NVIDIA GeForce RTX 5090 Laptop GPU for local LLMs
Written by Jakub Rusinowski · Last updated
With 24 GB of GDDR7 at 896 GB/s, the RTX 5090 Laptop GPU runs 121 catalogued models at Q4_K_M with 8K context. The largest that fits is Qwen 3 32B (~22.8 GB), and the top pick is Laguna XS 2.1 33B-A3B at 92–192 tok/s.
24 GB of GDDR7 on a 256-bit bus at 28 Gbps: 896 GB/s, against the desktop RTX 5090's 1,792 GB/s over 32 GB. The 3 GB memory modules are what made 24 GB possible in a laptop. 95-150 W by chassis, so the same model number varies by a third in throughput.
Models that run on the RTX 5090 Laptop GPU
Q4_K_M, 8K context, 24 GB usable. Ranked by quality and speed.
| Model | Memory · marker = 24 GB | VRAM | Speed |
|---|---|---|---|
| Laguna XS 2.1 33B-A3B Poolside Laguna XS 2.1 | 20.7 GB | 92–192 tok/s | |
| Granite 4.0 Small-H 32B-A9B IBM Granite 4.0 | 20.1 GB | 55–114 tok/s | |
| Olmo 3 32B Olmo 3 | 20.1 GB | 22–45 tok/s | |
| Nemotron-Cascade 2 30B-A3B Nemotron Cascade 2 | 19.9 GB | 88–182 tok/s | |
| Gemma 4 31B Gemma 4 | 21.4 GB | 22–46 tok/s | |
| Qwen 3 30B-A3B (MoE) Qwen 3 | 20 GB | 110–228 tok/s | |
| Qwen3-Coder 30B-A3B (MoE) Qwen3-Coder | 20 GB | 110–228 tok/s | |
| GLM-4.7-Flash 30B-A3B GLM-4.7 / GLM-Z1 | 18.9 GB | 93–194 tok/s | |
| Granite 4.1 30B IBM Granite 4.1 | 18.9 GB | 23–48 tok/s | |
| Nemotron 3 Nano Omni 30B-A3B Nemotron 3 Nano Omni | 18.9 GB | 93–194 tok/s | |
| North Mini Code 1.0 30B-A3B North Mini Code | 18.9 GB | 93–194 tok/s | |
| Granite 4.2 30B IBM Granite 4.2 | 18.9 GB | 23–48 tok/s |
Buy it or rent the same memory
Buy the card, or rent a GPU with the same memory by the hour to try models first.
or compare on Vast.ai from $0.35/hr (typical low · varies)
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Speed vs other GPUs
Llama 3.1 8B, Q4_K_M. Estimated ranges. How this is calculated
Specifications
Specs last updated 2026-09-19.
- Memory
- 24 GB GDDR7
- Memory bandwidth
- 896 GB/s
- Memory bus
- 256-bit
- Architecture
- Blackwell GB203
- Series
- RTX 50-series (Laptop)
- Board power
- 150 W
- Laptop TGP range
- 95–150 W
- Release year
- 2025
- Compute backends
- CUDA
- Usable for models
- 24 GB
Similar GPUs
Frequently asked questions
Can the NVIDIA GeForce RTX 5090 Laptop GPU run local LLMs?
Yes. With 24 GB (24 GB usable by a model) it runs 121 of the catalogued models at Q4_K_M with 8K context; the largest is Qwen 3 32B, needing about 22.8 GB.
How fast is the NVIDIA GeForce RTX 5090 Laptop GPU for AI inference?
It is estimated to run Llama 3.1 8B at 70–145 tok/s at Q4_K_M. Llama 3.3 70B does not fit: it needs about 44 GB against 24 GB usable. These are modelled estimates from memory bandwidth, not measurements; the methodology page shows the formula.
What LLMs can I run on 24 GB?
Among the best that fit: Laguna XS 2.1 33B-A3B, Granite 4.0 Small-H 32B-A9B, Olmo 3 32B, Nemotron-Cascade 2 30B-A3B, Gemma 4 31B.