What can NVIDIA GeForce RTX 5090 Mobile run?
Build: NVIDIA GeForce RTX 5090 Mobile + — + 32 GB RAM (windows)
Runs comfortably130 models
Full-VRAM resident, with room for context. No compromises.
Quant: Q4_K_MContext: 8,192VRAM: 2.4 GBHeadroom: 21.6 GB1705tok/sEstimated
Quant: FP16Context: 8,192VRAM: 19.8 GBHeadroom: 4.2 GBollama run llama3.1:8b39tok/sEstimated
ollama run llama3.1:8bQuant: Q8_0Context: 8,192VRAM: 14.4 GBHeadroom: 9.6 GBollama run qwen3:8b73tok/sEstimated
ollama run qwen3:8bQuant: Q4_K_MContext: 8,192VRAM: 3.6 GBHeadroom: 20.4 GB602tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 17.6 GBHeadroom: 6.4 GBollama run qwen3:14b73tok/sEstimated
ollama run qwen3:14bQuant: Q8_0Context: 8,192VRAM: 6.9 GBHeadroom: 17.1 GBollama run llama3.2:3b194tok/sEstimated
ollama run llama3.2:3bQuant: Q4_K_MContext: 8,192VRAM: 2.1 GBHeadroom: 21.9 GB3788tok/sEstimated
Quant: Q8_0Context: 8,192VRAM: 10.8 GBHeadroom: 13.2 GBollama run qwen2.5:7b83tok/sEstimated
ollama run qwen2.5:7bQuant: Q4_K_MContext: 2,048VRAM: 19.5 GBHeadroom: 4.5 GBollama run mistral-small:24b43tok/sEstimated
ollama run mistral-small:24bQuant: Q8_0Context: 8,192VRAM: 13.8 GBHeadroom: 10.2 GBollama run deepseek-r1:7b83tok/sEstimated
ollama run deepseek-r1:7bQuant: Q4_K_MContext: 8,192VRAM: 17.6 GBHeadroom: 6.4 GBollama run phi4:14b73tok/sEstimated
ollama run phi4:14bQuant: Q5_K_MContext: 8,192VRAM: 19.8 GBHeadroom: 4.2 GBollama run qwen2.5:14b64tok/sEstimated
ollama run qwen2.5:14bRuns with tradeoffs32 models
Tight VRAM, partial CPU offload, or context-limited.
Quant: Q4_K_MContext: 8,192VRAM: 35.7 GBHeadroom: 7.5 GB- • Partial CPU offload: ~33% of layers run on CPU
ollama run qwen3:30b34tok/sEstimated
- • Partial CPU offload: ~33% of layers run on CPU
ollama run qwen3:30bQuant: Q4_K_MContext: 8,192VRAM: 23.9 GBHeadroom: 0.1 GB- • Tight VRAM fit — only 0.1 GB headroom left for context growth
ollama run qwen2.5-coder:32b32tok/sEstimated
- • Tight VRAM fit — only 0.1 GB headroom left for context growth
ollama run qwen2.5-coder:32bQuant: Q4_K_MContext: 8,192VRAM: 37.8 GBHeadroom: 5.4 GB- • Partial CPU offload: ~36% of layers run on CPU
ollama run qwen3:32b32tok/sEstimated
- • Partial CPU offload: ~36% of layers run on CPU
ollama run qwen3:32bQuant: Q4_K_MContext: 8,192VRAM: 36.2 GBHeadroom: 7.0 GB- • Partial CPU offload: ~34% of layers run on CPU
ollama run gemma4:31b33tok/sEstimated
- • Partial CPU offload: ~34% of layers run on CPU
ollama run gemma4:31bQuant: Q4_K_MContext: 8,192VRAM: 37.8 GBHeadroom: 5.4 GB- • Partial CPU offload: ~36% of layers run on CPU
ollama run deepseek-r1:32b32tok/sEstimated
- • Partial CPU offload: ~36% of layers run on CPU
ollama run deepseek-r1:32bQuant: Q4_K_MContext: 2,048VRAM: 21.9 GBHeadroom: 2.1 GB- • Tight VRAM fit — only 2.1 GB headroom left for context growth
ollama run gemma4:26b-moe39tok/sEstimated
- • Tight VRAM fit — only 2.1 GB headroom left for context growth
ollama run gemma4:26b-moeQuant: Q4_K_MContext: 8,192VRAM: 35.7 GBHeadroom: 7.5 GB- • Partial CPU offload: ~33% of layers run on CPU
ollama run nemotron3:nano34tok/sEstimated
- • Partial CPU offload: ~33% of layers run on CPU
ollama run nemotron3:nanoQuant: Q4_K_MContext: 2,048VRAM: 22.0 GBHeadroom: 2.0 GB- • Tight VRAM fit — only 2.0 GB headroom left for context growth
ollama run gemma3:27b38tok/sEstimated
- • Tight VRAM fit — only 2.0 GB headroom left for context growth
ollama run gemma3:27bWhat if you upgraded?
Hypothetical scenarios. We re-ran the compatibility engine for each.
+32 GB system RAM
Check the current price
Adds 32 GB to your CPU-offload working set. Helps when models don't quite fit in VRAM.
Unlocks: 48 new tradeoff
- • Qwen 3 30B-A3B
- • Llama 3.3 70B Instruct
- • Qwen 2.5 Coder 32B Instruct
- • Qwen 3 32B
Upgrade to NVIDIA GeForce RTX 5090
Launch MSRP $1,999 (2025). Check the current price.
32 GB VRAM (vs your 24 GB) plus a bandwidth jump from ~? GB/s to ~1792 GB/s.
Unlocks: 24 new comfortable
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
- • Qwen 3 32B
- • Gemma 4 31B Dense
Add a second NVIDIA GeForce RTX 5090 Mobile
Check the current price
Tensor parallelism splits the model across both cards, effectively doubling VRAM. Bandwidth doesn't double — runs ~1.5× the single-card speed in practice.
Unlocks: 32 new comfortable
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
- • Qwen 3 32B
- • Gemma 4 31B Dense
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Won't runtop 5 popular models
Need more memory than you have. Shown for orientation.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
How to read these numbers
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