What can NVIDIA GeForce RTX 3090 run for reasoning?
Build: RTX 3090 + Ryzen 9 5950X + 64GB DDR4 (used market)
Runs comfortably120 models
Ranked by fit for reasoning use case + predicted speed. Click a row for VRAM breakdown.
Quant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 13.0 GB126tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 13.0 GBollama run RefinedNeuro/RN_TR_R1:latest126tok/sEstimated
ollama run RefinedNeuro/RN_TR_R1:latestQuant: Q4_K_MContext: 8,192VRAM: 10.7 GBHeadroom: 13.3 GB126tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 11.6 GBHeadroom: 12.4 GB112tok/sEstimated
Quant: Q8_0Context: 8,192VRAM: 13.8 GBHeadroom: 10.2 GBollama run deepseek-r1:7b82tok/sEstimated
ollama run deepseek-r1:7bQuant: Q4_K_MContext: 8,192VRAM: 17.6 GBHeadroom: 6.4 GBollama run phi4-reasoning:14b72tok/sEstimated
ollama run phi4-reasoning:14bQuant: Q4_K_MContext: 8,192VRAM: 17.6 GBHeadroom: 6.4 GBollama run deepseek-r1:14b72tok/sEstimated
ollama run deepseek-r1:14bQuant: Q4_K_MContext: 8,192VRAM: 19.8 GBHeadroom: 4.2 GB420tok/sEstimated
Quant: Q4_K_MContext: 2,048VRAM: 19.5 GBHeadroom: 4.5 GB42tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 17.6 GBHeadroom: 6.4 GBollama run phi4:14b72tok/sEstimated
ollama run phi4:14bQuant: Q4_K_MContext: 8,192VRAM: 10.2 GBHeadroom: 13.8 GB129tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 13.0 GBollama run RefinedNeuro/RN_TR_R2:latest126tok/sEstimated
ollama run RefinedNeuro/RN_TR_R2:latestRuns with tradeoffs73 models
Tight VRAM, partial CPU offload, or context-limited.
Quant: Q4_K_MContext: 2,048VRAM: 23.6 GBHeadroom: 0.4 GB- • Tight VRAM fit — only 0.4 GB headroom left for context growth
33tok/sEstimated
- • Tight VRAM fit — only 0.4 GB headroom left for context growth
Quant: Q4_K_MContext: 4,096VRAM: 21.7 GBHeadroom: 2.3 GB- • Tight VRAM fit — only 2.3 GB headroom left for context growth
42tok/sEstimated
- • Tight VRAM fit — only 2.3 GB headroom left for context growth
Quant: Q4_K_MContext: 8,192VRAM: 37.8 GBHeadroom: 24.6 GB- • Partial CPU offload: ~36% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwq:32b2tok/sEstimated
- • Partial CPU offload: ~36% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwq:32bQuant: Q4_K_MContext: 8,192VRAM: 36.3 GBHeadroom: 26.1 GB- • Partial CPU offload: ~34% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
2tok/sEstimated
- • Partial CPU offload: ~34% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
Quant: Q4_K_MContext: 8,192VRAM: 36.3 GBHeadroom: 26.1 GB- • Partial CPU offload: ~34% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
2tok/sEstimated
- • Partial CPU offload: ~34% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
Quant: AWQ-INT4Context: 8,192VRAM: 37.8 GBHeadroom: 24.6 GB- • Partial CPU offload: ~36% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
1tok/sEstimated
- • Partial CPU offload: ~36% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
Quant: AWQ-INT4Context: 8,192VRAM: 37.8 GBHeadroom: 24.6 GB- • Partial CPU offload: ~36% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
1tok/sEstimated
- • Partial CPU offload: ~36% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
Quant: AWQ-INT4Context: 8,192VRAM: 37.8 GBHeadroom: 24.6 GB- • Partial CPU offload: ~36% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
1tok/sEstimated
- • Partial CPU offload: ~36% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
What 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, 79 new tradeoff
- • Qwen 3 0.6B
- • Llama 3.2 3B Instruct
- • Qwen 3 1.7B
- • Gemma 3 270M
Upgrade to NVIDIA RTX PRO 4500 Blackwell
see current pricing
32 GB VRAM (vs your 24 GB) plus a bandwidth jump from ~936 GB/s to ~896 GB/s.
Unlocks: 103 new comfortable
- • Qwen 3 0.6B
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
- • Qwen3.6 27B
Add a second NVIDIA GeForce RTX 3090
~$899
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: 116 new comfortable
- • Qwen 3 0.6B
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
- • Qwen3.6 27B
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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 (38 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
How to read these numbers
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