What can NVIDIA GeForce RTX 4080 Super run for coding?
Build: RTX 4080 Super + i7-14700K + 32GB DDR5
Runs comfortably60 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 GBTTFT: fastollama run codegemma:7b113tok/sEstimated
ollama run codegemma:7bQuant: Q4_K_MContext: 8,192VRAM: 10.8 GBHeadroom: 5.2 GBTTFT: fastollama run qwen3:8b99tok/sEstimated
ollama run qwen3:8bQuant: Q8_0Context: 8,192VRAM: 10.8 GBHeadroom: 5.2 GBTTFT: fastollama run qwen2.5:7b64tok/sEstimated
ollama run qwen2.5:7bQuant: Q8_0Context: 8,192VRAM: 11.8 GBHeadroom: 4.2 GBTTFT: fastollama run llama3.1:8b56tok/sEstimated
ollama run llama3.1:8bQuant: Q4_K_MContext: 8,192VRAM: 5.3 GBHeadroom: 10.7 GBTTFT: fast264tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 5.4 GBHeadroom: 10.6 GBTTFT: fast264tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 6.3 GBTTFT: fast113tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 9.9 GBHeadroom: 6.1 GBTTFT: fast113tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 10.7 GBHeadroom: 5.3 GBTTFT: fast99tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 12.0 GBHeadroom: 4.0 GBTTFT: fast88tok/sEstimated
Quant: Q8_0Context: 8,192VRAM: 6.9 GBHeadroom: 9.1 GBTTFT: fastollama run llama3.2:3b150tok/sEstimated
ollama run llama3.2:3bQuant: Q4_K_MContext: 8,192VRAM: 6.5 GBHeadroom: 9.5 GBTTFT: fast189tok/sEstimated
Runs with tradeoffs46 models
Tight VRAM, partial CPU offload, or context-limited.
Quant: Q4_K_MContext: 2,048VRAM: 13.8 GBHeadroom: 2.2 GBTTFT: noticeable- • Tight VRAM fit — only 2.2 GB headroom left for context growth
ollama run deepseek-coder-v2:16b50tok/sEstimated
- • Tight VRAM fit — only 2.2 GB headroom left for context growth
ollama run deepseek-coder-v2:16bQuant: 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 qwen3:14b57tok/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 GBTTFT: noticeable- • Tight VRAM fit — only 3.1 GB headroom left for context growth
ollama run qwen2.5:14b57tok/sEstimated
- • Tight VRAM fit — only 3.1 GB headroom left for context growth
ollama run qwen2.5:14bQuant: Q4_K_MContext: 8,192VRAM: 26.5 GBHeadroom: 8.7 GBTTFT: noticeable- • Partial CPU offload: ~40% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run codestral:22b5tok/sEstimated
- • Partial CPU offload: ~40% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run codestral:22bQuant: Q4_K_MContext: 2,048VRAM: 12.4 GBHeadroom: 3.6 GBTTFT: noticeable- • Tight VRAM fit — only 3.6 GB headroom left for context growth
57tok/sEstimated
- • Tight VRAM fit — only 3.6 GB headroom left for context growth
Quant: Q4_K_MContext: 2,048VRAM: 13.1 GBHeadroom: 2.9 GBTTFT: noticeable- • Tight VRAM fit — only 2.9 GB headroom left for context growth
53tok/sEstimated
- • Tight VRAM fit — only 2.9 GB headroom left for context growth
Quant: Q4_K_MContext: 8,192VRAM: 23.9 GBHeadroom: 11.3 GBTTFT: noticeable- • 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:32b4tok/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: 2,048VRAM: 24.5 GBHeadroom: 10.7 GBTTFT: noticeable- • Partial CPU offload: ~35% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwen3:30b4tok/sEstimated
- • Partial CPU offload: ~35% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwen3:30bWhat 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: 28 new comfortable, 57 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)
Check the current price
22 GB VRAM (vs your 16 GB) plus a bandwidth jump from ~736 GB/s to ~616 GB/s.
Unlocks: 47 new comfortable
- • Qwen 3 0.6B
- • Qwen 3 14B
- • Qwen 3 1.7B
- • Gemma 3 270M
Add a second NVIDIA GeForce RTX 4080 Super
Launch MSRP $999 (2024). 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: 68 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.
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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.
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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The two tiers run on different hosts and terms. Check which tier a pod is on before a long job.
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How to read these numbers
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