NVIDIA · Hopper

Best AI Models for NVIDIA H100 PCIe (80 GB)

Runs everything up to 120B MoE well.

80 GB HBM2e2039 GB/s757 TFLOPS FP16350 WHopper 2022

What LLMs can the H100 PCIe run?

#	Model	Speed	Quant · VRAM
1	Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 76.1 t/s	76.1 tok/s	Q4_K_M · 17.9 GB
2	Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 76.1 t/s	76.1 tok/s	Q4_K_M · 17.9 GB
3	Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 65.0 t/s	65.0 tok/s	Q4_K_M · 21.7 GB
4	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 269 t/s	269 tok/s	Q4_K_M · 16.5 GB
5	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 253 t/s	253 tok/s	Q4_K_M · 19.3 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 76.1 t/s	76.1 tok/s	Q4_K_M · 17.9 GB
2	Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 65.0 t/s	65.0 tok/s	Q4_K_M · 21.7 GB
3	Qwen3.6 35B A3BAlibaba Q4_K_M · 22.0 GB · 228 t/s	228 tok/s	Q4_K_M · 22.0 GB
4	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 269 t/s	269 tok/s	Q4_K_M · 16.5 GB
5	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 253 t/s	253 tok/s	Q4_K_M · 19.3 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 76.1 t/s	76.1 tok/s	Q4_K_M · 17.9 GB
2	Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 65.0 t/s	65.0 tok/s	Q4_K_M · 21.7 GB
3	Qwen3.5 27BAlibaba Q4_K_M · 17.9 GB · 76.1 t/s	76.1 tok/s	Q4_K_M · 17.9 GB
4	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 269 t/s	269 tok/s	Q4_K_M · 16.5 GB
5	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 253 t/s	253 tok/s	Q4_K_M · 19.3 GB

#	Model	Speed	Math Score	Quant · VRAM
1	Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 76.1 t/s	76.1 tok/s		Q4_K_M · 17.9 GB
2	Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 76.1 t/s	76.1 tok/s		Q4_K_M · 17.9 GB
3	Qwen3.6 35B A3BAlibaba Q4_K_M · 22.0 GB · 228 t/s	228 tok/s		Q4_K_M · 22.0 GB
4	Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 65.0 t/s	65.0 tok/s	overall	Q4_K_M · 21.7 GB
5	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 253 t/s	253 tok/s		Q4_K_M · 19.3 GB

#	Model	Speed ↓	Quant · VRAM
1	Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 407 t/s	407 tok/s	Q4_K_M · 3.4 GB
2	GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 305 t/s	305 tok/s	Q4_K_M · 13.1 GB
3	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 269 t/s	269 tok/s	Q4_K_M · 16.5 GB
4	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 253 t/s	253 tok/s	Q4_K_M · 19.3 GB
5	Qwen3 30B A3B Thinking 2507Alibaba Q4_K_M · 19.4 GB · 239 t/s	239 tok/s	Q4_K_M · 19.4 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 76.1 t/s	76.1 tok/s	Q4_K_M · 17.9 GB
2	Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 76.1 t/s	76.1 tok/s	Q4_K_M · 17.9 GB
3	Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 65.0 t/s	65.0 tok/s	Q4_K_M · 21.7 GB
4	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 269 t/s	269 tok/s	Q4_K_M · 16.5 GB
5	JEV 27B VLautotrust Q4_K_M · 17.9 GB · 76.1 t/s	76.1 tok/s	Q4_K_M · 17.9 GB

See all 383 models on the H100 PCIe →Best quant that fits · speeds estimated on this card

About the H100 PCIe

The H100 PCIe runs everything up to 120B MoE well. Hopper generation, released 2022.

GenerationHopperreleased 2022

H100 PCIe strengths and weaknesses

Strengths

Runs 120B MoE models: GPT OSS 120B at 218 tok/s in 70.8 GB
Room for long context: Llama 3.3 70B Instruct at Q4 uses 51.3 GB, leaving 28.7 GB free
Reads long prompts fast: A 4K-token prompt on Qwen3.8 27B starts answering in 669 ms

H100 PCIe tokens per second by model size

Output speed depends on memory bandwidth (2039 GB/s).

9BQwen3.5 9B · Q4_K_M · 6.6 GB
208tok/s
14BQwen3 14B · Q4_K_M · 11.9 GB
140tok/s
27–31BQwen3.8 27B · Q4_K_M · 17.9 GB
76.1tok/s
35B MoEQwen3.6 35B A3B · Q4_K_M · 22.0 GB
228tok/s
70BLlama 3.3 70B Instruct · Q4_K_M · 51.3 GB
28.5tok/s
120B MoEGPT OSS 120B · Q4_K_M · 70.8 GB
218tok/s

Estimated speeds: Q4_K_M, 4K context, one user. How we estimate

All 383 models on the H100 PCIe

Featured only

Best quant that fits, ordered by llmrun Score. Fit grades how well it runs here, S best. How ranking works

