Every open model in our directory, ranked by how well it runs on NVIDIA GeForce RTX 4070 SUPER (12GB) at 4-bit. Grades weigh whether the model fits in memory and how fast it should run.
NVIDIA GeForce RTX 4070 SUPER has 12 GB of VRAM, enough to run 102 of the 169 open models we track at 4-bit. The largest that still fits well is Qwen3.6 35B-A3B (35B), and the top-rated is North Mini Code at about 48 tok/s.
102 of 169 models run comfortably on NVIDIA GeForce RTX 4070 SUPER.
See full NVIDIA GeForce RTX 4070 SUPER specs and pricingThe top models this device can run at 4-bit, ranked by fit and speed.
| Model | Grade | Speed | VRAM |
|---|---|---|---|
| North Mini CodeCohere | SS | 48.4 tok/s | 8.4 GB |
| Nemotron 3 Nano OmniNVIDIA | SS | 47.6 tok/s | 8.5 GB |
| Qwen3.6 35B-A3BAlibaba | SS | 47.6 tok/s | 8.5 GB |
| Qwen3.5-35B-A3BAlibaba | SS | 47.6 tok/s | 8.5 GB |
| Qwen3-30B-A3B |
North Mini Code is the highest-rated model that runs on NVIDIA GeForce RTX 4070 SUPER at 4-bit, at about 48 tok/s. In total, NVIDIA GeForce RTX 4070 SUPER can run 102 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 |
| 75.3 tok/s |
| 5.4 GB |
| Llama 2 13B ChatMeta | SS | 47.9 tok/s | 8.5 GB |
| Carnice-9b for Hermes agentkai-os | SS | 67.5 tok/s | 6.0 GB |
| Llama 3 8B InstructMeta | SS | 71.6 tok/s | 5.7 GB |
| Gemma 4 E4B ITGoogle | SS | 58.7 tok/s | 6.9 GB |
| Gemma 3 4B ITGoogle | SS | 58.7 tok/s | 6.9 GB |
| Mistral 7B InstructMistral AI | SS | 63.4 tok/s | 6.4 GB |
| PersonaPlex 7BNVIDIA | SS | 84.7 tok/s | 4.8 GB |
North Mini CodeCohere | 30B(3B active) | SS | 48.4 tok/s | 8.4 GB | |
Nemotron 3 Nano OmniNVIDIA | 30B(3B active) | SS | 47.6 tok/s | 8.5 GB | |
Qwen3.6 35B-A3BAlibaba | 35B(3B active) | SS | 47.6 tok/s | 8.5 GB | |
Qwen3.5-35B-A3BAlibaba | 35B(3B active) | SS | 47.6 tok/s | 8.5 GB | |
Qwen3-30B-A3BAlibaba | 30B(3B active) | SS | 75.3 tok/s | 5.4 GB | |
Llama 2 13B ChatMeta | 13B | SS | 47.9 tok/s | 8.5 GB | |
| 9B | SS | 67.5 tok/s | 6.0 GB | ||
| 8B | SS | 71.6 tok/s | 5.7 GB | ||
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Gemma 4 E4B ITGoogle | 4B | SS | 58.7 tok/s | 6.9 GB | |
Gemma 3 4B ITGoogle | 4B | SS | 58.7 tok/s | 6.9 GB | |
Mistral 7B InstructMistral AI | 7B | SS | 63.4 tok/s | 6.4 GB | |
PersonaPlex 7BNVIDIA | 7B | SS | 84.7 tok/s | 4.8 GB | |
Llama 2 7B ChatMeta | 7B | SS | 84.7 tok/s | 4.8 GB | |
LFM2.5-8B-A1BLiquid AI | 8.3B(1.5B active) | SS | 139.6 tok/s | 2.9 GB | |
DiffusionGemma 26B-A4BGoogle | 25.2B(3.8B active) | SS | 38.7 tok/s | 10.5 GB | |
VibeThinker-3BWeiboAI | 3B | AA | 106.4 tok/s | 3.8 GB | |
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Gemma 4 E2B ITGoogle | 2B | AA | 109.4 tok/s | 3.7 GB | |
Mixtral 8x7B InstructMistral AI | 46.7B(12.9B active) | AA | 35.7 tok/s | 11.4 GB | |
Gemma 4 26B-A4B ITGoogle | 26B(4B active) | AA | 36.8 tok/s | 11.0 GB | |
| 8B | FF | 30.4 tok/s | 13.3 GB | ||
Qwen3.5-9BAlibaba | 9B | FF | 16.5 tok/s | 24.6 GB | |
Gemma 4 12BGoogle | 12B | FF | 12.7 tok/s | 32.0 GB | |
Gemma 4 12B Coderyuxinlu1 | 12B | FF | 12.7 tok/s | 32.0 GB | |
Mistral Small 3 24BMistral AI | 24B | FF | 10.4 tok/s | 39.0 GB | |
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Carnice-V2-27bkai-os | 27B | FF | 5.6 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.
