Yes, and it is the first laptop GPU that genuinely reaches the high end of local AI. An RTX 5090 Laptop GPU can run local AI at a level no previous mobile chip matched, because it is the first to carry 24 GB of VRAM. On a 896 GB/s GDDR7 bus, that capacity puts a portable machine into the 32-billion-parameter tier that used to require a desktop with a 24 GB card.
What the RTX 5090 Laptop can run
VRAM is the capacity gate. At 4-bit quantization a 32-billion-parameter model occupies roughly 20 GB, which fits inside the 24 GB with room for context. The table below is computed with the same engine as the WillMyGPURunIt calculator and assumes a 32 GB DDR5 host system:
| DeepSeek-R1 Distill Qwen 32B | 33B | ~27 tok/s |
| Qwen2.5 32B | 33B | ~28 tok/s |
| Qwen2.5-Coder 32B | 33B | ~28 tok/s |
| QwQ 32B | 33B | ~28 tok/s |
| Qwen3 30B A3B | 31B | ~272 tok/s |
| Gemma 3 27B | 27B | ~28 tok/s |
| Mistral Small 24B | 24B | ~28 tok/s |
| Qwen2.5 14B | 15B | ~34 tok/s |
| Phi-4 (14.7B) | 15B | ~34 tok/s |
| Mistral Nemo 12B | 12B | ~41 tok/s |
| Gemma 3 12B | 12B | ~41 tok/s |
| Qwen3 8B | 8B | ~33 tok/s |
| Llama 3.1 8B | 8B | ~34 tok/s |
| Qwen2.5 7B | 8B | ~35 tok/s |
| Qwen2.5-Coder 7B | 8B | ~35 tok/s |
| Mistral 7B | 7B | ~37 tok/s |
| Gemma 3 4B | 4B | ~63 tok/s |
| Llama 3.2 3B | 3B | ~84 tok/s |
| Llama 3.2 1B | 1B | ~224 tok/s |
| Qwen2.5 0.5B | 0.5B | ~538 tok/s |
Larger models such as Qwen2.5 72B, Llama 3.3 70B, DeepSeek-R1 Distill Llama 70B will load only by offloading layers to system RAM, which runs them well below interactive speed.
The largest model the RTX 5090 Laptop holds fully in VRAM is around 34B, and a standard 8-billion-parameter model decodes at roughly 112 tokens per second. The 24 GB pool is the headline: it is the same capacity as a desktop RTX 3090 or 4090, which means a laptop can now hold 32B coding and reasoning models entirely on the GPU.
24 GB on a laptop changes the calculus
Until this generation, the most VRAM in a laptop was the 16 GB of the RTX 4090 Laptop, which capped portable local AI at the 13-to-14B tier. The RTX 5090 Laptop's 24 GB clears the 32B class, so the strongest open-weight models that fit on a single consumer card now run on a machine you can carry. For a developer who wants 32B coding assistance without a desktop, this is the chip that makes it possible.
The limits that remain
Even 24 GB has a ceiling. The 70-billion-parameter models need roughly 40 GB and must offload to system RAM, where they run far below interactive speed. And the usual laptop rules still apply. Total graphics power varies by chassis, so a thicker design sustains the speed above better under a long generation, and the VRAM is soldered for life. But within the single-card consumer range, the RTX 5090 Laptop leaves very little out of reach.
Is the RTX 5090 Laptop worth it for local AI?
If you need the largest portable local-AI machine, it is the clear choice. It holds 32B models on the GPU, runs everything below that fast, and does so on the well-supported NVIDIA CUDA platform with Ollama or LM Studio. For 7-to-8B chat work a cheaper laptop is plenty, but for serious local AI on the move the RTX 5090 Laptop is in a class of its own. Confirm specifics for your configuration in the calculator.