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0Q6_K · ~36.2 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4Q6_K · ~36.2 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7Q8_0 · ~46.5 t/s
- Command-R 35B35B · MMLU-Pro 33.0Q8_0 · ~45.4 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3Q8_0 · ~201.7 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2Q8_0 · ~55.3 t/s
- Ornith 1.5 35B-A3B (MoE)35B · MMLU-Pro N/AQ8_0 · ~201.7 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0Q8_0 · ~56.4 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5BF16 · ~32.5 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0BF16 · ~32.4 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 62.3BF16 · ~32.4 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0BF16 · ~32.4 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3BF16 · ~106.6 t/s
- Gemma 4 31B30.7B · MMLU-Pro 85.2BF16 · ~34.6 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5BF16 · ~104.7 t/s
- Nemotron 3.5 Lightning 30B-A3B30B · MMLU-Pro 81.6BF16 · ~108.6 t/s
- Muse Glimmer 30B27.8B · MMLU-Pro N/ABF16 · ~39 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0BF16 · ~37.9 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5BF16 · ~39.2 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2BF16 · ~39.9 t/s
- UI-Mate 27B27B · MMLU-Pro ~86.2BF16 · ~39.9 t/s
- Bonsai 27B27B · MMLU-Pro 81.5Ternary (Q2_0) · ~247.1 t/s
- Qwen 3.8 27B27B · MMLU-Pro N/ABF16 · ~39.9 t/s
- Gemma 4 26B (MoE)25.2B · MMLU-Pro 82.6BF16 · ~84.7 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8BF16 · ~44.1 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2BF16 · ~47.1 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9BF16 · ~90 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0BF16 · ~70.4 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7BF16 · ~70.2 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4BF16 · ~74.2 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6BF16 · ~84.6 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6BF16 · ~85.5 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2BF16 · ~80 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0BF16 · ~102.6 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5BF16 · ~119.2 t/s
- Ornith 1.5 9B9B · MMLU-Pro N/ABF16 · ~119.2 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3BF16 · ~127.5 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0BF16 · ~127.5 t/s
- Qwen3 8B8B · MMLU-Pro 56.7BF16 · ~126.5 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3BF16 · ~139 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0BF16 · ~139.8 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6BF16 · ~256.1 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4BF16 · ~241.8 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4BF16 · ~201.2 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3BF16 · ~251 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0BF16 · ~296.7 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4BF16 · ~334.9 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8BF16 · ~358.6 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0BF16 · ~494.6 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0BF16 · ~434.6 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8BF16 · ~673.1 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5BF16 · ~792.3 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7BF16 · ~935.7 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0BF16 · ~1978.4 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0BF16 · ~2062.9 t/s
Models that fit with CPU offload (6)
These use system RAM for layers that don't fit in VRAM, so expect much slower inference.
- DeepSeek V4 Flash 0731 284B284B · MMLU-Pro N/AUD-IQ1_S · ~21.9 t/s
- MiniMax M2.5 229B229B · MMLU-Pro 84.8Q2_K · ~10.6 t/s
- MiniMax M2.7 229B229B · MMLU-Pro 86.0Q2_K · ~10.6 t/s
- Step 3.7 Flash198B · MMLU-Pro N/AQ2_K · ~68.3 t/s
- Step 3.5 Flash196.81B · MMLU-Pro 84.4Q2_K · ~90.4 t/s
- Qwen3.8-Flash-Next180B · MMLU-Pro N/AQ3_K_M · ~19 t/s
Too large for this GPU (26)
- Llama 3.1 405B Instruct
- DeepSeek V3 671B
- DeepSeek R1 671B
- Llama 4 Maverick 400B
- Qwen3 235B-A22B (MoE)
- MiniMax M1 456B
- GLM-4.5 355B
- GLM-4.6 355B
- GLM-4.7 358B
- GLM-5 744B
- Kimi K2.6
- GLM-5.1 754B
- DeepSeek V4 Pro 1.6T
- DeepSeek V4 Flash 284B
- GLM-5.2 753B
- Nemotron 3 Ultra 550B-A55B
- MiMo V2.5 Pro
- Kimi K2.5
- MiniMax M3
- Inkling
- Kimi K3
- Qwen3.8 2.4T-A95B
- DeepSeek V4 Pro 0813 1.6T
- Ornith 1.5 397B (MoE)
- GLM-5.3 753B
- GLM-5.3-Flash 320B
Compare NVIDIA H100 80GB with other GPUs
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Frequently asked questions
- How much VRAM does the NVIDIA H100 80GB have?
- The NVIDIA H100 80GB has 80 GB of HBM3 with 3350 GB/s memory bandwidth.
- What is the NVIDIA H100 80GB best for?
- With 80 GB of VRAM, the NVIDIA H100 80GB is a server-class GPU that runs 70B-class dense models and large MoE models natively, with plenty of room for long context.
- What LLMs can the NVIDIA H100 80GB run locally?
- The NVIDIA H100 80GB can run 65 of the 97 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Qwen 3.8 27B at BF16, Ornith 1.5 35B-A3B (MoE) at Q8_0, Qwen 3.5 122B-A10B (MoE) at Q3_K_M.
- Can the NVIDIA H100 80GB run Gemma 4 31B?
- Yes. The NVIDIA H100 80GB runs Gemma 4 31B natively in VRAM at BF16 quantization, achieving approximately 34.6 tokens per second.
- Can the NVIDIA H100 80GB run Qwen 3.6 27B?
- Yes. The NVIDIA H100 80GB runs Qwen 3.6 27B natively in VRAM at BF16 quantization, achieving approximately 39.9 tokens per second.
- Can the NVIDIA H100 80GB run Qwen3 8B?
- Yes. The NVIDIA H100 80GB runs Qwen3 8B natively in VRAM at BF16 quantization, achieving approximately 126.5 tokens per second.
- Can I rent the NVIDIA H100 80GB instead of buying it?
- Yes: RunPod and similar cloud GPU providers let you rent NVIDIA H100 80GB instances by the hour, with no long-term contract. This is often cheaper than buying if you only need it occasionally, and lets you try the GPU before committing to a purchase.