What can NVIDIA GeForce RTX 5070 Ti run for creative?
Build: NVIDIA GeForce RTX 5070 Ti + — + 32 GB RAM (windows)
Runs comfortably76 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: 5.2 GBHeadroom: 10.8 GB322tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 5.2 GBHeadroom: 10.8 GB322tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 11.6 GBHeadroom: 4.4 GBollama run gemma4:e4b121tok/sEstimated
ollama run gemma4:e4bQuant: Q4_K_MContext: 8,192VRAM: 4.9 GBHeadroom: 11.1 GB370tok/sEstimated
Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 7.6 GBollama run gemma3:4b137tok/sEstimated
ollama run gemma3:4bQuant: Q4_K_MContext: 8,192VRAM: 11.6 GBHeadroom: 4.4 GBollama run codegemma:7b113tok/sEstimated
ollama run codegemma:7bQuant: Q4_K_MContext: 8,192VRAM: 11.6 GBHeadroom: 4.4 GB107tok/sEstimated
Quant: Q8_0Context: 8,192VRAM: 9.6 GBHeadroom: 6.4 GBollama run gemma4:e2b107tok/sEstimated
ollama run gemma4:e2bQuant: 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 qwen3:8bQuant: Q4_K_MContext: 8,192VRAM: 3.8 GBHeadroom: 12.2 GB567tok/sEstimated
Runs with tradeoffs47 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: 24.5 GBHeadroom: 10.7 GB- • Partial CPU offload: ~35% of layers run on CPU
ollama run qwen3:30b292tok/sEstimated
- • Partial CPU offload: ~35% of layers run on CPU
ollama run qwen3:30bQuant: 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-v2:16b402tok/sEstimated
- • Tight VRAM fit — only 2.2 GB headroom left for context growth
ollama run deepseek-coder-v2:16bQuant: Q4_K_MContext: 2,048VRAM: 12.8 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: 4,096VRAM: 15.8 GBHeadroom: 0.2 GB- • Tight VRAM fit — only 0.2 GB headroom left for context growth
345tok/sEstimated
- • Tight VRAM fit — only 0.2 GB headroom left for context growth
Quant: Q4_K_MContext: 8,192VRAM: 28.5 GBHeadroom: 6.7 GB- • Partial CPU offload: ~44% of layers run on CPU
40tok/sEstimated
- • Partial CPU offload: ~44% of layers run on CPU
Quant: Q4_K_MContext: 8,192VRAM: 12.7 GBHeadroom: 3.3 GB- • Tight VRAM fit — only 3.3 GB headroom left for context growth
ollama run alibayram/Trendyol-LLM-Asure-12B:latest82tok/sEstimated
- • Tight VRAM fit — only 3.3 GB headroom left for context growth
ollama run alibayram/Trendyol-LLM-Asure-12B:latestWhat 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: 15 new comfortable, 55 new tradeoff
- • Qwen 3 0.6B
- • Gemma 3 270M
- • SigLIP SO400M (patch14-384)
- • TinyLlama 1.1B Chat v1.0
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 ~896 GB/s to ~616 GB/s.
Unlocks: 33 new comfortable
- • Qwen 3 0.6B
- • Qwen 3 14B
- • Gemma 3 270M
- • Phi-4 14B
Add a second NVIDIA GeForce RTX 5070 Ti
Launch MSRP $749 (2025). 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: 56 new comfortable
- • Qwen 3 0.6B
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
- • 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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