NVIDIA / discrete

NVIDIA A100 80GB PCIe

80 GB decides what fits. 1935 GB/s decides how fast it runs once it does.

Quick answer

The NVIDIA A100 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 1935 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~430 tok/s Faster than you readOpen in the calculator →
Most downloaded that fitsQwen3.8-27B28B params · Q4_K_M · needs 18.6 GB~89 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 A100 80GB PCIe · 80 GB · 1935 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

1935GB/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	~1067 tok/s	Open →
NVIDIA-Nemotron-3-Nano-4B-BF164.0B parameters	3.46 GB	fits	~494 tok/s	Open →
Qwen3-VL-8B-Instruct8.8B parameters	7.04 GB	fits	~224 tok/s	Open →
Qwen3.5-9B9.7B parameters	7.05 GB	fits	~271 tok/s	Open →
gemma-4-12B-it12B parameters	9.54 GB	fits	~161 tok/s	Open →
gpt-oss-20b21B parameters	13.8 GB	fits	~526 tok/s	Open →
Qwen3.8-27B28B parameters	18.6 GB	fits	~89 tok/s	Open →
Qwen3.6-27B28B parameters	18.6 GB	fits	~89 tok/s	Open →
Kimi-Linear-48B-A3B-Instruct49B parameters	30.4 GB	fits	~704 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 A100 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 1935 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	~89 tok/s	6.9M	Open →
gemma-4-26B-A4B-itGoogle · 26B params	Q4_K_M	16.8 GB	63.2 GB	~494 tok/s	13M	Open →
gemma-4-31B-itGoogle · 31B params	Q4_K_M	22.2 GB	57.8 GB	~64 tok/s	9.9M	Open →
Qwen3.5-9BQwen · 9.7B params	Q4_K_M	7.05 GB	73.0 GB	~271 tok/s	9M	Open →
Qwen3.5-4BQwen · 4.7B params	Q4_K_M	3.98 GB	76.0 GB	~487 tok/s	7.8M	Open →
Qwen3.6-35B-A3BQwen · 36B params	Q4_K_M	23.1 GB	56.9 GB	~710 tok/s	3.3M	Open →
gemma-4-12B-itGoogle · 12B params	Q4_K_M	9.54 GB	70.5 GB	~161 tok/s	1.9M	Open →
Qwen3.6-27BQwen · 28B params	Q4_K_M	18.6 GB	61.4 GB	~89 tok/s	2.5M	Open →
Qwen3.5-2BQwen · 2.3B params	Q4_K_M	2.32 GB	77.7 GB	~1067 tok/s	4.9M	Open →
gemma-4-E4B-itGoogle · 8.0B params	Q4_K_M	5.86 GB	74.1 GB	~435 tok/s	4.4M	Open →
NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16NVIDIA · 32B params	Q4_K_M	20.3 GB	59.7 GB	~67 tok/s	530K	Open →
NVIDIA-Nemotron-3-Nano-4B-BF16NVIDIA · 4.0B params	Q4_K_M	3.46 GB	76.5 GB	~494 tok/s	3.4M	Open →
gemma-4-E2B-itGoogle · 5.1B params	Q4_K_M	4.02 GB	76.0 GB	~1149 tok/s	3M	Open →
Qwen3.5-0.8BQwen · 873M params	Q4_K_M	1.46 GB	78.5 GB	~2337 tok/s	2.6M	Open →
Qwen3-VL-8B-InstructQwen · 8.8B params	Q4_K_M	7.04 GB	73.0 GB	~224 tok/s	14.6M	Open →
Qwen3.5-27BQwen · 28B params	Q4_K_M	18.6 GB	61.4 GB	~89 tok/s	1.9M	Open →
North-Micro-Vision-InstructCohereLabs · 2.5B params	Q4_K_M	2.99 GB	77.0 GB	~701 tok/s	180.7K	Open →
Qwen3.5-35B-A3BQwen · 36B params	Q4_K_M	23.1 GB	56.9 GB	~710 tok/s	1.6M	Open →
NVIDIA-Nemotron-3-Super-120B-A12B-BF16NVIDIA · 124B params	Q4_K_M	76.9 GB	3.08 GB	~17 tok/sAbout reading pace	1.2M	Open →
granite-4.2-8bIBM · 8.8B params	Q4_K_M	7.49 GB	72.5 GB	~204 tok/s	128.4K	Open →
GLM-4.7-Flashzai-org · 31B params	Q4_K_M	20.4 GB	59.6 GB	~67 tok/s	1.8M	Open →
