What can NVIDIA GeForce RTX 5090 run for reasoning?
Build: RTX 5090 + Ryzen 9 9950X + 64GB DDR5
Runs comfortably152 models
Ranked by fit for reasoning use case + predicted speed. Click a row for VRAM breakdown.
Quant: Q8_0Context: 8,192VRAM: 13.8 GBHeadroom: 18.2 GBTTFT: fastollama run deepseek-r1:7b157tok/sEstimated
ollama run deepseek-r1:7bQuant: Q4_K_MContext: 8,192VRAM: 17.6 GBHeadroom: 14.4 GBTTFT: fastollama run phi4-reasoning:14b138tok/sEstimated
ollama run phi4-reasoning:14bQuant: Q4_K_MContext: 8,192VRAM: 17.6 GBHeadroom: 14.4 GBTTFT: fastollama run deepseek-r1:14b138tok/sEstimated
ollama run deepseek-r1:14bQuant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 21.0 GBTTFT: fast241tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 11.6 GBHeadroom: 20.4 GBTTFT: fast214tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 21.0 GBTTFT: fastollama run RefinedNeuro/RN_TR_R1:latest241tok/sEstimated
ollama run RefinedNeuro/RN_TR_R1:latestQuant: Q4_K_MContext: 8,192VRAM: 10.7 GBHeadroom: 21.3 GBTTFT: fast241tok/sEstimated
Quant: Q4_K_MContext: 2,048VRAM: 23.6 GBHeadroom: 8.4 GBTTFT: noticeable62tok/sEstimated
Quant: Q4_K_MContext: 2,048VRAM: 25.8 GBHeadroom: 6.2 GBTTFT: noticeableollama run deepseek-r1:32b60tok/sEstimated
ollama run deepseek-r1:32bQuant: Q4_K_MContext: 2,048VRAM: 25.8 GBHeadroom: 6.2 GBTTFT: noticeableollama run qwq:32b60tok/sEstimated
ollama run qwq:32bQuant: Q4_K_MContext: 2,048VRAM: 24.3 GBHeadroom: 7.7 GBTTFT: noticeable60tok/sEstimated
Quant: Q4_K_MContext: 2,048VRAM: 24.3 GBHeadroom: 7.7 GBTTFT: noticeable60tok/sEstimated
Runs with tradeoffs43 models
Tight VRAM, partial CPU offload, or context-limited.
Quant: Q4_K_MContext: 8,192VRAM: 28.5 GBHeadroom: 3.5 GBTTFT: fast- • Tight VRAM fit — only 3.5 GB headroom left for context growth
80tok/sEstimated
- • Tight VRAM fit — only 3.5 GB headroom left for context growth
Quant: Q4_K_MContext: 2,048VRAM: 51.0 GBHeadroom: 19.4 GBTTFT: noticeable- • Partial CPU offload: ~37% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
2tok/sEstimated
- • Partial CPU offload: ~37% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
Quant: Q4_K_MContext: 2,048VRAM: 52.6 GBHeadroom: 17.8 GBTTFT: noticeable- • Partial CPU offload: ~39% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run deepseek-r1:70b2tok/sEstimated
- • Partial CPU offload: ~39% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run deepseek-r1:70bQuant: Q4_K_MContext: 4,096VRAM: 28.3 GBHeadroom: 3.7 GBTTFT: noticeable- • Tight VRAM fit — only 3.7 GB headroom left for context growth
60tok/sEstimated
- • Tight VRAM fit — only 3.7 GB headroom left for context growth
Quant: Q4_K_MContext: 2,048VRAM: 31.4 GBHeadroom: 0.6 GBTTFT: noticeable- • Tight VRAM fit — only 0.6 GB headroom left for context growth
ollama run qwen3.6:35b-a3b55tok/sEstimated
- • Tight VRAM fit — only 0.6 GB headroom left for context growth
ollama run qwen3.6:35b-a3bQuant: Q4_K_MContext: 2,048VRAM: 31.4 GBHeadroom: 0.6 GBTTFT: noticeable- • Tight VRAM fit — only 0.6 GB headroom left for context growth
ollama run qwen3.5:35b-a3b55tok/sEstimated
- • Tight VRAM fit — only 0.6 GB headroom left for context growth
ollama run qwen3.5:35b-a3bQuant: Q4_K_MContext: 2,048VRAM: 28.2 GBHeadroom: 3.8 GBTTFT: noticeable- • Tight VRAM fit — only 3.8 GB headroom left for context growth
ollama run ornith:35b55tok/sEstimated
- • Tight VRAM fit — only 3.8 GB headroom left for context growth
ollama run ornith:35bQuant: Q4_K_MContext: 2,048VRAM: 28.2 GBHeadroom: 3.8 GBTTFT: noticeable- • Tight VRAM fit — only 3.8 GB headroom left for context growth
ollama run command-r:35b55tok/sEstimated
- • Tight VRAM fit — only 3.8 GB headroom left for context growth
ollama run command-r:35bWhat if you upgraded?
Hypothetical scenarios. We re-ran the compatibility engine for each.
+32 GB system RAM
~$80–150
Doubles your CPU-offload working set. Helps when models don't quite fit in VRAM.
Unlocks: 64 new comfortable, 48 new tradeoff
- • Qwen 3 0.6B
- • Llama 3.2 3B Instruct
- • Qwen 3 1.7B
- • Gemma 3 270M
Upgrade to NVIDIA A100 40GB
see current pricing
40 GB VRAM (vs your 32 GB) plus a bandwidth jump from ~1792 GB/s to ~1555 GB/s.
Unlocks: 85 new comfortable
- • Qwen 3 0.6B
- • Qwen3.6 35B-A3B
- • Llama 3.2 3B Instruct
- • Qwen 3 1.7B
Add a second NVIDIA GeForce RTX 5090
~$2499
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: 103 new comfortable
- • Qwen 3 0.6B
- • Llama 3.3 70B Instruct
- • DeepSeek R1 Distill Llama 70B
- • Qwen3.6 35B-A3B
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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 (32 GB) + 60% of system RAM (38 GB) combined.
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Even with CPU offload, needs more memory than your VRAM (32 GB) + 60% of system RAM (38 GB) combined.
Even with CPU offload, needs more memory than your VRAM (32 GB) + 60% of system RAM (38 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (32 GB) + 60% of system RAM (38 GB) combined.
Even with CPU offload, needs more memory than your VRAM (32 GB) + 60% of system RAM (38 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (32 GB) + 60% of system RAM (38 GB) combined.
Even with CPU offload, needs more memory than your VRAM (32 GB) + 60% of system RAM (38 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (32 GB) + 60% of system RAM (38 GB) combined.
Even with CPU offload, needs more memory than your VRAM (32 GB) + 60% of system RAM (38 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (32 GB) + 60% of system RAM (38 GB) combined.
How to read these numbers
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