Every open model in our directory, ranked by how well it runs on Origin PC L-CLASS v2 (48GB) at 4-bit. Grades weigh whether the model fits in memory and how fast it should run.
Origin PC L-CLASS v2 has 48 GB of VRAM, enough to run 125 of the 169 open models we track at 4-bit. The largest that still fits well is Qwen3-235B-A22B (235B), and the top-rated is minimax-m2.5 at about 34 tok/s.
125 of 169 models run comfortably on Origin PC L-CLASS v2.
See full Origin PC L-CLASS v2 specs and pricingThe top models this device can run at 4-bit, ranked by fit and speed.
| Model | Grade | Speed | VRAM |
|---|---|---|---|
| minimax-m2.5MiniMax | SS | 34.0 tok/s | 22.7 GB |
| Mixtral 8x7B InstructMistral AI | SS | 68.0 tok/s | 11.4 GB |
| Gemma 4 26B-A4B ITGoogle | SS | 70.2 tok/s | 11.0 GB |
| DiffusionGemma 26B-A4BGoogle | SS | 73.7 tok/s | 10.5 GB |
| Nemotron 3 Nano OmniNVIDIA | SS | 90.6 tok/s | 8.5 GB |
| Qwen3.6 35B-A3BAlibaba | SS | 90.6 tok/s | 8.5 GB |
| Qwen3.5-35B-A3BAlibaba | SS | 90.6 tok/s | 8.5 GB |
| North Mini CodeCohere | SS | 92.2 tok/s | 8.4 GB |
| Qwen3.5-122B-A10BAlibaba | SS | 28.3 tok/s | 27.3 GB |
| Llama 3.1 8B InstructMeta | SS | 58.0 tok/s | 13.3 GB |
| Qwen3-30B-A3BAlibaba | SS | 143.5 tok/s | 5.4 GB |
| Llama 2 13B ChatMeta | SS | 91.3 tok/s | 8.5 GB |
minimax-m2.5MiniMax | 230B(10B active) | SS | 34.0 tok/s | 22.7 GB | |
Mixtral 8x7B InstructMistral AI | 46.7B(12.9B active) | SS | 68.0 tok/s | 11.4 GB | |
Gemma 4 26B-A4B ITGoogle | 26B(4B active) | SS | 70.2 tok/s | 11.0 GB | |
DiffusionGemma 26B-A4BGoogle | 25.2B(3.8B active) | SS | 73.7 tok/s | 10.5 GB | |
Nemotron 3 Nano OmniNVIDIA | 30B(3B active) | SS | 90.6 tok/s | 8.5 GB | |
Qwen3.6 35B-A3BAlibaba | 35B(3B active) | SS | 90.6 tok/s | 8.5 GB | |
Qwen3.5-35B-A3BAlibaba | 35B(3B active) | SS | 90.6 tok/s | 8.5 GB | |
North Mini CodeCohere | 30B(3B active) | SS | 92.2 tok/s | 8.4 GB | |
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Qwen3.5-122B-A10BAlibaba | 122B(10B active) | SS | 28.3 tok/s | 27.3 GB | |
| 8B | SS | 58.0 tok/s | 13.3 GB | ||
Qwen3-30B-A3BAlibaba | 30B(3B active) | SS | 143.5 tok/s | 5.4 GB | |
Llama 2 13B ChatMeta | 13B | SS | 91.3 tok/s | 8.5 GB | |
Falcon 40B InstructTechnology Innovation Institute | 40B | SS | 31.7 tok/s | 24.4 GB | |
Qwen3.5-9BAlibaba | 9B | SS | 31.4 tok/s | 24.6 GB | |
| 9B | AA | 128.5 tok/s | 6.0 GB | ||
| 8B | AA | 136.4 tok/s | 5.7 GB | ||
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LFM2.5-8B-A1BLiquid AI | 8.3B(1.5B active) | AA | 265.9 tok/s | 2.9 GB | |
Qwen3-235B-A22BAlibaba | 235B(22B active) | AA | 21.3 tok/s | 36.3 GB | |
Gemma 4 12BGoogle | 12B | AA | 24.1 tok/s | 32.0 GB | |
Gemma 4 12B Coderyuxinlu1 | 12B | AA | 24.1 tok/s | 32.0 GB | |
Gemma 4 E4B ITGoogle | 4B | AA | 111.7 tok/s | 6.9 GB | |
Gemma 3 4B ITGoogle | 4B | AA | 111.7 tok/s | 6.9 GB | |
Mistral 7B InstructMistral AI | 7B | AA | 120.8 tok/s | 6.4 GB | |
PersonaPlex 7BNVIDIA | 7B | AA | 161.4 tok/s | 4.8 GB | |
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Llama 2 7B ChatMeta | 7B | AA | 161.4 tok/s | 4.8 GB | |
Model Compatibility
Use the calculator to weigh this device against any other, model by model, with speed and fit scores.
minimax-m2.5 is the highest-rated model that runs on Origin PC L-CLASS v2 at 4-bit, at about 34 tok/s. In total, Origin PC L-CLASS v2 can run 125 of the 169 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.
