What can NVIDIA GeForce RTX 3090 run locally?

161 models fit comfortably on NVIDIA GeForce RTX 3090 with 32 GB system RAM. 56 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 3090 based on windows support and the GPU backend.

What can NVIDIA GeForce RTX 3090 run for creative?

Build: NVIDIA GeForce RTX 3090 + — + 32 GB RAM (windows)

Memory: 24 GB VRAM + 32 GB system RAM

Runner: llama.cpp / Ollama (CUDA)

Any Chat Coding Agents Reasoning Vision Long context Creative

Runs comfortably
161 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: Q8_0Context: 8,192VRAM: 14.7 GBHeadroom: 9.3 GB

ollama run hermes3:8b

tok/s

Estimated

Weights

8.50 GB

KV cache

4.00 GB

Activations

0.43 GB

Runtime

1.80 GB

Model details →

#2Gemma 2 9B Instruct

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 16.6 GBHeadroom: 7.4 GB

ollama run gemma2:9b

tok/s

Estimated

Weights

9.80 GB

KV cache

4.50 GB

Activations

0.50 GB

Runtime

1.80 GB

Model details →

#3Dolphin 3.0 8B

dolphin

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 12.9 GB

ollama run dolphin3:8b

126

tok/s

Estimated

Weights

5.00 GB

KV cache

4.00 GB

Activations

0.26 GB

Runtime

1.80 GB

Model details →

#4Hermes 3 Llama 3.2 3B

hermes

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 5.2 GBHeadroom: 18.8 GB

336

tok/s

Estimated

Weights

1.80 GB

KV cache

1.50 GB

Activations

0.10 GB

Runtime

1.80 GB

Model details →

#5Dolphin 3.0 Llama 3.2 3B

dolphin

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 5.2 GBHeadroom: 18.8 GB

336

tok/s

Estimated

Weights

1.80 GB

KV cache

1.50 GB

Activations

0.10 GB

Runtime

1.80 GB

Model details →

#6Gemma 4 E4B (Effective 4B)

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 15.6 GB

ollama run gemma4:e4b

143

tok/s

Estimated

Weights

4.40 GB

KV cache

2.00 GB

Activations

0.23 GB

Runtime

1.80 GB

Model details →

#7Gemma 2 2B Instruct

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 4.0 GBHeadroom: 20.0 GB

504

tok/s

Estimated

Weights

1.10 GB

KV cache

1.00 GB

Activations

0.06 GB

Runtime

1.80 GB

Model details →

#8Gemma 3 4B

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 15.6 GB

ollama run gemma3:4b

143

tok/s

Estimated

Weights

4.40 GB

KV cache

2.00 GB

Activations

0.23 GB

Runtime

1.80 GB

Model details →

#9Gemma 4 E2B (Effective 2B)

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 5.1 GBHeadroom: 18.9 GB

ollama run gemma4:e2b

286

tok/s

Estimated

Weights

2.20 GB

KV cache

1.00 GB

Activations

0.12 GB

Runtime

1.80 GB

Model details →

#10CodeGemma 7B

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 14.3 GB

ollama run codegemma:7b

144

tok/s

Estimated

Weights

4.20 GB

KV cache

3.50 GB

Activations

0.22 GB

Runtime

1.80 GB

Model details →

#11Turkish Gemma 9B T1

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 11.6 GBHeadroom: 12.4 GB

112

tok/s

Estimated

Weights

5.00 GB

KV cache

4.50 GB

Activations

0.26 GB

Runtime

1.80 GB

Model details →

#12YTU Turkish Gemma 9B v0.1

9.2B

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 12.5 GBHeadroom: 11.5 GB

ollama run alibayram/turkish-gemma-9b-v0.1:latest

110

tok/s

Estimated

Weights

5.80 GB

KV cache

4.60 GB

Activations

0.30 GB

Runtime

1.80 GB

Model details →

Runs with tradeoffs
56 models

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

Jamba 1.5 Mini

52B

other

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 39.8 GBHeadroom: 3.4 GB

• Partial CPU offload: ~40% of layers run on CPU

tok/s

Estimated

Weights

30.00 GB

KV cache

6.50 GB

Activations

1.50 GB

Runtime

1.80 GB

Model details →

Gemma 4 26B MoE

26B

gemma

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 21.9 GBHeadroom: 2.1 GB

• Tight VRAM fit — only 2.1 GB headroom left for context growth

ollama run gemma4:26b-moe

tok/s

Estimated

Weights

16.00 GB

KV cache

3.25 GB

Activations

0.80 GB

Runtime

1.80 GB

Model details →

Gemma 4 26B-A4B

26B

gemma

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 21.9 GBHeadroom: 2.1 GB

• Tight VRAM fit — only 2.1 GB headroom left for context growth

ollama run gemma4:26b-a4b-it-q4_K_M

tok/s

Estimated

Weights

16.00 GB

KV cache

3.25 GB

Activations

0.80 GB

Runtime

1.80 GB

Model details →

Gemma 4 Turkish 26B (4B active)

26B

gemma

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 20.1 GBHeadroom: 3.9 GB

• Tight VRAM fit — only 3.9 GB headroom left for context growth

tok/s

Estimated

Weights

14.30 GB

KV cache

3.25 GB

Activations

0.72 GB

Runtime

1.80 GB

Model details →

Gemma 3 27B

27B

gemma

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 22.0 GBHeadroom: 2.0 GB

• Tight VRAM fit — only 2.0 GB headroom left for context growth

ollama run gemma3:27b

tok/s

Estimated

Weights

16.00 GB

KV cache

3.38 GB

Activations

0.80 GB

Runtime

1.80 GB

Model details →

MedGemma 27B

27B

gemma

Quant: Q4_K_MContext: 2,048VRAM: 22.0 GBHeadroom: 2.0 GB

• Tight VRAM fit — only 2.0 GB headroom left for context growth

tok/s

Estimated

Weights

16.00 GB

KV cache

3.38 GB

Activations

0.80 GB

Runtime

1.80 GB

Model details →

Llama 3.2 11B Vision Instruct

11B

llama

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 20.4 GBHeadroom: 3.6 GB

• Tight VRAM fit — only 3.6 GB headroom left for context growth

ollama run llama3.2-vision:11b

tok/s

Estimated

Weights

12.50 GB

KV cache

5.50 GB

Activations

0.63 GB

Runtime

1.80 GB

Model details →

GPT-OSS Swallow 20B RL v0.1

20B

other

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 23.4 GBHeadroom: 0.6 GB

• Tight VRAM fit — only 0.6 GB headroom left for context growth

tok/s

Estimated

Weights

11.00 GB

KV cache

10.00 GB

Activations

0.56 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, 73 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 PRO 4500 Blackwell

see current pricing

32 GB VRAM (vs your 24 GB) plus a bandwidth jump from ~936 GB/s to ~896 GB/s.

Unlocks: 62 new comfortable

• Qwen 3 0.6B
• Qwen 3 30B-A3B
• Qwen 2.5 Coder 32B Instruct
• Qwen3.6 27B

Shop this upgrade↗

Add a second NVIDIA GeForce RTX 3090

~$899

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: 75 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