What can NVIDIA GeForce RTX 3080 16GB (Mobile) run for reasoning?
Build: NVIDIA GeForce RTX 3080 16GB (Mobile) + — + 32 GB RAM (windows)
Runs comfortably48 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: noticeableollama run deepseek-r1:7b79tok/sEstimated
ollama run deepseek-r1:7bQuant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 5.0 GBTTFT: noticeable69tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 5.0 GBTTFT: noticeableollama run RefinedNeuro/RN_TR_R1:latest69tok/sEstimated
ollama run RefinedNeuro/RN_TR_R1:latestQuant: Q4_K_MContext: 8,192VRAM: 10.7 GBHeadroom: 5.3 GBTTFT: noticeable69tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 9.9 GBHeadroom: 6.1 GBTTFT: noticeable79tok/sEstimated
Quant: Q4_K_MContext: 4,096VRAM: 8.2 GBHeadroom: 7.8 GBTTFT: noticeable79tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 9.9 GBHeadroom: 6.1 GBTTFT: noticeable79tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 5.0 GBTTFT: noticeableollama run RefinedNeuro/RN_TR_R2:latest69tok/sEstimated
ollama run RefinedNeuro/RN_TR_R2:latestQuant: Q4_K_MContext: 8,192VRAM: 11.9 GBHeadroom: 4.1 GBTTFT: noticeable61tok/sEstimated
Quant: Q4_0Context: 8,192VRAM: 9.7 GBHeadroom: 6.3 GBTTFT: noticeableollama run brooqs/mistral-turkish-v2:latest82tok/sEstimated
ollama run brooqs/mistral-turkish-v2:latestQuant: Q4_K_MContext: 4,096VRAM: 7.6 GBHeadroom: 8.4 GBTTFT: noticeable79tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 9.4 GBHeadroom: 6.6 GBTTFT: noticeable79tok/sEstimated
Runs with tradeoffs62 models
Tight VRAM, partial CPU offload, or context-limited.
Quant: Q4_K_MContext: 2,048VRAM: 13.8 GBHeadroom: 2.2 GBTTFT: fast- • Tight VRAM fit — only 2.2 GB headroom left for context growth
230tok/sEstimated
- • Tight VRAM fit — only 2.2 GB headroom left for context growth
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:14b39tok/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-r1:14b39tok/sEstimated
- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run deepseek-r1:14bQuant: Q4_K_MContext: 8,192VRAM: 28.5 GBHeadroom: 6.7 GBTTFT: slow- • Partial CPU offload: ~44% of layers run on CPU
23tok/sEstimated
- • Partial CPU offload: ~44% of layers run on CPU
Quant: Q4_K_MContext: 2,048VRAM: 23.6 GBHeadroom: 11.6 GBTTFT: slow- • Partial CPU offload: ~32% of layers run on CPU
18tok/sEstimated
- • Partial CPU offload: ~32% of layers run on CPU
Quant: Q4_K_MContext: 2,048VRAM: 25.8 GBHeadroom: 9.4 GBTTFT: slow- • Partial CPU offload: ~38% of layers run on CPU
ollama run deepseek-r1:32b17tok/sEstimated
- • Partial CPU offload: ~38% of layers run on CPU
ollama run deepseek-r1:32bQuant: Q4_K_MContext: 2,048VRAM: 25.8 GBHeadroom: 9.4 GBTTFT: slow- • Partial CPU offload: ~38% of layers run on CPU
ollama run qwq:32b17tok/sEstimated
- • Partial CPU offload: ~38% of layers run on CPU
ollama run qwq:32bQuant: 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 phi4:14b39tok/sEstimated
- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run phi4:14bWhat 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: 49 new comfortable, 78 new tradeoff
- • Qwen 3 0.6B
- • Qwen 3 1.7B
- • Llama 3.2 3B Instruct
- • 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 ~512 GB/s to ~616 GB/s.
Unlocks: 68 new comfortable
- • Qwen 3 0.6B
- • Qwen 3 1.7B
- • Qwen 3 14B
- • Llama 3.2 3B Instruct
Add a second NVIDIA GeForce RTX 3080 16GB (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: 99 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.
—
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.
How to read these numbers
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