LLM models compatible with NVIDIA H100 PCIe — ranked by Score, recency and Fit
Model	Quant	VRAM	Speed	Context	Fit
GPT OSS 120B116.8BChatFunctions Q4_K_M·217.5 t/s	Q4_K_M	70.8 GB89%	217.5 t/s	131K	S88, Runs great
Qwen3.5 9B9.7BVisionFunctions Q4_K_M·208.4 t/s	Q4_K_M	6.6 GB8%	208.4 t/s	262K	S100, Runs great
GLM 4.6V107.7BVisionFunctions Q4_K_M·110.6 t/s	Q4_K_M	66.0 GB82%	110.6 t/s	131K	S90, Runs great
Ling 3.0 Tiny7.9BChatFunctions Q4_K_M·493.8 t/s	Q4_K_M	5.5 GB7%	493.8 t/s	131K	S100, Runs great
Qwen3.5 4B4.7BVisionFunctions Q4_K_M·406.5 t/s	Q4_K_M	3.4 GB4%	406.5 t/s	262K	S100, Runs great
Qwen3.6 27B27.8BVisionFunctions Q4_K_M·76.1 t/s	Q4_K_M	17.9 GB22%	76.1 t/s	262K	S87, Runs great
Hy Memory 30B A3B30.1BChatFunctions Q4_K_M·248.2 t/s	Q4_K_M	19.1 GB24%	248.2 t/s	262K	S100, Runs great
MiniCPM5 2B2.5BChatFunctions Q4_K_M·697.6 t/s	Q4_K_M	2.5 GB3%	697.6 t/s	131K	S100, Runs great
Nanbeige4.3 33B33.1BChatFunctions Q4_K_M·64.3 t/s	Q4_K_M	23.7 GB30%	64.3 t/s	524K	A84, Runs well
Ornith 1.0 35B35.1BChatFunctions Q4_K_M·250.1 t/s	Q4_K_M	21.5 GB27%	250.1 t/s	262K	S100, Runs great
Gemma 4 12B IT12.0BChatFunctions Q4_K_M·161.0 t/s	Q4_K_M	9.0 GB11%	161.0 t/s	262K	S97, Runs great
Spark X2.6 4B4.1BChatFunctions Q4_K_M·447.8 t/s	Q4_K_M	4.3 GB5%	447.8 t/s	1049K	S100, Runs great
NVIDIA Nemotron 3.5 Bolt 74B A9B BF1674.9BVisionFunctions Q4_K_M·140.4 t/s	Q4_K_M	54.2 GB68%	140.4 t/s	—	S96, Runs great
NVIDIA Nemotron 3.5 Bolt E4B BF168.0BChatFunctions Q4_K_M·249.1 t/s	Q4_K_M	5.6 GB7%	249.1 t/s	66K	S100, Runs great
Qwen3 Next 80B A3B Instruct81.3BChatFunctions Q4_K_M·193.7 t/s	Q4_K_M	49.3 GB62%	193.7 t/s	262K	S99, Runs great

Rent NVIDIA H100 PCIe in the cloud

Hourly rate on demand for one H100 PCIe.

Vast.aiMarketplace
from$2.76/hr
Deploy

Cost per million tokens

Output-token cost on an H100 PCIe rented at $2.76/hr, one request at a time. Batch several users on the same card and the cost per token drops several-fold.

Model	Quant	Speed	Per 1M output
NVIDIA Nemotron 3.5 Lightning 30B A3B BF16	Q4_K_M	253 tok/s	$3.02$0.015 input
GLM 4.7 Flash	Q4_K_M	209 tok/s	$3.67$0.021 input
Qwen3.8 27B	Q4_K_M	76 tok/s	$10.06$0.12 input
Muse Glimmer 30B	Q4_K_M	72 tok/s	$10.59$0.13 input
Gemma 4 31B IT	Q4_K_M	65 tok/s	$11.78$0.14 input

H100 PCIe specs, power and PSU

Specs

Architecture: Hopper
VRAM: 80 GB HBM2e
Memory bandwidth: 2039.0 GB/s
FP16 (dense): 756.50 TFLOPS
CUDA cores: 14,592
Tensor cores: 456
Compute capability: 9.0
TDP: 350 W
Released: 2022-09-01

H100 PCIe FAQ

What is the largest model the H100 PCIe can run?

Mistral Medium 3.5 128B (127.7B) is the largest at 4-bit or better: at Q4_K_S it uses 78.0 GB of 80 GB, and runs at about 18.2 tok/s.

Can the H100 PCIe run Qwen3.8 27B?

Yes. Qwen3.8 27B fits at Q4_K_M, using 17.9 GB of 80 GB, and it is the best model that runs well on this card.

How many tokens per second does the H100 PCIe give?

About 76.1 tok/s on Qwen3.8 27B at Q4_K_M, with the first token after 0.7 s on a 4K-token prompt.

What power supply does the H100 PCIe need?

Plan a 700 W PSU (2× the 350 W TDP).

Is 80 GB enough for coding and agents?

Yes for coding: Qwen3.8 27B, the best of the 12 coding models that fit, runs at about 76 tok/s. For agents, not yet: no agent-scored model fits in less than 96 GB.

Best coding LLMs 48 GB VRAM GPUs with 80 GB or more