Every open model in our directory, ranked by how well it runs on NOVATECH AI Workstation (i9-14900K + RTX 5080) (16GB) at 4-bit. Grades weigh whether the model fits in memory and how fast it should run.
NOVATECH AI Workstation (i9-14900K + RTX 5080) has 16 GB of VRAM, enough to run 113 of the 169 open models we track at 4-bit. The largest that still fits well is Mixtral 8x7B Instruct (46.7B), and the top-rated is Mixtral 8x7B Instruct at about 68 tok/s.
113 of 169 models run comfortably on NOVATECH AI Workstation (i9-14900K + RTX 5080).
See full NOVATECH AI Workstation (i9-14900K + RTX 5080) specs and pricingThe top models this device can run at 4-bit, ranked by fit and speed.
| Model | Grade | Speed | VRAM |
|---|---|---|---|
| Mixtral 8x7B InstructMistral AI | SS | 68.0 tok/s | 11.4 GB |
| DiffusionGemma 26B-A4BGoogle | SS | 73.7 tok/s | 10.5 GB |
| Gemma 4 26B-A4B ITGoogle | SS | 70.2 tok/s | 11.0 GB |
| North Mini CodeCohere | SS | 92.2 tok/s | 8.4 GB |
| Nemotron 3 Nano Omni |
Mixtral 8x7B Instruct is the highest-rated model that runs on NOVATECH AI Workstation (i9-14900K + RTX 5080) at 4-bit, at about 68 tok/s. In total, NOVATECH AI Workstation (i9-14900K + RTX 5080) can run 113 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.
| 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 |
| Llama 2 13B ChatMeta | SS | 91.3 tok/s | 8.5 GB |
| Qwen3-30B-A3BAlibaba | SS | 143.5 tok/s | 5.4 GB |
| Carnice-9b for Hermes agentkai-os | SS | 128.5 tok/s | 6.0 GB |
| Llama 3 8B InstructMeta | SS | 136.4 tok/s | 5.7 GB |
| Gemma 4 E4B ITGoogle | SS | 111.7 tok/s | 6.9 GB |
Mixtral 8x7B InstructMistral AI | 46.7B(12.9B active) | SS | 68.0 tok/s | 11.4 GB | |
DiffusionGemma 26B-A4BGoogle | 25.2B(3.8B active) | SS | 73.7 tok/s | 10.5 GB | |
Gemma 4 26B-A4B ITGoogle | 26B(4B active) | SS | 70.2 tok/s | 11.0 GB | |
North Mini CodeCohere | 30B(3B active) | SS | 92.2 tok/s | 8.4 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 | |
Llama 2 13B ChatMeta | 13B | SS | 91.3 tok/s | 8.5 GB | |
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Qwen3-30B-A3BAlibaba | 30B(3B active) | SS | 143.5 tok/s | 5.4 GB | |
| 9B | SS | 128.5 tok/s | 6.0 GB | ||
| 8B | SS | 136.4 tok/s | 5.7 GB | ||
Gemma 4 E4B ITGoogle | 4B | SS | 111.7 tok/s | 6.9 GB | |
Gemma 3 4B ITGoogle | 4B | SS | 111.7 tok/s | 6.9 GB | |
Mistral 7B InstructMistral AI | 7B | SS | 120.8 tok/s | 6.4 GB | |
LFM2.5-8B-A1BLiquid AI | 8.3B(1.5B active) | AA | 265.9 tok/s | 2.9 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 | |
| 8B | AA | 58.0 tok/s | 13.3 GB | ||
VibeThinker-3BWeiboAI | 3B | AA | 202.7 tok/s | 3.8 GB | |
Gemma 4 E2B ITGoogle | 2B | AA | 208.4 tok/s | 3.7 GB | |
Qwen3.5-9BAlibaba | 9B | FF | 31.4 tok/s | 24.6 GB | |
Gemma 4 12BGoogle | 12B | FF | 24.1 tok/s | 32.0 GB | |
Gemma 4 12B Coderyuxinlu1 | 12B | FF | 24.1 tok/s | 32.0 GB | |
Mistral Small 3 24BMistral AI | 24B | FF | 19.8 tok/s | 39.0 GB | |
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Carnice-V2-27bkai-os | 27B | FF | 10.6 tok/s | 72.8 GB | |
Model Compatibility
Use the calculator to weigh this device against any other, model by model, with speed and fit scores.
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.
