Every open model in our directory, ranked by how well it runs on MINISFORUM AI X1 Pro-370 (16GB) at 4-bit. Grades weigh whether the model fits in memory and how fast it should run.
MINISFORUM AI X1 Pro-370 has 16 GB of VRAM, enough to run 214 of the 298 open models we track at 4-bit. The largest that still fits well is AliceAI-Foundation-80B-A3B-Base (81.3B), and the top-rated is Mellum2.1 at about 36 tok/s.
214 of 298 models run comfortably on MINISFORUM AI X1 Pro-370.
See full MINISFORUM AI X1 Pro-370 specs and pricingThe top models this device can run at 4-bit, ranked by fit and speed.
| Model | Grade | Speed | VRAM |
|---|---|---|---|
| Mellum2.1JetBrains | AA | 35.7 tok/s | 2.0 GB |
| Holo4-35B-A3BHcompany | AA | 31.0 tok/s | 2.3 GB |
| LFM2.5-8B-A1BLiquid AI | AA | 24.9 tok/s | 2.9 GB |
| BolmoAi2 | AA | 144.9 tok/s | 0.5 GB |
| AliceAI-Foundation-80B-A3B-Baseyandex | BB | 8.5 tok/s | 8.5 GB |
| Qwen3-30B-A3BAlibaba | BB | 13.5 tok/s | 5.4 GB |
| North Mini CodeCohere | BB | 8.6 tok/s | 8.4 GB |
| Nemotron 3 Nano OmniNVIDIA | BB | 8.5 tok/s | 8.5 GB |
| Qwen3.6 35B-A3BAlibaba | BB | 8.5 tok/s | 8.5 GB |
| Qwen3.5-35B-A3BAlibaba | BB | 8.5 tok/s | 8.5 GB |
| Mixtral 8x7B InstructMistral AI | BB | 6.4 tok/s | 11.4 GB |
| DiffusionGemma 26B-A4BGoogle | BB | 6.9 tok/s | 10.5 GB |
Mellum2.1JetBrains | 12B(2.5B active) | AA | 35.7 tok/s | 2.0 GB | |
Holo4-35B-A3BHcompany | 35B(3B active) | AA | 31.0 tok/s | 2.3 GB | |
LFM2.5-8B-A1BLiquid AI | 8.3B(1.5B active) | AA | 24.9 tok/s | 2.9 GB | |
BolmoAi2 | 0B | AA | 144.9 tok/s | 0.5 GB | |
| 81.29B(3B active) | BB | 8.5 tok/s | 8.5 GB | ||
Qwen3-30B-A3BAlibaba | 30B(3B active) | BB | 13.5 tok/s | 5.4 GB | |
North Mini CodeCohere | 30B(3B active) | BB | 8.6 tok/s | 8.4 GB | |
Nemotron 3 Nano OmniNVIDIA | 30B(3B active) | BB | 8.5 tok/s | 8.5 GB | |
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Qwen3.6 35B-A3BAlibaba | 35B(3B active) | BB | 8.5 tok/s | 8.5 GB | |
Qwen3.5-35B-A3BAlibaba | 35B(3B active) | BB | 8.5 tok/s | 8.5 GB | |
Mixtral 8x7B InstructMistral AI | 46.7B(12.9B active) | BB | 6.4 tok/s | 11.4 GB | |
DiffusionGemma 26B-A4BGoogle | 25.2B(3.8B active) | BB | 6.9 tok/s | 10.5 GB | |
Gemma 4 26B-A4B ITGoogle | 26B(4B active) | BB | 6.6 tok/s | 11.0 GB | |
Llama 2 13B ChatMeta | 13B | BB | 8.6 tok/s | 8.5 GB | |
Xing4.0-29B-A4BChina Telecom AI | 29B(4B active) | BB | 6.5 tok/s | 11.2 GB | |
Apertus 8BEPFL, ETH Zurich, CSCS | 8B | BB | 13.4 tok/s | 5.4 GB | |
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| 8B | BB | 12.8 tok/s | 5.7 GB | ||
LensVLM-9BApple | 9B | BB | 12.0 tok/s | 6.0 GB | |
| 9B | BB | 12.0 tok/s | 6.0 GB | ||
PersonaPlex 7BNVIDIA | 7B | BB | 15.1 tok/s | 4.8 GB | |
Llama 2 7B ChatMeta | 7B | BB | 15.1 tok/s | 4.8 GB | |
Gemma 4 E4B ITGoogle | 4B | BB | 10.5 tok/s | 6.9 GB | |
Gemma 3 4B ITGoogle | 4B | BB | 10.5 tok/s | 6.9 GB | |
Youtu-Parsing-Omnitencent | 5.33B | BB | 11.3 tok/s | 6.4 GB | |
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Mistral 7B InstructMistral AI | 7B | BB | 11.3 tok/s | 6.4 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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Mellum2.1 is the highest-rated model that runs on MINISFORUM AI X1 Pro-370 at 4-bit, at about 36 tok/s. In total, MINISFORUM AI X1 Pro-370 can run 214 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.