Is 80GB of VRAM enough for AI?

It depends entirely on which models, and the honest answer is a list rather than a yes or no. On our measured gates, 80GB runs 12 of our 12 workloads: Qwen3 4B, Llama 3.1 8B, Stable Diffusion XL, Qwen2.5-Coder 14B, Z-Image Turbo, LTX-Video and others. Nothing in our suite is out of reach at this tier.

Can you run Llama 3.3 70B on 80GB?

Yes, 70B needs roughly 42GB at Q4_K_M and 80GB clears it.

Can you run FLUX.1-dev on 80GB?

Yes, FLUX.1-dev needs about 26GB at BF16 and 80GB clears it.

What's the next VRAM tier worth stepping up to from 80GB?

Nothing in our suite requires more than 80GB, so the next tier up buys headroom for longer context, larger batches and models beyond what we test, not new capability against this suite.

Does a smaller quantisation get me past the VRAM limit?

Often, yes, and we deliberately don't count it. Running a model at a lower quantisation reduces its memory requirement and its output quality, and if we silently swapped precision whenever a card ran out of room, every number on this site would become meaningless. Our floors are for Q4_K_M language models and BF16 diffusion. If you drop below that you're running a different model, and it should be measured as one.

Do I need system RAM as well as 80GB of VRAM?

For some workloads, yes, and it catches people out. LTX-Video wants roughly 20GB of host RAM and Wan 2.2 wants around 38GB, on top of their VRAM requirement, because the weights have to be staged. A card with enough VRAM sitting in a RAM-starved machine still fails. We gate on both, which is why a few cards you'd expect to clear a workload don't.

I'm between two VRAM tiers and money's tight. Which way do I jump?

Here's my rule after benchmarking the whole board: between 16GB and 24GB, go 16 and rent for the big jobs, the jump to 24 isn't big enough to matter once you account for context-window headroom on top of model weights, so they're effectively the same tier now. The real step up is 32GB on the newest architecture (RTX 5090). Below that, buy the cheaper card and put the savings into rented hours on cards you could never justify owning.

VRAM tiers · measured gates across our fleet · Updated July 2026

What Can You Actually Run on 80GB of VRAM?

A 80GB GPU runs 12 of the 12 AI workloads we benchmark and is locked out of 0. 80GB is datacenter territory, H100, A100, H800 class. Everything in our 12-workload suite fits with headroom, and the question stops being 'what can I run' and becomes 'is it worth what it costs to run it'. Every floor below is measured: we ran each model on each card until it failed, and published where it failed.

Rentable at this tier

NVIDIA H100 80GB HBM3

80GB at 3350 GB/s. AI Score 62.6/100 across our 12-workload suite. Measured on our bench.

Pros

80GB, runs 12 of our 12 workloads
3350 GB/s of memory bandwidth
Rentable by the hour

Cons

Datacenter hardware

Best for: Testing whether 80GB is genuinely enough for your workload before you buy.

Rentable at this tier

NVIDIA H100 PCIe

80GB at 2000 GB/s. AI Score 46.4/100 across our 12-workload suite. Anchored estimate.

Pros

80GB, runs 12 of our 12 workloads
2000 GB/s of memory bandwidth
Rentable by the hour

Cons

Datacenter hardware

Best for: Testing whether 80GB is genuinely enough for your workload before you buy.

Rentable at this tier

NVIDIA A100 80GB SXM4

80GB at 2039 GB/s. AI Score 33.1/100 across our 12-workload suite. Measured on our bench.

Pros

80GB, runs 12 of our 12 workloads
2039 GB/s of memory bandwidth
Rentable by the hour

Cons

Datacenter hardware

Best for: Testing whether 80GB is genuinely enough for your workload before you buy.

12/12

Workloads that run

at the precision we test

0/12

Workloads gated out

hard VRAM ceiling, no fix

n/a

Next thing you'd unlock

nothing. This tier runs everything

80GB cards in our data

of the fleet we've benchmarked

80GB is datacenter territory, H100, A100, H800 class. Everything in our 12-workload suite fits with headroom, and the question stops being 'what can I run' and becomes 'is it worth what it costs to run it'.

What runs on 80GB. Measured floors from our own gate testing

Model	Type	VRAM floor	On 80GB?
Qwen3 4B	LLM	~5GB	Runs
Llama 3.1 8B	LLM	~8GB	Runs
Stable Diffusion XL	Image	~8GB	Runs
Qwen2.5-Coder 14B	LLM	~11.5GB	Runs
Z-Image Turbo	Image	~13GB	Runs
LTX-Video	Video	~14GB	Runs
Wan 2.2 (720p)	Video	~18GB	Runs
Qwen3 32B	LLM	~20GB	Runs
FLUX.1-dev	Image	~26GB	Runs
FLUX.1 Kontext	Editing	~26GB	Runs
Llama 3.3 70B	LLM	~42GB	Runs
Qwen-Image-Edit	Editing	~42GB	Runs

Floors are measured at Q4_K_M for language models and BF16 for diffusion (SDXL at FP16). A smaller quantisation lowers the requirement and the quality, we don't mix precisions in one column.

