What can NVIDIA GeForce RTX 5070 Ti run for creative?
Build: RTX 5070 Ti + Ryzen 7 9700X + 32GB DDR5
Runs comfortably119 models
Ranked by fit for creative use case + predicted speed. Click a row for VRAM breakdown.
Quant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 5.0 GBollama run hermes3:8b121tok/sEstimated
ollama run hermes3:8bQuant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 4.9 GBollama run dolphin3:8b121tok/sEstimated
ollama run dolphin3:8bQuant: Q4_K_MContext: 8,192VRAM: 5.2 GBHeadroom: 10.8 GB322tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 5.2 GBHeadroom: 10.8 GB322tok/sEstimated
Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 7.6 GBollama run gemma4:e4b137tok/sEstimated
ollama run gemma4:e4bQuant: Q4_K_MContext: 8,192VRAM: 4.0 GBHeadroom: 12.0 GB482tok/sEstimated
Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 7.6 GBollama run gemma3:4b137tok/sEstimated
ollama run gemma3:4bQuant: Q8_0Context: 8,192VRAM: 5.1 GBHeadroom: 10.9 GBollama run gemma4:e2b274tok/sEstimated
ollama run gemma4:e2bQuant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 6.3 GBollama run codegemma:7b138tok/sEstimated
ollama run codegemma:7bQuant: Q4_K_MContext: 8,192VRAM: 11.6 GBHeadroom: 4.4 GB107tok/sEstimated
Quant: BF16Context: 8,192VRAM: 9.6 GBHeadroom: 6.4 GB97tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 10.8 GBHeadroom: 5.2 GBollama run qwen3:8b121tok/sEstimated
ollama run qwen3:8bRuns with tradeoffs94 models
Tight VRAM, partial CPU offload, or context-limited.
Quant: Q4_K_MContext: 8,192VRAM: 12.4 GBHeadroom: 3.6 GB- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run gemma2:9b107tok/sEstimated
- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run gemma2:9bQuant: Q4_K_MContext: 8,192VRAM: 12.5 GBHeadroom: 3.5 GB- • Tight VRAM fit — only 3.5 GB headroom left for context growth
ollama run alibayram/turkish-gemma-9b-v0.1:latest105tok/sEstimated
- • Tight VRAM fit — only 3.5 GB headroom left for context growth
ollama run alibayram/turkish-gemma-9b-v0.1:latestQuant: Q4_K_MContext: 2,048VRAM: 12.8 GBHeadroom: 3.2 GB- • Tight VRAM fit — only 3.2 GB headroom left for context growth
402tok/sEstimated
- • Tight VRAM fit — only 3.2 GB headroom left for context growth
Quant: Q4_K_MContext: 2,048VRAM: 13.8 GBHeadroom: 2.2 GB- • Tight VRAM fit — only 2.2 GB headroom left for context growth
322tok/sEstimated
- • Tight VRAM fit — only 2.2 GB headroom left for context growth
Quant: Q4_K_MContext: 4,096VRAM: 15.8 GBHeadroom: 0.2 GB- • Tight VRAM fit — only 0.2 GB headroom left for context growth
402tok/sEstimated
- • Tight VRAM fit — only 0.2 GB headroom left for context growth
Quant: Q4_K_MContext: 2,048VRAM: 13.8 GBHeadroom: 2.2 GB- • Tight VRAM fit — only 2.2 GB headroom left for context growth
402tok/sEstimated
- • Tight VRAM fit — only 2.2 GB headroom left for context growth
Quant: 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 gemma4:12b80tok/sEstimated
- • Tight VRAM fit — only 0.2 GB headroom left for context growth
ollama run gemma4:12bQuant: Q4_K_MContext: 8,192VRAM: 15.5 GBHeadroom: 0.5 GB- • Tight VRAM fit — only 0.5 GB headroom left for context growth
ollama run gemma3:12b80tok/sEstimated
- • Tight VRAM fit — only 0.5 GB headroom left for context growth
ollama run gemma3:12bWhat 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: 23 new comfortable, 112 new tradeoff
- • Qwen 3 0.6B
- • Gemma 3 270M
- • SmolLM2 135M Instruct
- • TinyLlama 1.1B Chat v1.0
Upgrade to NVIDIA RTX 2080 Ti 22GB (China-mod)
~$350
22 GB VRAM (vs your 16 GB) plus a bandwidth jump from ~896 GB/s to ~616 GB/s.
Unlocks: 50 new comfortable
- • Qwen 3 0.6B
- • Gemma 4 12B
- • Qwen3.5 9B
- • Qwen 3 14B
Add a second NVIDIA GeForce RTX 5070 Ti
~$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: 97 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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