NVIDIA GeForce RTX 5090 Laptop GPU — Local LLM Performance & Compatibility

Written by Jakub Rusinowski · Last updated September 19, 2026

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.

Technical Specifications

VRAM24 GB
Memory Bandwidth896 GB/s
TDP150 W
ArchitectureBlackwell GB203
Release Year2025
MSRP at Launch$0
Inference Speed (Llama 3.1 8B Q4_K_M)70–145 tok/s (estimated)
Inference Speed (Llama 3.3 70B Q4_K_M)Does not fit — needs ~44 GB of 24 GB usable
Buy This HardwareAMD Radeon RX 7900 XTX 24GB — 24 GB VRAM · 355 W board powerDeploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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LLMs Compatible with 24 GB VRAM

All models below run comfortably in 24 GB VRAM with Q4_K_M quantization.

Command R FamilyCommand R (35B) · 22 GB VRAM · Q4_K_M · ollama run command-r
Qwen 3.5Qwen 3.5 35B-A3B · 22 GB VRAM · Q4_K_M · ollama run qwen3.5:35b-a3b
Qwen 3.6Qwen 3.6 35B-A3B · 22 GB VRAM · Q4_K_M · ollama run qwen3.6:35b-a3b
Nex-N2Nex-N2 mini · 22 GB VRAM · Q4_K_M · nex-n2
Yi 1.5 FamilyYi 1.5 34B Chat · 22 GB VRAM · Q4_K_M · ollama run yi:34b
Qwen 3Qwen 3 32B · 21 GB VRAM · Q4_K_M · ollama run qwen3:32b
Aya ExpanseAya Expanse 32B · 20 GB VRAM · Q4_K_M · ollama run aya-expanse:32b
DeepSeek R1DeepSeek R1 Distill Qwen 32B · 20 GB VRAM · Q4_K_M · ollama run deepseek-r1:32b

56 more families also fit 24 GB — browse the full model library.

Best Use Cases

FAQ

Can the NVIDIA GeForce RTX 5090 Laptop GPU run local LLMs?

Yes — the NVIDIA GeForce RTX 5090 Laptop GPU has 24 GB VRAM and runs 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 mem

How fast is the NVIDIA GeForce RTX 5090 Laptop GPU for AI inference?

The NVIDIA GeForce RTX 5090 Laptop GPU is estimated to run Llama 3.1 8B at 70–145 tok/s with Q4_K_M quantization. Llama 3.3 70B does not fit: it needs about 44 GB against 24 GB usable. These are modelled estimates, not measurements — see /en/methodology.

What LLMs can I run on 24 GB VRAM?

With 24 GB you can run: Command R Family, Qwen 3.5, Qwen 3.6, Nex-N2, Yi 1.5 Family. Use Ollama for the easiest setup: ollama run llama3.1:8b.

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