What can NVIDIA GeForce RTX 4060 Ti 16GB run?
Build: NVIDIA GeForce RTX 4060 Ti 16GB + — + 32 GB RAM (windows)
Runs comfortably88 models
Full-VRAM resident, with room for context. No compromises.
Quant: Q4_K_MContext: 8,192VRAM: 2.4 GBHeadroom: 13.6 GB718tok/sEstimated
Quant: Q8_0Context: 8,192VRAM: 11.8 GBHeadroom: 4.2 GBollama run llama3.1:8b31tok/sEstimated
ollama run llama3.1:8bQuant: Q4_K_MContext: 8,192VRAM: 10.8 GBHeadroom: 5.2 GBollama run qwen3:8b54tok/sEstimated
ollama run qwen3:8bQuant: Q8_0Context: 8,192VRAM: 6.9 GBHeadroom: 9.1 GBollama run llama3.2:3b82tok/sEstimated
ollama run llama3.2:3bQuant: Q4_K_MContext: 8,192VRAM: 3.6 GBHeadroom: 12.4 GB253tok/sEstimated
Quant: Q8_0Context: 8,192VRAM: 10.8 GBHeadroom: 5.2 GBollama run qwen2.5:7b35tok/sEstimated
ollama run qwen2.5:7bQuant: Q4_K_MContext: 8,192VRAM: 2.1 GBHeadroom: 13.9 GB1595tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 10.2 GBHeadroom: 5.8 GBollama run deepseek-r1:7b62tok/sEstimated
ollama run deepseek-r1:7bQuant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 5.0 GBollama run hermes3:8b54tok/sEstimated
ollama run hermes3:8bQuant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 7.6 GBollama run gemma4:e4b61tok/sEstimated
ollama run gemma4:e4bQuant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 7.6 GBollama run qwen3:4b61tok/sEstimated
ollama run qwen3:4bQuant: Q4_K_MContext: 8,192VRAM: 2.0 GBHeadroom: 14.0 GB3190tok/sEstimated
Runs with tradeoffs46 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
ollama run qwen3:30b14tok/sEstimated
- • Partial CPU offload: ~35% of layers run on CPU
ollama run qwen3:30bQuant: Q4_K_MContext: 8,192VRAM: 23.9 GBHeadroom: 11.3 GB- • Partial CPU offload: ~33% of layers run on CPU
ollama run qwen2.5-coder:32b13tok/sEstimated
- • Partial CPU offload: ~33% of layers run on CPU
ollama run qwen2.5-coder:32bQuant: Q4_K_MContext: 2,048VRAM: 25.8 GBHeadroom: 9.4 GB- • Partial CPU offload: ~38% of layers run on CPU
ollama run qwen3:32b13tok/sEstimated
- • Partial CPU offload: ~38% of layers run on CPU
ollama run qwen3:32bQuant: Q4_K_MContext: 2,048VRAM: 24.6 GBHeadroom: 10.6 GB- • Partial CPU offload: ~35% of layers run on CPU
ollama run gemma4:31b14tok/sEstimated
- • Partial CPU offload: ~35% of layers run on CPU
ollama run gemma4:31bQuant: Q4_K_MContext: 2,048VRAM: 25.8 GBHeadroom: 9.4 GB- • Partial CPU offload: ~38% of layers run on CPU
ollama run deepseek-r1:32b13tok/sEstimated
- • Partial CPU offload: ~38% of layers run on CPU
ollama run deepseek-r1:32bQuant: Q4_K_MContext: 2,048VRAM: 12.4 GBHeadroom: 3.6 GB- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run qwen3:14b31tok/sEstimated
- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run qwen3:14bQuant: Q4_K_MContext: 8,192VRAM: 28.5 GBHeadroom: 6.7 GB- • Partial CPU offload: ~44% of layers run on CPU
ollama run mistral-small:24b18tok/sEstimated
- • Partial CPU offload: ~44% of layers run on CPU
ollama run mistral-small:24bQuant: Q4_K_MContext: 2,048VRAM: 12.9 GBHeadroom: 3.1 GB- • Tight VRAM fit — only 3.1 GB headroom left for context growth
ollama run qwen2.5:14b31tok/sEstimated
- • Tight VRAM fit — only 3.1 GB headroom left for context growth
ollama run qwen2.5:14bWhat 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: 57 new tradeoff
- • Qwen 3 30B-A3B
- • Llama 3.3 70B Instruct
- • Qwen 2.5 Coder 32B Instruct
- • Qwen 3 32B
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 ~? GB/s to ~616 GB/s.
Unlocks: 19 new comfortable
- • Qwen 3 14B
- • Phi-4 14B
- • Phi-4 Reasoning 14B
- • Mistral Nemo 12B Instruct
Add a second NVIDIA GeForce RTX 4060 Ti 16GB
Launch MSRP $499 (2023). 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: 40 new comfortable
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
- • Qwen 3 32B
- • Gemma 4 31B Dense
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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.
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.
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How to read these numbers
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