NVIDIA / discrete

NVIDIA H100 80GB PCIe

80 GB decides what fits. 2000 GB/s decides how fast it runs once it does.

Quick answer

The NVIDIA H100 80GB PCIe fits 244 of 320 sized models entirely in its 80 GB; the largest widely used one is Ling-3.0-flash (78.2 GB at Q4_K_M). Memory decides what fits; its 2000 GB/s of bandwidth decides how fast it answers.

Largest popular model that fitsLing-3.0-flash127B params · Q4_K_M · needs 78.2 GB~445 tok/s Faster than you readOpen in the calculator →
Most downloaded that fitsQwen3.8-27B28B params · Q4_K_M · needs 18.6 GB~92 tok/s Faster than you readOpen in the calculator →

At 8,192 tokens of context with the whole model in device memory. Speeds are estimates from memory bandwidth, not benchmarks run on this card.

NVIDIA H100 80GB PCIe · 80 GB · 2000 GB/s · Q4_K_M where published · 8,192 tokensSpecificationpublished dataDecode speedestimate

244 of 320 fit entirely

244 of the 320 models the engine can size fit entirely in device memory at Q4_K_M where it is published, otherwise the nearest published format, and 8,192 tokens. 2 run with some layers on system memory (32 GB assumed), and 74 do not run at all.

76%of the models the engine can size fit entirely

80GB of device memory

2000GB/s memory bandwidth

2run with system memory

74do not run at 8,192 tokens

Featured models · Q4_K_M at 8,192 tokens, including offload

Model	Needs	Verdict	Decode	Calculator
Qwen3.5-2B2.3B parameters	2.32 GB	fits	~1102 tok/s	Open →
NVIDIA-Nemotron-3-Nano-4B-BF164.0B parameters	3.46 GB	fits	~511 tok/s	Open →
Qwen3-VL-8B-Instruct8.8B parameters	7.04 GB	fits	~231 tok/s	Open →
Qwen3.5-9B9.7B parameters	7.05 GB	fits	~280 tok/s	Open →
gemma-4-12B-it12B parameters	9.54 GB	fits	~166 tok/s	Open →
gpt-oss-20b21B parameters	13.8 GB	fits	~544 tok/s	Open →
Qwen3.8-27B28B parameters	18.6 GB	fits	~92 tok/s	Open →
Qwen3.6-27B28B parameters	18.6 GB	fits	~92 tok/s	Open →
Kimi-Linear-48B-A3B-Instruct49B parameters	30.4 GB	fits	~727 tok/s	Open →
Qwen3.8-Flash-Next180B parameters	111.6 GB	needs 32.2 GB of system RAM; 32 GB assumed	does not run	Open →
DeepSeek-V4-Flash-Vision-Exp305B parameters	188.1 GB	needs 108.9 GB of system RAM; 32 GB assumed	does not run	Open →
GLM-5.3-Flash321B parameters	198.3 GB	needs 118.4 GB of system RAM; 32 GB assumed	does not run	Open →

These examples are selected from prominent labs using the catalogue’s latest Hugging Face 30-day downloads and repository-creation freshness signal, with newer releases guaranteed a place. Offloaded rows assume 32 GB of system RAM, and a speed is only shown for a row that runs. Fitting in memory is not the same as loading: whether the runtime and version you have supports each architecture and format on this machine has not been tested here. Each “Open” link carries the same model, format, context and RAM into the calculator.

Fits entirely in NVIDIA H100 80GB PCIe memory — 244 of 320 sized models

Every model that fitswhole model resident · offload off

The most demanding model that fits is Devstral-2-123B-Instruct-2512 at Q4_K_M: 78.6 GB of the 80 GB, leaving 1.35 GB spare.

Fully resident at 8,192 tokens (or the model’s own maximum, where that is shorter), offload off, at 2000 GB/s. Each row is a run of the engine for this configuration; the rows start with current, prominent releases and “fits” is memory, not a tested runtime. Older or less prominent models remain available through this search and “Show all”.

Find a model in this list

Showing 40 of 244 models that fit.

