Written by Jakub Rusinowski · Last updated September 19, 2026
The AD103 die — the same silicon as the desktop RTX 4080, not the desktop 4090. 16 GB GDDR6 on a 256-bit bus gives 576 GB/s, against the desktop 4090's 1,008 GB/s over 24 GB. Configurable from 80 W to 150 W by the laptop maker, so two machines with the same sticker can differ by a third in throughput.
| 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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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.
Yes — the NVIDIA GeForce RTX 4090 Laptop GPU has 16 GB VRAM and runs The AD103 die — the same silicon as the desktop RTX 4080, not the desktop 4090. 16 GB GDDR6 on a 256-bit bus gives 576 G
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.
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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