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Nvidia logo Thuê Nvidia H100 SXM.

Hopper 80GB VRAM From $1.55/hr
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$1.55
Per day
$37.20
Per week
$260.40
Per month
$1116
AI models

Models that run on this GPU.

GPU-count + quantization recommendations covering fine-tuning, inference, and run-it-yourself scenarios on the Nvidia H100 SXM.

Mistral Large 2

by Mistral AI
INT4
Required GPUs
1× Nvidia H100 SXM
Total VRAM
80 GB
Parameters
123B
Context
128K tokens

Qwen3.5 122B A10b Fp8

by Alibaba (Qwen Team)
INT4
Required GPUs
1× Nvidia H100 SXM
Total VRAM
80 GB
Parameters
122B
Context
262K tokens

Qwen: Qwen3.5-122B-A10B

by Alibaba (Qwen Team)
INT4
Required GPUs
1× Nvidia H100 SXM
Total VRAM
80 GB
Parameters
122B
Context
262K tokens

Nvidia Nemotron 3 Super 120B A12b Fp8

by Nvidia
INT4
Required GPUs
1× Nvidia H100 SXM
Total VRAM
80 GB
Parameters
120B
Context
262K tokens

Nvidia Nemotron 3 Super 120B A12b Bf16

by Nvidia
INT4
Required GPUs
1× Nvidia H100 SXM
Total VRAM
80 GB
Parameters
120B
Context
262K tokens

GPT-OSS 120B

by OpenAI
INT4
Required GPUs
1× Nvidia H100 SXM
Total VRAM
80 GB
Parameters
120B (5B active)
Context
128K tokens
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FAQ

AI models on this GPU.

What AI models can I run on a Nvidia H100 SXM?
The grid above lists every open-weights model with a recommended GPU configuration for this card. Each row tells you the minimum GPU count and the quantization level (FP16, FP8, INT8, INT4) needed to load the model in 80GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia H100 SXM?
Rule of thumb: a model needs roughly (parameters × bytes-per-weight × 1.2) of VRAM to load, plus headroom for the KV cache during inference. FP16 = 2 bytes/weight, FP8/INT8 = 1 byte, INT4 = 0.5 bytes. A 70B model at FP16 needs ~168GB; at INT4 it drops to ~42GB and fits a single high-VRAM card.
How does quantization (FP16 vs FP8 vs INT4) affect what fits?
Lower-precision quantization shrinks the memory footprint nearly linearly with the bit count. The trade-off is output quality: FP16 is the reference, FP8 is usually indistinguishable for most prompts, INT8 introduces small quality losses, INT4 is noticeably degraded on reasoning-heavy tasks but fine for chat. The badge on each row tells you which level the recommendation assumes.
Can I fine-tune on the Nvidia H100 SXM or only do inference?
Fine-tuning needs 4–8× more VRAM than inference at the same model size — gradients, optimizer state, and activations all live in memory. LoRA / QLoRA cut that overhead dramatically (often 4–10×). The notes column flags whether a row is an inference-only recommendation or includes a fine-tuning path.
Where do these GPU-count recommendations come from?
We curate them from official model cards, community benchmark threads (r/LocalLLaMA, HuggingFace forum), and known-good configurations published by the model makers. Each recommendation has been verified to load at the stated quantization on the listed GPU count — though throughput and context-length still vary by workload.