Model	Format	Needs	Spare	Decode	Downloads / 30d	Calculator
Qwen3.8-27BQwen · 28B params	Q4_K_M	18.6 GB	61.4 GB	~92 tok/s	6.9M	Open →
gemma-4-26B-A4B-itGoogle · 26B params	Q4_K_M	16.8 GB	63.2 GB	~511 tok/s	13M	Open →
gemma-4-31B-itGoogle · 31B params	Q4_K_M	22.2 GB	57.8 GB	~66 tok/s	9.9M	Open →
Qwen3.5-9BQwen · 9.7B params	Q4_K_M	7.05 GB	73.0 GB	~280 tok/s	9M	Open →
Qwen3.5-4BQwen · 4.7B params	Q4_K_M	3.98 GB	76.0 GB	~504 tok/s	7.8M	Open →
Qwen3.6-35B-A3BQwen · 36B params	Q4_K_M	23.1 GB	56.9 GB	~734 tok/s	3.3M	Open →
gemma-4-12B-itGoogle · 12B params	Q4_K_M	9.54 GB	70.5 GB	~166 tok/s	1.9M	Open →
Qwen3.6-27BQwen · 28B params	Q4_K_M	18.6 GB	61.4 GB	~92 tok/s	2.5M	Open →
Qwen3.5-2BQwen · 2.3B params	Q4_K_M	2.32 GB	77.7 GB	~1102 tok/s	4.9M	Open →
gemma-4-E4B-itGoogle · 8.0B params	Q4_K_M	5.86 GB	74.1 GB	~450 tok/s	4.4M	Open →
NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16NVIDIA · 32B params	Q4_K_M	20.3 GB	59.7 GB	~70 tok/s	530K	Open →
NVIDIA-Nemotron-3-Nano-4B-BF16NVIDIA · 4.0B params	Q4_K_M	3.46 GB	76.5 GB	~511 tok/s	3.4M	Open →
gemma-4-E2B-itGoogle · 5.1B params	Q4_K_M	4.02 GB	76.0 GB	~1188 tok/s	3M	Open →
Qwen3.5-0.8BQwen · 873M params	Q4_K_M	1.46 GB	78.5 GB	~2415 tok/s	2.6M	Open →
Qwen3-VL-8B-InstructQwen · 8.8B params	Q4_K_M	7.04 GB	73.0 GB	~231 tok/s	14.6M	Open →
Qwen3.5-27BQwen · 28B params	Q4_K_M	18.6 GB	61.4 GB	~92 tok/s	1.9M	Open →
North-Micro-Vision-InstructCohereLabs · 2.5B params	Q4_K_M	2.99 GB	77.0 GB	~725 tok/s	180.7K	Open →
Qwen3.5-35B-A3BQwen · 36B params	Q4_K_M	23.1 GB	56.9 GB	~734 tok/s	1.6M	Open →
NVIDIA-Nemotron-3-Super-120B-A12B-BF16NVIDIA · 124B params	Q4_K_M	76.9 GB	3.08 GB	~18 tok/sAbout reading pace	1.2M	Open →
granite-4.2-8bIBM · 8.8B params	Q4_K_M	7.49 GB	72.5 GB	~210 tok/s	128.4K	Open →
GLM-4.7-Flashzai-org · 31B params	Q4_K_M	20.4 GB	59.6 GB	~69 tok/s	1.8M	Open →
granite-4.1-3bIBM · 3.4B params	Q4_K_M	3.72 GB	76.3 GB	~491 tok/s	521.8K	Open →
LFM2.5-2.6BLiquidAI · 2.7B params	Q4_K_M	2.61 GB	77.4 GB	~787 tok/s	108.1K	Open →
Qwen3-VL-4B-InstructQwen · 4.4B params	Q4_K_M	4.72 GB	75.3 GB	~367 tok/s	3.5M	Open →
granite-4.1-30bIBM · 29B params	Q4_K_M	20.4 GB	59.6 GB	~69 tok/s	301.5K	Open →
Qwen3.5-122B-A10BQwen · 125B params	Q4_K_M	77.9 GB	2.05 GB	~269 tok/s	512.6K	Open →
Qwen3-VL-2B-InstructQwen · 2.1B params	Q4_K_M	3.05 GB	77.0 GB	~666 tok/s	2.8M	Open →
granite-4.2-3bIBM · 3.7B params	Q4_K_M	3.72 GB	76.3 GB	~491 tok/s	50.6K	Open →
LFM2.5-230MLiquidAI · 230M params	Q4_K_M	1.05 GB	78.9 GB	~5489 tok/s	88.6K	Open →
Qwen3-0.6BQwen · 752M params	Q4_K_M	2.22 GB	77.8 GB	~1021 tok/s	29.7M	Open →
Qwen3-Coder-NextQwen · 80B params	Q4_K_M	50.0 GB	30.0 GB	~488 tok/s	596.3K	Open →
gpt-oss-20bOpenAI · 21B params	Q4_K_M	13.8 GB	66.2 GB	~544 tok/s	6.6M	Open →
granite-4.2-30bIBM · 29B params	Q4_K_M	20.7 GB	59.3 GB	~69 tok/s	35.8K	Open →
granite-4.1-8bIBM · 8.8B params	Q4_K_M	7.49 GB	72.5 GB	~210 tok/s	179.3K	Open →
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16NVIDIA · 32B params	Q4_K_M	20.3 GB	59.7 GB	~70 tok/s	875.2K	Open →
gpt-oss-120bOpenAI · 117B params	Q4_K_M	71.9 GB	8.06 GB	~383 tok/s	4.5M	Open →
LFM2.5-VL-3BLiquidAI · 3.1B params	Q4_K_M	2.85 GB	77.1 GB	~787 tok/s	26.7K	Open →
MiMo-V2.6-Distill-Qwen-9BNEWXiaomiMiMo · 9.4B params	Q4_K_M	6.90 GB	73.1 GB	~280 tok/s	14.6K	Open →
Qwen3-4B-Instruct-2507Qwen · 4.0B params	Q4_K_M	4.72 GB	75.3 GB	~367 tok/s	3.7M	Open →
Ling-3.0-tinyinclusionAI · 7.9B params	Q4_K_M	5.85 GB	74.1 GB	~1586 tok/s	17.7K	Open →

