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Best local AI models for an RTX 4070 12 GB
An NVIDIA GeForce RTX 4070 12 GB card has about 10.8 GB of VRAM free for a model after the driver and context cache. These are the most downloaded open language and vision-language models that fit, for Ollama, llama.cpp, LM Studio or vLLM.
12 GB VRAM · about 10.8 GB usable for a model
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Last updated 3 November 2026 · refreshed after every Hugging Face sync · 15 models
| # | Model | Size | Memory | License | Downloads (30 days) | Likes | Est. speed (Q4) |
|---|---|---|---|---|---|---|---|
| 1 | Qwen3-4B Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 8.4M | 782 | – |
| 2 | Qwen3.5-4B Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 6.4M | 1.2K | ~131 tok/s |
| 3 | Qwen2.5-1.5B-Instruct Qwen | 1.5B · FP16 | ~4 GB | Apache 2.0 | 4.6M | 928 | ~259 tok/s |
| 4 | Ornith-1.5-9B-GGUF ornith-ai | 9B · GGUF | ~6 GB | MIT | 5.5M | 626 | – |
| 5 | Qwen3-1.7B Qwen | 1.7B · FP16 | ~5 GB | Apache 2.0 | 4.3M | 612 | – |
| 6 | Qwen3-VL-4B-Instruct Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 3.9M | 523 | – |
| 7 | Qwen3-4B-Instruct-2507 Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 2.7M | 1K | – |
| 8 | Qwen2.5-3B-Instruct Qwen | 3B · FP16 | ~8 GB | Custom (other) | 3M | 633 | ~165 tok/s |
| 9 | Ornith-1.0-9B-GGUF ornith-ai | 9B · GGUF | ~6 GB | MIT | 2.4M | 699 | – |
| 10 | Llama-3.2-3B-Instruct meta-llama | 3B · FP16 | ~8 GB | Llama 3.2 license | 969.6K | 2.9K | ~143 tok/s |
| 11 | MiniCPM5-2B openbmb | 2B · FP16 | ~5 GB | Apache 2.0 | 1.1M | 1.8K | – |
| 12 | DeepSeek-R1-Distill-Qwen-1.5B deepseek-ai | 1.5B · FP16 | ~4 GB | MIT | 784.6K | 1.7K | – |
| 13 | NVIDIA-Nemotron-3-Nano-4B-BF16 nvidia | 4B · FP16 | ~10 GB | Custom (other) | 1.9M | 142 | – |
| 14 | Qwen3-14B-AWQ Qwen | 14B · AWQ | ~9 GB | Apache 2.0 | 1.9M | 89 | ~44 tok/s |
| 15 | LocateAnything-3B nvidia | 3B · FP16 | ~8 GB | Custom (other) | 322.5K | 3.2K | – |
How we rank
Language and vision-language models in GGUF, FP16, AWQ, GPTQ format whose published weights need about 3–10.8 GB. We include models seen on Hugging Face in the last 14 days with at least 10,000 downloads in the last 30 days. Score = log10(30-day downloads) + ½ × log10(likes + 1), so popularity leads and likes break near-ties. We keep one entry per model name and leave out adult and uncensored builds. Memory is estimated from parameter count and the published file format (16-bit ≈ 2 bytes per parameter, 4-bit ≈ 0.5) plus 20% headroom; long contexts need more. A quantised copy of a bigger model may also fit — check the model page.
Speed data: ModelFit (CC BY 4.0), from its open dataset, refreshed weekly. These are modelled estimates of decode speed for a 4-bit (Q4) build of the same model at a 4K context, not measured benchmarks, and they are not an endorsement by ModelFit. A dash means ModelFit has no estimate for that model.