What can NVIDIA GeForce RTX 5060 Ti 16GB run?
Build: RTX 5060 Ti 16GB + Ryzen 5 9600X + 32GB DDR5
Runs comfortably142 models
Full-VRAM resident, with room for context. No compromises.
Quant: Q4_K_MContext: 8,192VRAM: 2.4 GBHeadroom: 13.6 GB804tok/sEstimated
Quant: Q8_0Context: 8,192VRAM: 11.8 GBHeadroom: 4.2 GBollama run llama3.1:8b34tok/sEstimated
ollama run llama3.1:8bQuant: Q4_K_MContext: 8,192VRAM: 10.8 GBHeadroom: 5.2 GBollama run qwen3:8b60tok/sEstimated
ollama run qwen3:8bQuant: Q8_0Context: 8,192VRAM: 6.9 GBHeadroom: 9.1 GBollama run llama3.2:3b91tok/sEstimated
ollama run llama3.2:3bQuant: Q4_K_MContext: 8,192VRAM: 3.6 GBHeadroom: 12.4 GB284tok/sEstimated
Quant: Q8_0Context: 8,192VRAM: 10.8 GBHeadroom: 5.2 GBollama run qwen2.5:7b39tok/sEstimated
ollama run qwen2.5:7bQuant: Q4_K_MContext: 8,192VRAM: 2.1 GBHeadroom: 13.9 GB1786tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 10.2 GBHeadroom: 5.8 GBollama run deepseek-r1:7b69tok/sEstimated
ollama run deepseek-r1:7bQuant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 5.0 GBollama run hermes3:8b60tok/sEstimated
ollama run hermes3:8bQuant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 4.9 GBollama run dolphin3:8b60tok/sEstimated
ollama run dolphin3:8bQuant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 7.6 GBollama run gemma4:e4b69tok/sEstimated
ollama run gemma4:e4bQuant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 7.6 GBollama run qwen3:4b69tok/sEstimated
ollama run qwen3:4bRuns with tradeoffs94 models
Tight VRAM, partial CPU offload, or context-limited.
Quant: Q4_K_MContext: 2,048VRAM: 24.5 GBHeadroom: 10.7 GB- • Partial CPU offload: ~35% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwen3:30b3tok/sEstimated
- • Partial CPU offload: ~35% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwen3:30bQuant: Q4_K_MContext: 8,192VRAM: 23.9 GBHeadroom: 11.3 GB- • Partial CPU offload: ~33% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwen2.5-coder:32b3tok/sEstimated
- • Partial CPU offload: ~33% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwen2.5-coder:32bQuant: Q4_K_MContext: 8,192VRAM: 33.2 GBHeadroom: 2.0 GB- • Partial CPU offload: ~52% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwen3.6:27b2tok/sEstimated
- • Partial CPU offload: ~52% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwen3.6:27bQuant: Q4_K_MContext: 2,048VRAM: 25.8 GBHeadroom: 9.4 GB- • Partial CPU offload: ~38% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwen3:32b2tok/sEstimated
- • Partial CPU offload: ~38% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwen3:32bQuant: Q4_K_MContext: 2,048VRAM: 24.6 GBHeadroom: 10.6 GB- • Partial CPU offload: ~35% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run gemma4:31b2tok/sEstimated
- • Partial CPU offload: ~35% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run gemma4:31bQuant: Q4_K_MContext: 8,192VRAM: 15.8 GBHeadroom: 0.2 GB- • Tight VRAM fit — only 0.2 GB headroom left for context growth
ollama run gemma4:12b40tok/sEstimated
- • Tight VRAM fit — only 0.2 GB headroom left for context growth
ollama run gemma4:12bQuant: Q4_K_MContext: 8,192VRAM: 13.2 GBHeadroom: 2.8 GB- • Tight VRAM fit — only 2.8 GB headroom left for context growth
ollama run qwen3.5:9b54tok/sEstimated
- • Tight VRAM fit — only 2.8 GB headroom left for context growth
ollama run qwen3.5:9bQuant: Q4_K_MContext: 2,048VRAM: 25.5 GBHeadroom: 9.7 GB- • Partial CPU offload: ~37% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwen3-coder:30b2tok/sEstimated
- • Partial CPU offload: ~37% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwen3-coder:30bWhat 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: 112 new tradeoff
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
- • Qwen3.6 27B
- • Llama 3.3 70B Instruct
Upgrade to NVIDIA RTX 2080 Ti 22GB (China-mod)
~$350
22 GB VRAM (vs your 16 GB) plus a bandwidth jump from ~448 GB/s to ~616 GB/s.
Unlocks: 27 new comfortable
- • Gemma 4 12B
- • Qwen3.5 9B
- • Qwen 3 14B
- • Phi-4 14B
Add a second NVIDIA GeForce RTX 5060 Ti 16GB
~$459
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: 74 new comfortable
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
- • Qwen3.6 27B
- • 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.
How to read these numbers
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