What can NVIDIA GeForce RTX 4060 Ti 16GB run for coding?
Build: NVIDIA GeForce RTX 4060 Ti 16GB + — + 32 GB RAM (windows)
Runs comfortably101 models
Ranked by fit for coding use case + predicted speed. Click a row for VRAM breakdown.
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 6.3 GBollama run codegemma:7b44tok/sEstimated
ollama run codegemma:7bQuant: Q4_K_MContext: 8,192VRAM: 10.8 GBHeadroom: 5.2 GBollama run qwen3:8b39tok/sEstimated
ollama run qwen3:8bQuant: Q8_0Context: 8,192VRAM: 10.8 GBHeadroom: 5.2 GBollama run qwen2.5:7b25tok/sEstimated
ollama run qwen2.5:7bQuant: Q8_0Context: 8,192VRAM: 11.8 GBHeadroom: 4.2 GBollama run llama3.1:8b22tok/sEstimated
ollama run llama3.1:8bQuant: Q4_K_MContext: 8,192VRAM: 5.4 GBHeadroom: 10.6 GB103tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 5.3 GBHeadroom: 10.7 GB103tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 9.9 GBHeadroom: 6.1 GB44tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 6.3 GB44tok/sEstimated
Quant: Q4_K_MContext: 4,096VRAM: 8.4 GBHeadroom: 7.6 GB39tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 10.7 GBHeadroom: 5.3 GB39tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 12.0 GBHeadroom: 4.0 GB34tok/sEstimated
Quant: Q6_KContext: 8,192VRAM: 11.9 GBHeadroom: 4.1 GBollama run qwen2.5-coder:7b32tok/sEstimated
ollama run qwen2.5-coder:7bRuns with tradeoffs94 models
Tight VRAM, partial CPU offload, or context-limited.
Quant: Q4_K_MContext: 2,048VRAM: 13.8 GBHeadroom: 2.2 GB- • Tight VRAM fit — only 2.2 GB headroom left for context growth
ollama run deepseek-coder-v2:16b19tok/sEstimated
- • Tight VRAM fit — only 2.2 GB headroom left for context growth
ollama run deepseek-coder-v2:16bQuant: Q4_K_MContext: 8,192VRAM: 26.5 GBHeadroom: 8.7 GB- • Partial CPU offload: ~40% of layers run on CPU
ollama run codestral:22b14tok/sEstimated
- • Partial CPU offload: ~40% of layers run on CPU
ollama run codestral:22bQuant: 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 qwen3:14b22tok/sEstimated
- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run qwen3:14bQuant: 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 qwen2.5:14b22tok/sEstimated
- • Tight VRAM fit — only 3.1 GB headroom left for context growth
ollama run qwen2.5:14bQuant: 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:32b10tok/sEstimated
- • Partial CPU offload: ~33% of layers run on CPU
ollama run qwen2.5-coder:32bQuant: Q4_K_MContext: 2,048VRAM: 24.5 GBHeadroom: 10.7 GB- • Partial CPU offload: ~35% of layers run on CPU
ollama run qwen3:30b10tok/sEstimated
- • Partial CPU offload: ~35% of layers run on CPU
ollama run qwen3:30bQuant: Q4_K_MContext: 8,192VRAM: 20.4 GBHeadroom: 14.8 GB- • Partial CPU offload: ~21% of layers run on CPU
ollama run muse-glimmer10tok/sEstimated
- • Partial CPU offload: ~21% of layers run on CPU
ollama run muse-glimmerQuant: Q4_K_MContext: 2,048VRAM: 24.6 GBHeadroom: 10.6 GB- • Partial CPU offload: ~35% of layers run on CPU
ollama run gemma4:31b10tok/sEstimated
- • Partial CPU offload: ~35% of layers run on CPU
ollama run gemma4:31bWhat 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: 41 new comfortable, 112 new tradeoff
- • Qwen 3 0.6B
- • Qwen 3 1.7B
- • Gemma 3 270M
- • SmolLM2 135M Instruct
Upgrade to NVIDIA RTX 2080 Ti 22GB (China-mod)
~$350
22 GB VRAM (vs your 16 GB) plus a bandwidth jump from ~288 GB/s to ~616 GB/s.
Unlocks: 68 new comfortable
- • Qwen 3 0.6B
- • Gemma 4 12B
- • Qwen3.5 9B
- • Qwen 3 1.7B
Add a second NVIDIA GeForce RTX 4060 Ti 16GB
~$449
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: 115 new comfortable
- • Qwen 3 0.6B
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
- • Qwen3.6 27B
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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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