What can NVIDIA GeForce RTX 5080 run for reasoning?
Build: RTX 5080 + Ryzen 9 9950X + 32GB DDR5
Runs comfortably42 models
Ranked by fit for reasoning use case + predicted speed. Click a row for VRAM breakdown.
Quant: Q4_K_MContext: 8,192VRAM: 10.2 GBHeadroom: 5.8 GBTTFT: fastollama run deepseek-r1:7b148tok/sEstimated
ollama run deepseek-r1:7bQuant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 5.0 GBTTFT: fast129tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 5.0 GBTTFT: fastollama run RefinedNeuro/RN_TR_R1:latest129tok/sEstimated
ollama run RefinedNeuro/RN_TR_R1:latestQuant: Q4_K_MContext: 8,192VRAM: 10.7 GBHeadroom: 5.3 GBTTFT: fast129tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 5.0 GBTTFT: fastollama run RefinedNeuro/RN_TR_R2:latest129tok/sEstimated
ollama run RefinedNeuro/RN_TR_R2:latestQuant: Q4_K_MContext: 4,096VRAM: 8.2 GBHeadroom: 7.8 GBTTFT: fast148tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 9.9 GBHeadroom: 6.1 GBTTFT: fast148tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 10.8 GBHeadroom: 5.2 GBTTFT: fastollama run qwen3:8b129tok/sEstimated
ollama run qwen3:8bQuant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 5.0 GBTTFT: fastollama run hermes3:8b129tok/sEstimated
ollama run hermes3:8bQuant: Q5_K_MContext: 8,192VRAM: 10.7 GBHeadroom: 5.3 GBTTFT: fastollama run mistral:7b130tok/sEstimated
ollama run mistral:7bQuant: Q4_K_MContext: 8,192VRAM: 10.4 GBHeadroom: 5.6 GBTTFT: fast129tok/sEstimated
Quant: Q4_K_MContext: 4,096VRAM: 7.6 GBHeadroom: 8.4 GBTTFT: fast148tok/sEstimated
Runs with tradeoffs46 models
Tight VRAM, partial CPU offload, or context-limited.
Quant: Q4_K_MContext: 2,048VRAM: 12.4 GBHeadroom: 3.6 GBTTFT: noticeable- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run phi4-reasoning:14b74tok/sEstimated
- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run phi4-reasoning:14bQuant: Q4_K_MContext: 2,048VRAM: 12.4 GBHeadroom: 3.6 GBTTFT: noticeable- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run deepseek-r1:14b74tok/sEstimated
- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run deepseek-r1:14bQuant: Q4_K_MContext: 2,048VRAM: 12.4 GBHeadroom: 3.6 GBTTFT: noticeable- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run phi4:14b74tok/sEstimated
- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run phi4:14bQuant: Q4_K_MContext: 2,048VRAM: 23.6 GBHeadroom: 11.6 GBTTFT: noticeable- • Partial CPU offload: ~32% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
5tok/sEstimated
- • Partial CPU offload: ~32% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
Quant: Q4_K_MContext: 2,048VRAM: 25.8 GBHeadroom: 9.4 GBTTFT: noticeable- • Partial CPU offload: ~38% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run deepseek-r1:32b4tok/sEstimated
- • Partial CPU offload: ~38% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run deepseek-r1:32bQuant: Q4_K_MContext: 2,048VRAM: 25.8 GBHeadroom: 9.4 GBTTFT: noticeable- • Partial CPU offload: ~38% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwq:32b4tok/sEstimated
- • Partial CPU offload: ~38% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwq:32bQuant: Q4_K_MContext: 2,048VRAM: 12.8 GBHeadroom: 3.2 GBTTFT: fast- • Tight VRAM fit — only 3.2 GB headroom left for context growth
431tok/sEstimated
- • Tight VRAM fit — only 3.2 GB headroom left for context growth
Quant: Q4_K_MContext: 4,096VRAM: 15.8 GBHeadroom: 0.2 GBTTFT: fast- • Tight VRAM fit — only 0.2 GB headroom left for context growth
431tok/sEstimated
- • Tight VRAM fit — only 0.2 GB headroom left for context growth
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: 46 new comfortable, 57 new tradeoff
- • Qwen 3 0.6B
- • Llama 3.2 3B Instruct
- • Qwen 3 1.7B
- • Gemma 3 270M
Upgrade to NVIDIA RTX 2080 Ti 22GB (China-mod)
Check the current price
22 GB VRAM (vs your 16 GB) plus a bandwidth jump from ~960 GB/s to ~616 GB/s.
Unlocks: 65 new comfortable
- • Qwen 3 0.6B
- • Qwen 3 14B
- • Llama 3.2 3B Instruct
- • Qwen 3 1.7B
Add a second NVIDIA GeForce RTX 5080
Launch MSRP $999 (2025). 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: 86 new comfortable
- • Qwen 3 0.6B
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
- • Qwen 3 32B
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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 (16 GB) + 60% of system RAM (19 GB) combined.
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Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
Not enough VRAM for these models? Rent a GPU by the hour
The models above need more memory than 16 GB VRAM + 32 GB system RAM. You can rent a GPU with enough VRAM by the hour to run one of them, or to test it before you buy hardware. For daily use, owning a card usually costs less over time.
RunPod
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The two tiers run on different hosts and terms. Check which tier a pod is on before a long job.
Rent on RunPodVast.ai
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How to read these numbers
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