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Nvidia Nvidia H100 NVL mieten.

Name: Nvidia Nvidia H100 NVL
Brand: Nvidia
SKU: nvidia-h100-nvl

Hopper 80GB VRAM From $0.80/hr

Per hour

$0.80

Per day

$19.20

Per week

$134.40

Per month

$576

Provider spread 4 providers · up to 67% cheaper at the low end

Cheapest · $0.80/hr on io.net Median $1.16/hr Most expensive · $2.41/hr on RunPod

Price history

Daily median across providers.

Where to rent it

All providers carrying this GPU.

Provider	$/hr	Range	Offers	30d trend	Updated
io.net	$0.80/hr	$0.80/hr–$0.80/hr	0	·	about 9 hours	Launch ↗
Vast.ai	$1.13/hr	$1.13/hr–$1.13/hr	1	↓ 36%	about 1 hour	Launch ↗
NOS Nosana	$1.19/hr	$1.02/hr–$1.36/hr	2	·	about 5 hours
RunPod	$2.41/hr	$2.41/hr–$2.41/hr	1	·	about 9 hours	Launch ↗

AI models that fit See all 59 →

Run these on this GPU.

Mistral Large 2 1× · int4
Qwen 2.5 Coder 32B 1× · fp16
DeepSeek R1 Distill Qwen 32B 1× · fp16

FAQ

Frequently asked.

What AI models can I run on a Nvidia H100 NVL?

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 NVL?

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 NVL 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.

AI models

Models that run on this GPU.

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

Nvidia Nvidia H100 NVL mieten.

Daily median across providers.

All providers carrying this GPU.

Run these on this GPU.

Frequently asked.

Models that run on this GPU.

Mistral Large 2

Qwen 2.5 Coder 32B

DeepSeek R1 Distill Qwen 32B

Qwen 2.5 32B

Qwen 3 32B

LLaVA 34B

Code Llama 34B

DeepSeek Coder 33B

Yi-34B

Qwen: Qwen3.6 35B A3B

Qwen: Qwen3.5-35B-A3B

TheDrummer: Skyfall 36B V2

Holo3 35B A3b

Deepseek Coder 33B Instruct

Qwen3.6 35B A3b Fp8

Qwen3.5 122B A10b Fp8

Qwen: Qwen3.5-122B-A10B

Nvidia Nemotron 3 Super 120B A12b Fp8

Nvidia Nemotron 3 Super 120B A12b Bf16

GPT-OSS 120B

Llama 3.3 70B

Qwen 2.5 72B

DeepSeek R1 Distill Llama 70B

Llama 3.1 70B

Code Llama 70B

Hermes 3 70B

Nous: Hermes 4 70B

Qwen: Qwen2.5 VL 72B Instruct

Sao10K: Llama 3.1 70B Hanami x1

Sao10K: Llama 3.3 Euryale 70B

Magnum v4 72B

Sao10K: Llama 3.1 Euryale 70B v2.2

Sao10k: Llama 3 Euryale 70B v2.1

Meta: Llama 3 70B Instruct

Meta Llama 3.3 70B Instruct Turbo

Meta Llama 3.1 70B Instruct Turbo

nim/meta/llama-3.1-70b-instruct

nim/nvidia/llama-3.1-nemotron-70b-instruct

Cogito V1 Preview Llama 70B

Cogito V1 Preview Llama 70B Turbo

Qwen 2 (72B)

Qwen2.5 72B

Llama 3.1 70B

Meta Llama 3 70B Instruct Turbo

nim/meta/llama-3.3-70b-instruct

Llama 3.1 Nemotron 70B Instruct HF

meta-llama/Llama-3.3-70B-Instruct

Llama 3.3 70B Instruct FP8 Lora

Qwen2.5 72B Instruct

Qwen2 72B Instruct

Qwen2.5 72B Instruct Turbo

Qwen2-VL (72B) Instruct

Hermes-3-Llama-3.1-70B

L3.1-70B-Euryale-v2.2

Meta-Llama-3.1-70B-Instruct

Xiaomi: MiMo-V2.5-Pro

Writer: Palmyra X5

Morph: Morph V3 Large

Mixtral 8x22B

AI models on this GPU.