Sized at the format most people actually download, not at FP16. The catalogue ordered by downloads →

Rent it instead

Rent an H100 PCIe by the hour

What the NVIDIA H100 80GB PCIe costs on Vast.ai and RunPod right now, per machine, from one card to eight. Every price is read from the provider's own API.

Prices · 18:30 UTC, 22 Sept

1× H100 PCIe · 80 GB

$2.40per hour

Vast.ai · Marketplace · 98.7% reliableRent on Vast.ai

2× H100 PCIe · 160 GB

$4.79per hour

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4× H100 PCIe · 320 GB

$9.59per hour

Vast.ai · Marketplace · 98.3% reliableRent on Vast.ai

8× H100 PCIe · 640 GB

$19.16per hour

Vast.ai · Marketplace · 98.3% reliableRent on Vast.ai

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Hours of use per day4 h

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At 4 h a day, renting costs $3,504.00 a year.

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The NVIDIA H100 80GB PCIe, model by model

One page per model: whether it fits this device, at which formats, and how fast.

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Under the hood

The specification behind every figure

What the manufacturer publishes for this device, and the pages it was read from.

Manufacturer specification

Memory scope	dedicated
Capacity	80 GB
Published options	80 GB
Bandwidth	2000 GB/s
Memory type	HBM2e
Bus width	5120-bit
FP32 peak	48 TFLOPS
Dense matrix peak	Not published without sparsity
Max thermal design power (TDP)	350 W

Source ledger

NVIDIA H100 PCIe Product Brief ↗

Retrieved 2026-08-30

NVIDIA H100 Tensor Core GPU Datasheet ↗

Retrieved 2026-08-30

NVIDIA H100 Tensor Core GPU Architecture ↗

Retrieved 2026-08-30

Caveats

The architecture whitepaper labels its H100 bandwidth table not finalized; the later product brief's 2TB/s value is canonical. See conflicts.
No dense matrix throughput is catalogued for this device: NVIDIA's current H100 pages publish FP16 Tensor Core throughput only with sparsity. Time to first token is withheld rather than priced against a sparsity figure.

Published capacity is a hardware ceiling, not guaranteed free runtime memory. The calculator shows the runtime reserve separately rather than folding it into a single number.

Run the diagnostic on the NVIDIA H100 80GB PCIe →