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.
| VRAM | 24 GB |
| Memory Bandwidth | 896 GB/s |
| TDP | 150 W |
| Architecture | Blackwell GB203 |
| Release Year | 2025 |
| 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 |
or compare on Vast.ai from $0.35/hr (typical low · varies)
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All models below run comfortably in 24 GB VRAM with Q4_K_M quantization.
| Command R Family | Command R (35B) · 22 GB VRAM · Q4_K_M · ollama run command-r |
| Qwen 3.5 | Qwen 3.5 35B-A3B · 22 GB VRAM · Q4_K_M · ollama run qwen3.5:35b-a3b |
| Qwen 3.6 | Qwen 3.6 35B-A3B · 22 GB VRAM · Q4_K_M · ollama run qwen3.6:35b-a3b |
| Nex-N2 | Nex-N2 mini · 22 GB VRAM · Q4_K_M · nex-n2 |
| Yi 1.5 Family | Yi 1.5 34B Chat · 22 GB VRAM · Q4_K_M · ollama run yi:34b |
| Qwen 3 | Qwen 3 32B · 21 GB VRAM · Q4_K_M · ollama run qwen3:32b |
| Aya Expanse | Aya Expanse 32B · 20 GB VRAM · Q4_K_M · ollama run aya-expanse:32b |
| DeepSeek R1 | DeepSeek 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.
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
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.
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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