What LLMs Can I Run on a NVIDIA A100 80GB?

80 GB of VRAM, of which about 76.0 GB is usable for a model. That runs 19 of the 20 models here , up to Qwen2.5 72B.

Largest model that fits: Qwen2.5 72B at Q8_0, needing 41.4 GB and leaving 34.6 GB spare. Its context can run to 32K tokens before memory runs out.

Every model, on this hardware

Which models run on a NVIDIA A100 80GB
Model	Size	Needs	Runs	Max context
Llama 3.2 1B	1.2B	1.7 GB	Yes	128K
Llama 3.2 3B	3.2B	3.4 GB	Yes	128K
Llama 3.1 8B	8.0B	6.0 GB	Yes	128K
Llama 3.3 70B	70.5B	40.3 GB	Yes	122K
Qwen2.5 3B	3.1B	2.7 GB	Yes	32K
Qwen2.5 7B	7.6B	5.2 GB	Yes	32K
Qwen2.5 14B	14.8B	10.0 GB	Yes	32K
Qwen2.5 32B	32.8B	20.0 GB	Yes	32K
Qwen2.5 72B	72.7B	41.4 GB	Yes	32K
Qwen2.5 Coder 7B	7.6B	5.2 GB	Yes	32K
Qwen2.5 Coder 32B	32.8B	20.0 GB	Yes	32K
Qwen3 32B	32.8B	20.0 GB	Yes	40K
Mistral 7B v0.3	7.3B	5.6 GB	Yes	32K
Gemma 2 9B	9.2B	8.3 GB	Yes	8K
Gemma 2 27B	27.2B	17.9 GB	Yes	8K
Phi-4	14.7B	10.0 GB	Yes	16K
Qwen3 30B-A3B	30.5B	17.5 GB	Yes	40K
Qwen3 235B-A22B	235.1B	125.4 GB	No	—
Mixtral 8x7B	46.7B	26.3 GB	Yes	32K
DeepSeek-R1 Distill Qwen 14B	14.8B	10.0 GB	Yes	128K

Q4_K_M at 8K context. "Max context" is the longest context that still fits once the weights are loaded.

Memory decides this, not speed

For inference, memory capacity decides what runs and bandwidth decides how fast. A slower card with more memory beats a faster card with less, every time, because a model that does not fit does not run at any speed. That is why a 16 GB RTX 4060 Ti is a more useful local-LLM card than a faster 12 GB one.

The other half is context. Weights are a fixed cost, but the key-value cache grows with every token, and on a long conversation it can outgrow the weights. That is why the table above gives a maximum context per model rather than a yes or no — plenty of models load fine on this hardware and then run out of room mid-conversation.

Frequently asked questions

What LLMs can a NVIDIA A100 80GB run?

19 of the 20 models on this site, at Q4_K_M with a 8K context. The largest is Qwen2.5 72B at 72.7B parameters, needing 41.4 GB of the roughly 76.0 GB usable.

Why is usable memory less than the 80 GB on the box?

The framework, the driver and, on a card driving a display, the screen itself all take a share before the weights get any. This page assumes about 76.0 GB of the 80 GB is available, which is why a model that looks like it fits on paper sometimes does not.

Does a bigger model at a rougher quantisation beat a smaller one?

Usually, yes. A 32B model at Q3_K_M generally outperforms a 7B at Q8_0, even though the smaller model is barely quantised at all. The practical rule is to fit the largest model you can and then step the quantisation down only as far as you must — which is what the "best format" column above is showing.

How accurate are these figures?

They count weights, the key-value cache at the chosen context, and a fixed runtime allowance. Real usage moves with the runtime, the batch size and whether flash attention is enabled. Treat anything within a gigabyte of capacity as "probably not" rather than "just fits" — which is why the table says "only just" rather than "yes" past 95% of usable memory.

Manufacturer specifications for VRAM and unified memory. Model config.json files published on Hugging Face.

An estimate of memory, not a benchmark. It counts model weights, the key-value cache at the context you choose, and a fixed runtime allowance. Actual usage moves with the runtime, the batch size, whether flash attention is on, and how much the operating system has already taken from a shared memory pool. Treat a result within a gigabyte of your card's capacity as 'probably not' rather than 'just fits'.

Data on this page last verified 2 September 2026. How these figures are calculated