What can NVIDIA GeForce RTX 4070 Ti Super run locally?

119 models fit comfortably on NVIDIA GeForce RTX 4070 Ti Super with 32 GB system RAM. 94 more run with tradeoffs. Top pick: Hermes 3 Llama 3.1 8B.

What's the recommended runner for this hardware?

llama.cpp / Ollama (CUDA). This is the runner that gives best tokens-per-second on NVIDIA GeForce RTX 4070 Ti Super based on windows support and the GPU backend.

What can NVIDIA GeForce RTX 4070 Ti Super run for creative?

Build: RTX 4070 Ti Super + Ryzen 7 7700X + 32GB DDR5

Memory: 16 GB VRAM + 32 GB system RAM

Runner: llama.cpp / Ollama (CUDA)

Any Chat Coding Agents Reasoning Vision Long context Creative

Runs comfortably
119 models

Ranked by fit for creative use case + predicted speed. Click a row for VRAM breakdown.

#1Hermes 3 Llama 3.1 8B

hermes

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 5.0 GB

ollama run hermes3:8b

tok/s

Estimated

Weights

4.90 GB

KV cache

4.00 GB

Activations

0.25 GB

Runtime

1.80 GB

Model details →

#2Hermes 3 Llama 3.2 3B

hermes

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 5.2 GBHeadroom: 10.8 GB

241

tok/s

Estimated

Weights

1.80 GB

KV cache

1.50 GB

Activations

0.10 GB

Runtime

1.80 GB

Model details →

#3Dolphin 3.0 Llama 3.2 3B

dolphin

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 5.2 GBHeadroom: 10.8 GB

241

tok/s

Estimated

Weights

1.80 GB

KV cache

1.50 GB

Activations

0.10 GB

Runtime

1.80 GB

Model details →

#4Dolphin 3.0 8B

dolphin

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 4.9 GB

ollama run dolphin3:8b

tok/s

Estimated

Weights

5.00 GB

KV cache

4.00 GB

Activations

0.26 GB

Runtime

1.80 GB

Model details →

#5Gemma 2 2B Instruct

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 4.0 GBHeadroom: 12.0 GB

362

tok/s

Estimated

Weights

1.10 GB

KV cache

1.00 GB

Activations

0.06 GB

Runtime

1.80 GB

Model details →

#6Gemma 4 E2B (Effective 2B)

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 5.1 GBHeadroom: 10.9 GB

ollama run gemma4:e2b

206

tok/s

Estimated

Weights

2.20 GB

KV cache

1.00 GB

Activations

0.12 GB

Runtime

1.80 GB

Model details →

#7CodeGemma 7B

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 6.3 GB

ollama run codegemma:7b

103

tok/s

Estimated

Weights

4.20 GB

KV cache

3.50 GB

Activations

0.22 GB

Runtime

1.80 GB

Model details →

#8Gemma 4 E4B (Effective 4B)

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 7.6 GB

ollama run gemma4:e4b

103

tok/s

Estimated

Weights

4.40 GB

KV cache

2.00 GB

Activations

0.23 GB

Runtime

1.80 GB

Model details →

#9Gemma 3 4B

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 7.6 GB

ollama run gemma3:4b

103

tok/s

Estimated

Weights

4.40 GB

KV cache

2.00 GB

Activations

0.23 GB

Runtime

1.80 GB

Model details →

#10Llama 3.2 3B Instruct

llama

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 6.9 GBHeadroom: 9.1 GB

ollama run llama3.2:3b

137

tok/s

Estimated

Weights

3.40 GB

KV cache

1.50 GB

Activations

0.18 GB

Runtime

1.80 GB

Model details →

#11Qwen 3 1.7B

1.7B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 3.6 GBHeadroom: 12.4 GB

426

tok/s

Estimated

Weights

0.90 GB

KV cache

0.85 GB

Activations

0.05 GB

Runtime

1.80 GB

Model details →

#12Kumru 2B

2.4B

mistral

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 4.6 GBHeadroom: 11.4 GB

ollama run alibayram/kumru:latest

301

tok/s

Estimated

Weights

1.50 GB

KV cache

1.20 GB

Activations

0.08 GB

Runtime

1.80 GB

Model details →

Runs with tradeoffs
94 models

Tight VRAM, partial CPU offload, or context-limited.

Gemma 2 9B Instruct

gemma

Commercial OK

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 gemma2:9b

tok/s

Estimated

Weights

5.80 GB

KV cache

4.50 GB

Activations

0.30 GB

Runtime

1.80 GB

Model details →

DeepSeek V2 Lite Chat

15.7B

deepseek

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 12.8 GBHeadroom: 3.2 GB

• Tight VRAM fit — only 3.2 GB headroom left for context growth

301

tok/s

Estimated

Weights

8.60 GB

KV cache

1.96 GB

Activations

0.43 GB

Runtime

1.80 GB

Model details →

Granite 3 MoE (3B active)

16B

granite

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 13.8 GBHeadroom: 2.2 GB

• Tight VRAM fit — only 2.2 GB headroom left for context growth

241

tok/s

Estimated

Weights

9.50 GB

KV cache

2.00 GB

Activations

0.48 GB

Runtime

1.80 GB

Model details →

DeepSeek MoE 16B Base

16B

deepseek

Commercial OK

Quant: Q4_K_MContext: 4,096VRAM: 15.8 GBHeadroom: 0.2 GB

• Tight VRAM fit — only 0.2 GB headroom left for context growth

301

tok/s

Estimated

Weights

9.50 GB

KV cache

4.00 GB

Activations

0.48 GB

Runtime

1.80 GB

Model details →

DeepSeek V3 Lite (16B MoE)

16B

deepseek

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 13.8 GBHeadroom: 2.2 GB

• Tight VRAM fit — only 2.2 GB headroom left for context growth

301

tok/s

Estimated

Weights

9.50 GB

KV cache

2.00 GB

Activations

0.48 GB

Runtime

1.80 GB

Model details →

YTU Turkish Gemma 9B v0.1

9.2B

gemma

Commercial OK

Quant: 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:latest

tok/s

Estimated

Weights

5.80 GB

KV cache

4.60 GB

Activations

0.30 GB

Runtime

1.80 GB

Model details →

Gemma 4 12B

12B

gemma

Commercial OK

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 gemma4:12b

tok/s

Estimated

Weights

7.60 GB

KV cache

6.00 GB

Activations

0.39 GB

Runtime

1.80 GB

Model details →

Gemma 3 12B

12B

gemma

Commercial OK

Quant: 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 gemma3:12b

tok/s

Estimated

Weights

7.30 GB

KV cache

6.00 GB

Activations

0.37 GB

Runtime

1.80 GB

Model details →

What 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

Shop this upgrade↗

Upgrade to NVIDIA RTX 2080 Ti 22GB (China-mod)

~$350

22 GB VRAM (vs your 16 GB) plus a bandwidth jump from ~672 GB/s to ~616 GB/s.

Unlocks: 50 new comfortable

• Qwen 3 0.6B
• Gemma 4 12B
• Qwen3.5 9B
• Qwen 3 14B

Shop this upgrade↗

Add a second NVIDIA GeForce RTX 4070 Ti Super

~$829

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

Shop this upgrade↗

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Won't run
top 5 popular models

Need more memory than you have. Shown for orientation.

DeepSeek V4 Pro (1.6T MoE)

1600B

deepseek

Commercial OK