Build: NVIDIA GeForce RTX 5070 Ti + — + 32 GB RAM (windows)
Ranked by fit for reasoning use case + predicted speed. Click a row for VRAM breakdown.
ollama run RefinedNeuro/RN_TR_R1:latestollama run RefinedNeuro/RN_TR_R2:latestTight VRAM, partial CPU offload, or context-limited.
ollama run deepseek-r1:7bollama run phi4-reasoning:14bollama run deepseek-r1:14bollama run deepseek-r1:32bHypothetical scenarios. We re-ran the compatibility engine for each.
~$80–150
Doubles your CPU-offload working set. Helps when models don't quite fit in VRAM.
Unlocks: 98 new comfortable, 89 new tradeoff
~$350
22 GB VRAM (vs your 16 GB) plus a bandwidth jump from ~896 GB/s to ~616 GB/s.
Unlocks: 126 new comfortable
~$849
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: 160 new comfortable
Some links above are affiliate links. We may earn a commission at no extra cost to you. How we make money.
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.
Want a specific benchmark we don't have? Email Contact support and we'll prioritize it.