What can NVIDIA GeForce RTX 5090 Mobile run for coding?
Build: NVIDIA GeForce RTX 5090 Mobile + — + 32 GB RAM (windows)
Runs comfortably102 models
Ranked by fit for coding use case + predicted speed. Click a row for VRAM breakdown.
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 14.3 GBollama run codegemma:7b146tok/sEstimated
ollama run codegemma:7bQuant: Q4_K_MContext: 8,192VRAM: 19.8 GBHeadroom: 4.2 GBollama run deepseek-coder-v2:16b64tok/sEstimated
ollama run deepseek-coder-v2:16bQuant: Q4_K_MContext: 2,048VRAM: 18.2 GBHeadroom: 5.8 GBollama run codestral:22b46tok/sEstimated
ollama run codestral:22bQuant: Q4_K_MContext: 8,192VRAM: 17.6 GBHeadroom: 6.4 GBollama run qwen3:14b73tok/sEstimated
ollama run qwen3:14bQuant: Q5_K_MContext: 8,192VRAM: 19.8 GBHeadroom: 4.2 GBollama run qwen2.5:14b64tok/sEstimated
ollama run qwen2.5:14bQuant: Q8_0Context: 8,192VRAM: 10.8 GBHeadroom: 13.2 GBollama run qwen2.5:7b83tok/sEstimated
ollama run qwen2.5:7bQuant: Q8_0Context: 8,192VRAM: 14.4 GBHeadroom: 9.6 GBollama run qwen3:8b73tok/sEstimated
ollama run qwen3:8bQuant: Q4_K_MContext: 2,048VRAM: 19.5 GBHeadroom: 4.5 GBollama run mistral-small:24b43tok/sEstimated
ollama run mistral-small:24bQuant: FP16Context: 8,192VRAM: 19.8 GBHeadroom: 4.2 GBollama run llama3.1:8b39tok/sEstimated
ollama run llama3.1:8bQuant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 14.3 GB146tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 10.7 GBHeadroom: 13.3 GB128tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 5.3 GBHeadroom: 18.7 GB341tok/sEstimated
Runs with tradeoffs32 models
Tight VRAM, partial CPU offload, or context-limited.
Quant: 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: 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: 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 qwen3:32b32tok/sEstimated
- • Partial CPU offload: ~36% of layers run on CPU
ollama run qwen3:32bQuant: Q4_K_MContext: 8,192VRAM: 37.8 GBHeadroom: 5.4 GB- • Partial CPU offload: ~36% of layers run on CPU
ollama run qwen2.5:32b32tok/sEstimated
- • Partial CPU offload: ~36% of layers run on CPU
ollama run qwen2.5:32bQuant: Q4_K_MContext: 2,048VRAM: 22.5 GBHeadroom: 1.5 GB- • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run qwen3.8:27b38tok/sEstimated
- • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run qwen3.8:27bQuant: AWQ-INT4Context: 8,192VRAM: 37.8 GBHeadroom: 5.4 GB- • Partial CPU offload: ~36% of layers run on CPU
19tok/sEstimated
- • Partial CPU offload: ~36% of layers run on CPU
Quant: AWQ-INT4Context: 8,192VRAM: 38.3 GBHeadroom: 4.9 GB- • Partial CPU offload: ~37% of layers run on CPU
19tok/sEstimated
- • Partial CPU offload: ~37% of layers run on CPU
What 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: 28 new comfortable, 48 new tradeoff
- • Qwen 3 0.6B
- • Qwen 3 1.7B
- • Gemma 3 270M
- • SmolLM2 135M Instruct
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: 52 new comfortable
- • Qwen 3 0.6B
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
- • Qwen 3 32B
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: 60 new comfortable
- • Qwen 3 0.6B
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
- • Qwen 3 32B
Some links above are affiliate links. We may earn a commission at no extra cost to you. How we make money.
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
Want a specific benchmark we don't have? Email Contact support and we'll prioritize it.