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Every open model in our directory, ranked by how well it runs on NVIDIA A100 SXM4 80GB (80GB) at 4-bit. Grades weigh whether the model fits in memory and how fast it should run.
NVIDIA A100 SXM4 80GB has 80 GB of VRAM, enough to run 256 of the 297 open models we track at 4-bit. The largest that still fits well is Kimi K2 Instruct (1000B), and the top-rated is Qwen3-235B-A22B at about 45 tok/s.
256 of 297 models run comfortably on NVIDIA A100 SXM4 80GB.
See full NVIDIA A100 SXM4 80GB specs and pricingThe top models this device can run at 4-bit, ranked by fit and speed.
| Model | Grade | Speed | VRAM |
|---|---|---|---|
| Qwen3-235B-A22BAlibaba | SS | 45.2 tok/s | 36.3 GB |
| Mistral Small 3 24BMistral AI | SS | 42.1 tok/s | 39.0 GB |
| Apertus 70BEPFL, ETH Zurich, CSCS | SS | 37.8 tok/s | 43.4 GB |
| Llama 2 70B ChatMeta | SS | 37.8 tok/s | 43.4 GB |
| Mixtral 8x22B InstructMistral AI | SS | 37.7 tok/s | 43.6 GB |
| LLaMA 65BMeta | SS | 41.8 tok/s | 39.3 GB |
| Qwen 3.5 OmniAlibaba | SS | 36.3 tok/s | 45.2 GB |
| Llama 3 70B InstructMeta | SS | 35.9 tok/s | 45.7 GB |
| Gemma 3 27B ITGoogle | SS | 37.5 tok/s | 43.8 GB |
| Qwen3.5-122B-A10BAlibaba | SS | 60.2 tok/s | 27.3 GB |
| Qwen3.5-397B-A17BAlibaba | SS | 35.7 tok/s | 46.0 GB |
| KolibriAleph Alpha | SS | 62.6 tok/s | 26.2 GB |
Qwen3-235B-A22BAlibaba | 235B(22B active) | SS | 45.2 tok/s | 36.3 GB | |
Mistral Small 3 24BMistral AI | 24B | SS | 42.1 tok/s | 39.0 GB | |
Apertus 70BEPFL, ETH Zurich, CSCS | 70B | SS | 37.8 tok/s | 43.4 GB | |
Llama 2 70B ChatMeta | 70B | SS | 37.8 tok/s | 43.4 GB | |
Mixtral 8x22B InstructMistral AI | 141B(39B active) | SS | 37.7 tok/s | 43.6 GB | |
LLaMA 65BMeta | 65B | SS | 41.8 tok/s | 39.3 GB | |
Qwen 3.5 OmniAlibaba | 397B(17B active) | SS | 36.3 tok/s | 45.2 GB | |
| 70B | SS | 35.9 tok/s | 45.7 GB | ||
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Gemma 3 27B ITGoogle | 27B | SS | 37.5 tok/s | 43.8 GB | |
Qwen3.5-122B-A10BAlibaba | 122B(10B active) | SS | 60.2 tok/s | 27.3 GB | |
Qwen3.5-397B-A17BAlibaba | 397B(17B active) | SS | 35.7 tok/s | 46.0 GB | |
KolibriAleph Alpha | 78B(3B active) | SS | 62.6 tok/s | 26.2 GB | |
minimax-m2.5MiniMax | 230B(10B active) | SS | 72.3 tok/s | 22.7 GB | |
Gemma 4 12BGoogle | 12B | SS | 51.2 tok/s | 32.0 GB | |
Gemma 4 12B Coderyuxinlu1 | 12B | SS | 51.2 tok/s | 32.0 GB | |
GLM-4.5Z.ai | 355B(32B active) | SS | 31.7 tok/s | 51.8 GB | |
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Kimi K2 InstructMoonshot AI | 1000B(32B active) | SS | 31.7 tok/s | 51.8 GB | |
Muse Glimmer-30BMeta | 30B | SS | 33.3 tok/s | 49.4 GB | |
K2-Horizon-MoVA-36B-A4BInstitute of Foundation Models | 36B(4B active) | SS | 83.8 tok/s | 19.6 GB | |
GLM-4.7Z.ai | 358B(32B active) | SS | 31.2 tok/s | 52.6 GB | |
Holo4-27BHcompany | 27B | SS | 96.3 tok/s | 17.0 GB | |
Mixtral 8x7B InstructMistral AI | 46.7B(12.9B active) | SS | 144.4 tok/s | 11.4 GB | |
Falcon 40B InstructTechnology Innovation Institute | 40B | SS | 67.4 tok/s | 24.4 GB | |
Hy3Tencent | 295B(21B active) | SS | 29.5 tok/s | 55.7 GB | |
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| 81.29B(3B active) | SS | 192.4 tok/s | 8.5 GB | ||
Model Compatibility
Use the calculator to weigh this device against any other, model by model, with speed and fit scores.
Straight answers to the questions we hear most often.
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Qwen3-235B-A22B is the highest-rated model that runs on NVIDIA A100 SXM4 80GB at 4-bit, at about 45 tok/s. In total, NVIDIA A100 SXM4 80GB can run 256 of the 297 open models we track.
We size each model at 4-bit (Q4_K_M), add the KV cache for its context length and a fixed runtime overhead, then compare that to this device VRAM. Each model is graded on how comfortably it fits and how fast it should run on this card.
It is an estimate of decode speed in tokens per second at 4-bit, based on this device memory bandwidth and each model size. Real speed depends on your runtime, batch size, and context length, so use it to compare models, not as a hard promise.
Lower-bit quantization shrinks a model so more of them fit. These grades already assume 4-bit, which is the common local default. Going below 4-bit can squeeze a larger model in at some quality cost; going above needs more VRAM and may not fit.
Mixture-of-experts models only activate a fraction of their parameters at a time, so their runtime memory is closer to the active size than the headline size. We score fit on the active parameters, which is why some very large models still fit.