What 80GB actually delivers, NVIDIA H100 80GB HBM3, single stream, Q4_K_M

Qwen3 4B

310.26 tok/s

Llama 3.1 8B

261.83 tok/s

Qwen2.5-Coder 14B

144.84 tok/s

Qwen3 32B

74.07 tok/s

Llama 3.3 70B

41 tok/s

Measured on our bench. NVIDIA H100 80GB HBM3 is the highest-scoring 80GB card in our data (AI Score 62.6/100). Speed at a given tier varies with bandwidth, capacity decides what runs, bandwidth decides how fast.

Two things decide whether a GPU can do AI, and people consistently get the order wrong. Capacity decides what runs. Bandwidth decides how fast it runs. Capacity comes first, because a model that doesn't fit doesn't run slowly: it doesn't run at all, and no amount of bandwidth rescues it. That's why our AI Score treats a won't-fit as a zero rather than quietly excluding it. The practical consequence: shop for the VRAM tier that clears the models you actually intend to use, then optimise for bandwidth within that tier. Buying a faster card at the same capacity gets you a percentage. Buying the next capacity tier up can get you a model you literally could not run before.

Our verdict

80GB runs everything we test. The question at this tier is economics, not capability. On our measured gates, 80GB clears 12 of 12 workloads. Buy capacity first and bandwidth second: capacity decides what runs, bandwidth only decides how fast.

FAQ

Is 80GB of VRAM enough for AI?: It depends entirely on which models, and the honest answer is a list rather than a yes or no. On our measured gates, 80GB runs 12 of our 12 workloads: Qwen3 4B, Llama 3.1 8B, Stable Diffusion XL, Qwen2.5-Coder 14B, Z-Image Turbo, LTX-Video and others. Nothing in our suite is out of reach at this tier.
Can you run Llama 3.3 70B on 80GB?: Yes, 70B needs roughly 42GB at Q4_K_M and 80GB clears it.
Can you run FLUX.1-dev on 80GB?: Yes, FLUX.1-dev needs about 26GB at BF16 and 80GB clears it.
What's the next VRAM tier worth stepping up to from 80GB?: Nothing in our suite requires more than 80GB, so the next tier up buys headroom for longer context, larger batches and models beyond what we test, not new capability against this suite.
Does a smaller quantisation get me past the VRAM limit?: Often, yes, and we deliberately don't count it. Running a model at a lower quantisation reduces its memory requirement and its output quality, and if we silently swapped precision whenever a card ran out of room, every number on this site would become meaningless. Our floors are for Q4_K_M language models and BF16 diffusion. If you drop below that you're running a different model, and it should be measured as one.
Do I need system RAM as well as 80GB of VRAM?: For some workloads, yes, and it catches people out. LTX-Video wants roughly 20GB of host RAM and Wan 2.2 wants around 38GB, on top of their VRAM requirement, because the weights have to be staged. A card with enough VRAM sitting in a RAM-starved machine still fails. We gate on both, which is why a few cards you'd expect to clear a workload don't.
I'm between two VRAM tiers and money's tight. Which way do I jump?: Here's my rule after benchmarking the whole board: between 16GB and 24GB, go 16 and rent for the big jobs, the jump to 24 isn't big enough to matter once you account for context-window headroom on top of model weights, so they're effectively the same tier now. The real step up is 32GB on the newest architecture (RTX 5090). Below that, buy the cheaper card and put the savings into rented hours on cards you could never justify owning.

How we test

The VRAM floors on this page are measured, not calculated. We ran every model in our suite on every GPU in our fleet and recorded where it failed. When a model exceeds a card's VRAM we publish a hard won't-fit result with the requirement we observed, rather than quietly dropping to a smaller quantisation to produce a number, a card that can't run a model scores zero on it in our AI Score. LLMs are measured at Q4_K_M on llama.cpp (llama-bench, -p 512 -n 128). Diffusion and video run at BF16 on diffusers/ComfyUI, with SDXL at FP16. Floors are for those precisions: running a model at a smaller quantisation will lower its VRAM requirement and its quality, and we don't mix the two in one column. Two caveats worth stating plainly. First, these floors assume the model is the only thing on the card: a display attached to the same GPU, or a desktop compositor, eats into your headroom. Second, some workloads gate on system RAM as well as VRAM, LTX-Video wants roughly 20GB of host RAM and Wan 2.2 wants around 38GB, and a card with enough VRAM in a RAM-starved machine still fails. We gate on both. Speed figures are single-GPU, single-stream, batch-size-1, from our own runs. Cards labelled Measured were rented and run by us; Estimated cards are interpolated per workload against those anchors and labelled on every row.