granite-4.1-3bIBM · 3.4B params	Q4_K_M	3.72 GB	76.3 GB	~475 tok/s	521.8K	Open →
LFM2.5-2.6BLiquidAI · 2.7B params	Q4_K_M	2.61 GB	77.4 GB	~762 tok/s	108.1K	Open →
Qwen3-VL-4B-InstructQwen · 4.4B params	Q4_K_M	4.72 GB	75.3 GB	~355 tok/s	3.5M	Open →
granite-4.1-30bIBM · 29B params	Q4_K_M	20.4 GB	59.6 GB	~67 tok/s	301.5K	Open →
Qwen3.5-122B-A10BQwen · 125B params	Q4_K_M	77.9 GB	2.05 GB	~260 tok/s	512.6K	Open →
Qwen3-VL-2B-InstructQwen · 2.1B params	Q4_K_M	3.05 GB	77.0 GB	~644 tok/s	2.8M	Open →
granite-4.2-3bIBM · 3.7B params	Q4_K_M	3.72 GB	76.3 GB	~475 tok/s	50.6K	Open →
LFM2.5-230MLiquidAI · 230M params	Q4_K_M	1.05 GB	78.9 GB	~5311 tok/s	88.6K	Open →
Qwen3-0.6BQwen · 752M params	Q4_K_M	2.22 GB	77.8 GB	~988 tok/s	29.7M	Open →
Qwen3-Coder-NextQwen · 80B params	Q4_K_M	50.0 GB	30.0 GB	~472 tok/s	596.3K	Open →
gpt-oss-20bOpenAI · 21B params	Q4_K_M	13.8 GB	66.2 GB	~526 tok/s	6.6M	Open →
granite-4.2-30bIBM · 29B params	Q4_K_M	20.7 GB	59.3 GB	~67 tok/s	35.8K	Open →
granite-4.1-8bIBM · 8.8B params	Q4_K_M	7.49 GB	72.5 GB	~204 tok/s	179.3K	Open →
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16NVIDIA · 32B params	Q4_K_M	20.3 GB	59.7 GB	~67 tok/s	875.2K	Open →
gpt-oss-120bOpenAI · 117B params	Q4_K_M	71.9 GB	8.06 GB	~370 tok/s	4.5M	Open →
LFM2.5-VL-3BLiquidAI · 3.1B params	Q4_K_M	2.85 GB	77.1 GB	~762 tok/s	26.7K	Open →
MiMo-V2.6-Distill-Qwen-9BNEWXiaomiMiMo · 9.4B params	Q4_K_M	6.90 GB	73.1 GB	~271 tok/s	14.6K	Open →
Qwen3-4B-Instruct-2507Qwen · 4.0B params	Q4_K_M	4.72 GB	75.3 GB	~355 tok/s	3.7M	Open →
Ling-3.0-tinyinclusionAI · 7.9B params	Q4_K_M	5.85 GB	74.1 GB	~1534 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 A100 80GB PCIe by the hour

What the NVIDIA A100 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× A100 80GB PCIe · 80 GB

$1.19per hour

RunPod · Community CloudRunPod $1.59/h Secure CloudRent on RunPod

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Hours of use per day4 h

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Watts start at the NVIDIA A100 80GB PCIe’s published board power (300 W), an upper bound: decoding rarely holds a card at its limit. Rent starts at the cheapest live price (RunPod). The electricity price is a placeholder — put yours in.

At 4 h a day, renting costs $1,737.40 a year.

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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	1935 GB/s
Memory type	HBM2e
Bus width	5120-bit
FP32 peak	19.5 TFLOPS
Dense matrix peak	312 TFLOPS (FP16 Tensor Core, dense)
Max TDP	300 W

Source ledger

NVIDIA A100 Tensor Core GPU ↗

Retrieved 2026-08-30

NVIDIA A100 80GB PCIe Product Brief ↗

Retrieved 2026-08-30

NVIDIA A100 Tensor Core GPU Datasheet ↗

Retrieved 2026-08-31

Caveats

The older product brief says up to 1.94TB/s; the current NVIDIA specification table is retained as the canonical exact value (1,935GB/s). See conflicts.

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 A100 80GB PCIe →