Every open model in our directory, ranked by how well it runs on Rubin R100 Ultra (288GB) at 4-bit. Grades weigh whether the model fits in memory and how fast it should run.
Rubin R100 Ultra has 288 GB of VRAM, enough to run 286 of the 298 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 90 tok/s.
286 of 298 models run comfortably on Rubin R100 Ultra.
See full Rubin R100 Ultra 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 | 89.5 tok/s | 197.8 GB |
| Llama 4 MaverickMeta | SS | 121.0 tok/s | 146.4 GB |
| MiniMax M3MiniMax | SS | 89.5 tok/s | 197.8 GB |
| BeamReflection AI | SS | 89.5 tok/s | 197.8 GB |
| Ring-2.6-1TinclusionAI | SS | 104.7 tok/s | 169.2 GB |
| DeepSeek-V4.1-Flashdeepseek-ai | SS | 128.5 tok/s | 137.8 GB |
| Naive-N0.5-FlashNaiveAI | SS | 132.7 tok/s | 133.5 GB |
| MiMo-V2.6-Flash-RLXiaomi MiMo | SS | 137.1 tok/s | 129.2 GB |
| Llama 3.1 70B InstructMeta | SS | 157.0 tok/s | 112.8 GB |
| Llama 3.3 70B InstructMeta | SS | 157.0 tok/s | 112.8 GB |
| DeepSeek-V4-FlashDeepSeek | SS | 158.1 tok/s | 112.0 GB |
| Nvidia Nemotron 3 SuperNVIDIA | SS | 171.2 tok/s | 103.5 GB |
Mistral Large 2Mistral AI | 123B | SS | 89.5 tok/s | 197.8 GB | |
Llama 4 MaverickMeta | 400B(17B active) | SS | 121.0 tok/s | 146.4 GB | |
MiniMax M3MiniMax | 428B(23B active) | SS | 89.5 tok/s | 197.8 GB | |
BeamReflection AI | 501B(23B active) | SS | 89.5 tok/s | 197.8 GB | |
Ring-2.6-1TinclusionAI | 1000B(63B active) | SS | 104.7 tok/s | 169.2 GB | |
DeepSeek-V4.1-Flashdeepseek-ai | 552B(16B active) | SS | 128.5 tok/s | 137.8 GB | |
Naive-N0.5-FlashNaiveAI | 309B(15.5B active) | SS | 132.7 tok/s | 133.5 GB | |
MiMo-V2.6-Flash-RLXiaomi MiMo | 310.76B(15B active) | SS | 137.1 tok/s | 129.2 GB | |
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| 70B | SS | 157.0 tok/s | 112.8 GB | ||
| 70B | SS | 157.0 tok/s | 112.8 GB | ||
DeepSeek-V4-FlashDeepSeek | 284B(13B active) | SS | 158.1 tok/s | 112.0 GB | |
Nvidia Nemotron 3 SuperNVIDIA | 120B(12B active) | SS | 171.2 tok/s | 103.5 GB | |
GLM-5Z.ai | 744B(40B active) | SS | 201.9 tok/s | 87.7 GB | |
GLM-5.1Z.ai | 744B(40B active) | SS | 201.9 tok/s | 87.7 GB | |
Kimi K2.7 CodeMoonshot AI | 1000B(32B active) | SS | 205.5 tok/s | 86.2 GB | |
Kimi K2.6Moonshot AI | 1000B(32B active) | SS | 205.5 tok/s | 86.2 GB | |
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Kimi K2 Instruct 0905Moonshot AI | 1000B(32B active) | SS | 209.3 tok/s | 84.6 GB | |
Kimi K2 ThinkingMoonshot AI | 1000B(32B active) | SS | 209.3 tok/s | 84.6 GB | |
Kimi K2.5Moonshot AI | 1000B(32B active) | SS | 209.3 tok/s | 84.6 GB | |
Falcon 180BTechnology Innovation Institute | 180B | SS | 164.2 tok/s | 107.8 GB | |
IQuest-Q1IQuest | 320.32B(15B active) | SS | 245.6 tok/s | 72.1 GB | |
GLM-4.6Z.ai | 355B(32B active) | SS | 252.1 tok/s | 70.3 GB | |
Mistral Large 3 675BMistral AI | 675B(41B active) | SS | 267.3 tok/s | 66.3 GB | |
Gemma 4 31B ITGoogle | 31B | SS | 216.1 tok/s | 82.0 GB | |
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DeepSeek-V3DeepSeek | 671B(37B active) | SS | 295.9 tok/s | 59.8 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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Mistral Large 2 is the highest-rated model that runs on Rubin R100 Ultra at 4-bit, at about 90 tok/s. In total, Rubin R100 Ultra can run 286 of the 298 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.