Every open model in our directory, ranked by how well it runs on NVIDIA Rubin GPU (288GB) at 4-bit. Grades weigh whether the model fits in memory and how fast it should run.
NVIDIA Rubin GPU has 288 GB of VRAM, enough to run 311 of the 324 open models we track at 4-bit. The largest that still fits well is Mistral Large 4 (1050B), and the top-rated is Mistral Large 2 at about 716 tok/s.
311 of 324 models run comfortably on NVIDIA Rubin GPU.
See full NVIDIA Rubin GPU specs and pricingThe top models this device can run at 4-bit, ranked by fit and speed.
| Model | Grade | Speed | VRAM |
|---|---|---|---|
| Mistral Large 2Mistral AI | SS | 716.3 tok/s | 197.8 GB |
| GLM-5.3-FlashZhipu AI | SS | 914.4 tok/s | 154.9 GB |
| Llama 4 MaverickMeta | SS | 968.0 tok/s | 146.4 GB |
| MiniMax M3MiniMax | SS | 716.1 tok/s | 197.8 GB |
| BeamReflection AI | SS | 716.1 tok/s | 197.8 GB |
| Beam 1.0 501B A23BReflection | SS | 673.5 tok/s | 210.4 GB |
| Ring-2.6-1TinclusionAI | SS | 837.5 tok/s | 169.2 GB |
| DeepSeek-V4.1-Flashdeepseek-ai | SS | 1028.3 tok/s | 137.8 GB |
| Naive-N0.5-FlashNaiveAI | SS | 1061.4 tok/s | 133.5 GB |
| MiMo-V2.6-Flash-RLXiaomi MiMo | SS | 1096.6 tok/s | 129.2 GB |
| Llama 3.1 70B InstructMeta | SS | 1256.3 tok/s | 112.8 GB |
| Llama 3.3 70B InstructMeta | SS | 1256.3 tok/s | 112.8 GB |
Mistral Large 2Mistral AI | 123B | SS | 716.3 tok/s | 197.8 GB | |
GLM-5.3-FlashZhipu AI | 320B(18B active) | SS | 914.4 tok/s | 154.9 GB | |
Llama 4 MaverickMeta | 400B(17B active) | SS | 968.0 tok/s | 146.4 GB | |
MiniMax M3MiniMax | 428B(23B active) | SS | 716.1 tok/s | 197.8 GB | |
BeamReflection AI | 501B(23B active) | SS | 716.1 tok/s | 197.8 GB | |
Beam 1.0 501B A23BReflection | 501.27B(23.4B active) | SS | 673.5 tok/s | 210.4 GB | |
Ring-2.6-1TinclusionAI | 1000B(63B active) | SS | 837.5 tok/s | 169.2 GB | |
DeepSeek-V4.1-Flashdeepseek-ai | 552B(16B active) | SS | 1028.3 tok/s | 137.8 GB | |
| Ad | |||||
Naive-N0.5-FlashNaiveAI | 309B(15.5B active) | SS | 1061.4 tok/s | 133.5 GB | |
MiMo-V2.6-Flash-RLXiaomi MiMo | 310.76B(15B active) | SS | 1096.6 tok/s | 129.2 GB | |
| 70B | SS | 1256.3 tok/s | 112.8 GB | ||
| 70B | SS | 1256.3 tok/s | 112.8 GB | ||
DeepSeek-V4-FlashDeepSeek | 284B(13B active) | SS | 1264.5 tok/s | 112.0 GB | |
| 120B(12B active) | SS | 1310.4 tok/s | 108.1 GB | ||
Nvidia Nemotron 3 SuperNVIDIA | 120B(12B active) | SS | 1369.4 tok/s | 103.5 GB | |
GLM-5Z.ai | 744B(40B active) | SS | 1615.5 tok/s | 87.7 GB | |
| Ad | |||||
GLM-5.1Z.ai | 744B(40B active) | SS | 1615.5 tok/s | 87.7 GB | |
Kimi K2.7 CodeMoonshot AI | 1000B(32B active) | SS | 1644.2 tok/s | 86.2 GB | |
Kimi K2.6Moonshot AI | 1000B(32B active) | SS | 1644.2 tok/s | 86.2 GB | |
Kimi K2 Instruct 0905Moonshot AI | 1000B(32B active) | SS | 1674.8 tok/s | 84.6 GB | |
Kimi K2 ThinkingMoonshot AI | 1000B(32B active) | SS | 1674.8 tok/s | 84.6 GB | |
Kimi K2.5Moonshot AI | 1000B(32B active) | SS | 1674.8 tok/s | 84.6 GB | |
Falcon 180BTechnology Innovation Institute | 180B | SS | 1313.7 tok/s | 107.8 GB | |
IQuest-Q1IQuest | 320.32B(15B active) | SS | 1964.7 tok/s | 72.1 GB | |
| Ad | |||||
GLM-4.6Z.ai | 355B(32B active) | SS | 2016.5 tok/s | 70.3 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.
Can't find what you're looking for? Book a Discovery Call
Mistral Large 2 is the highest-rated model that runs on NVIDIA Rubin GPU at 4-bit, at about 716 tok/s. In total, NVIDIA Rubin GPU can run 311 of the 324 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.