Best local AI models for an RTX 4090 Laptop 16 GB
A laptop with an NVIDIA GeForce RTX 4090 Laptop GPU 16 GB has about 14.4 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.
16 GB VRAM · about 14.4 GB usable for a model · ModelFit has no laptop-GPU data, so speeds are estimates for the desktop RTX 4080; a laptop chip runs at lower power, so expect somewhat less
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Last updated 4 November 2026 · refreshed after every Hugging Face sync · 15 models
| # | Model | Size | Memory | License | Downloads (30 days) | Likes |
|---|---|---|---|---|---|---|
| 1 | Qwen3-4B Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 8.4M | 783 |
| 2 | Qwen3.5-4B Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 6.4M | 1.2K |
| 3 | Ornith-1.5-9B-GGUF ornith-ai | 9B · GGUF | ~6 GB | MIT | 5.5M | 629 |
| 4 | Qwen3-1.7B Qwen | 1.7B · FP16 | ~5 GB | Apache 2.0 | 4.3M | 612 |
| 5 | Qwen3-VL-4B-Instruct Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 3.9M | 523 |
| 6 | Qwen3-4B-Instruct-2507 Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 2.7M | 1K |
| 7 | Qwen2.5-3B-Instruct Qwen | 3B · FP16 | ~8 GB | Custom (other) | 3M | 636 |
| 8 | Ornith-1.0-9B-GGUF ornith-ai | 9B · GGUF | ~6 GB | MIT | 2.4M | 699 |
| 9 | Llama-3.2-3B-Instruct meta-llama | 3B · FP16 | ~8 GB | Llama 3.2 license | 969.6K | 2.9K |
| 10 | MiniCPM5-2B openbmb | 2B · FP16 | ~5 GB | Apache 2.0 | 1.1M | 1.8K |
| 11 | NVIDIA-Nemotron-3-Nano-4B-BF16 nvidia | 4B · FP16 | ~10 GB | Custom (other) | 1.9M | 142 |
| 12 | Qwen3-14B-AWQ Qwen | 14B · AWQ | ~9 GB | Apache 2.0 | 1.9M | 89 |
| 13 | LocateAnything-3B nvidia | 3B · FP16 | ~8 GB | Custom (other) | 322.5K | 3.2K |
| 14 | Qwen3-8B-AWQ Qwen | 8B · AWQ | ~5 GB | Apache 2.0 | 2M | 70 |
| 15 | Qwen2.5-7B-Instruct-AWQ Qwen | 7B · AWQ | ~5 GB | Apache 2.0 | 1.8M | 61 |
How we rank
Language and vision-language models in GGUF, FP16, AWQ, GPTQ format whose published weights need about 4–14.4 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.