NVIDIA GeForce RTX 4090 Laptop GPU — Local LLM Performance & Compatibility
作者： Jakub Rusinowski · 最后更新： 2026年9月19日
AD103核心——与桌面版RTX 4080相同的硅片，而不是桌面版4090。256位总线上的16 GB GDDR6提供576 GB/s，而桌面版4090为24 GB、1008 GB/s。笔记本厂商可在80 W到150 W之间配置，因此贴着同样标签的两台机器吞吐量可相差三分之一。
Technical Specifications
| VRAM | 16 GB |
| Memory Bandwidth | 576 GB/s |
| TDP | 150 W |
| Architecture | Ada Lovelace AD103 |
| Release Year | 2023 |
| MSRP at Launch | $0 |
| Inference Speed (Llama 3.1 8B Q4_K_M) | 56–107 tok/s (estimated) |
| Inference Speed (Llama 3.3 70B Q4_K_M) | Does not fit — needs ~44 GB of 16 GB usable |
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LLMs Compatible with 16 GB VRAM
All models below run comfortably in 16 GB VRAM with Q4_K_M quantization.
| Mistral Family | Mistral Small 3 (24B) · 15 GB VRAM · Q4_K_M · ollama run mistral-small |
| Magistral Small | Magistral Small 24B · 15 GB VRAM · Q4_K_M · ollama run magistral:24b |
| Mistral Small 3.1 | Mistral Small 3.1 24B · 15 GB VRAM · Q4_K_M · ollama run mistral-small3.1 |
| Mistral Small 3.2 | Mistral Small 3.2 24B · 15 GB VRAM · Q4_K_M · ollama run mistral-small:24b |
| Codestral | Codestral 22B · 14 GB VRAM · Q4_K_M · ollama run codestral:22b |
| EuroLLM | EuroLLM 22B · 14 GB VRAM · Q4_K_M · eurollm |
| InternLM 3 | InternLM 3 20B Instruct · 13 GB VRAM · Q4_K_M · ollama run internlm3:20b |
| StarCoder 2 | StarCoder 2 15B · 10 GB VRAM · Q4_K_M · ollama run starcoder2:15b |
42 more families also fit 16 GB — browse the full model library.
Best Use Cases
- 16 GB laptop AI
- portable inference
- gaming laptops
FAQ
Can the NVIDIA GeForce RTX 4090 Laptop GPU run local LLMs?
Yes — the NVIDIA GeForce RTX 4090 Laptop GPU has 16 GB VRAM and runs AD103核心——与桌面版RTX 4080相同的硅片，而不是桌面版4090。256位总线上的16 GB GDDR6提供576 GB/s，而桌面版4090为24 GB、1008 GB/s。笔记本厂商可在80 W到150 W之间配置，因此贴着同
How fast is the NVIDIA GeForce RTX 4090 Laptop GPU for AI inference?
The NVIDIA GeForce RTX 4090 Laptop GPU is estimated to run Llama 3.1 8B at 56–107 tok/s with Q4_K_M quantization. Llama 3.3 70B does not fit: it needs about 44 GB against 16 GB usable. These are modelled estimates, not measurements — see /en/methodology.
What LLMs can I run on 16 GB VRAM?
With 16 GB you can run: Mistral Family, Magistral Small, Mistral Small 3.1, Mistral Small 3.2, Codestral. Use Ollama for the easiest setup: ollama run llama3.1:8b.
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VRAM Tier
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