What can NVIDIA GeForce RTX 5090 Mobile run locally?

125 models fit comfortably on NVIDIA GeForce RTX 5090 Mobile with 32 GB system RAM. 32 more run with tradeoffs. Top pick: Qwen 3 0.6B.

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 5090 Mobile based on windows support and the GPU backend.

What can NVIDIA GeForce RTX 5090 Mobile run for chat?

Build: NVIDIA GeForce RTX 5090 Mobile + — + 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
125 models

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

#1Qwen 3 0.6B

0.6B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 2.4 GBHeadroom: 21.6 GB

1705

tok/s

Estimated

Weights

0.30 GB

KV cache

0.30 GB

Activations

0.02 GB

Runtime

1.80 GB

Model details →

#2Qwen 3 1.7B

1.7B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 3.6 GBHeadroom: 20.4 GB

602

tok/s

Estimated

Weights

0.90 GB

KV cache

0.85 GB

Activations

0.05 GB

Runtime

1.80 GB

Model details →

#3Llama 3.2 3B Instruct

llama

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 6.9 GBHeadroom: 17.1 GB

ollama run llama3.2:3b

194

tok/s

Estimated

Weights

3.40 GB

KV cache

1.50 GB

Activations

0.18 GB

Runtime

1.80 GB

Model details →

#4TinyLlama 1.1B Chat v1.0

1.1B

llama

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 2.6 GBHeadroom: 21.4 GB

930

tok/s

Estimated

Weights

0.60 GB

KV cache

0.14 GB

Activations

0.03 GB

Runtime

1.80 GB

Model details →

#5Gemma 2 2B Instruct

gemma

Commercial OK

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

511

tok/s

Estimated

Weights

1.10 GB

KV cache

1.00 GB

Activations

0.06 GB

Runtime

1.80 GB

Model details →

#6Mistral 7B Instruct v0.3

mistral

Commercial OK

Quant: Q5_K_MContext: 8,192VRAM: 10.7 GBHeadroom: 13.3 GB

ollama run mistral:7b

128

tok/s

Estimated

Weights

5.10 GB

KV cache

3.50 GB

Activations

0.26 GB

Runtime

1.80 GB

Model details →

#7DeepSeek V2 Lite Chat

15.7B

deepseek

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 18.7 GBHeadroom: 5.3 GB

426

tok/s

Estimated

Weights

8.60 GB

KV cache

7.85 GB

Activations

0.44 GB

Runtime

1.80 GB

Model details →

#8Trendyol LLM 7B Chat v0.1

llama

Quant: Q4_K_MContext: 4,096VRAM: 7.6 GBHeadroom: 16.4 GB

146

tok/s

Estimated

Weights

3.90 GB

KV cache

1.75 GB

Activations

0.20 GB

Runtime

1.80 GB

Model details →

#9Kumru 2B

2.4B

mistral

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 4.6 GBHeadroom: 19.4 GB

ollama run alibayram/kumru:latest

426

tok/s

Estimated

Weights

1.50 GB

KV cache

1.20 GB

Activations

0.08 GB

Runtime

1.80 GB

Model details →

#10Turkcell LLM 7B v1

7.4B

mistral

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 10.2 GBHeadroom: 13.8 GB

ollama run RefinedNeuro/Turkcell-LLM-7b-v1:latest

138

tok/s

Estimated

Weights

4.50 GB

KV cache

3.70 GB

Activations

0.23 GB

Runtime

1.80 GB

Model details →

#11Mistral Turkish v2 (brooqs)

7.2B

mistral

Quant: Q4_0Context: 8,192VRAM: 9.7 GBHeadroom: 14.3 GB

ollama run brooqs/mistral-turkish-v2:latest

152

tok/s

Estimated

Weights

4.10 GB

KV cache

3.60 GB

Activations

0.21 GB

Runtime

1.80 GB

Model details →

#12Malhajar Mistral 7B Turkish

7.2B

mistral

Commercial OK

Quant: Q5_K_MContext: 8,192VRAM: 10.8 GBHeadroom: 13.2 GB

ollama run koezgen/malhajar-mistral-tur7b:latest

125

tok/s

Estimated

Weights

5.10 GB

KV cache

3.60 GB

Activations

0.26 GB

Runtime

1.80 GB

Model details →

Runs with tradeoffs
32 models

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

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 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 →

Gemma 4 31B Dense

31B

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 36.2 GBHeadroom: 7.0 GB

• Partial CPU offload: ~34% of layers run on CPU

ollama run gemma4:31b

tok/s

Estimated

Weights

18.00 GB

KV cache

15.50 GB

Activations

0.91 GB

Runtime

1.80 GB

Model details →

OLMo 2 32B

32B

other

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 37.8 GBHeadroom: 5.4 GB

• Partial CPU offload: ~36% of layers run on CPU

tok/s

Estimated

Weights

19.00 GB

KV cache

16.00 GB

Activations

0.96 GB

Runtime

1.80 GB

Model details →

Yi 1.5 34B

34B

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 39.8 GBHeadroom: 3.4 GB

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

ollama run yi:34b

tok/s

Estimated

Weights

20.00 GB

KV cache

17.00 GB

Activations

1.01 GB

Runtime

1.80 GB

Model details →

Mixtral 8x7B Instruct

47B

mixtral

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 37.1 GBHeadroom: 6.1 GB

• Partial CPU offload: ~35% of layers run on CPU

ollama run mixtral:8x7b

tok/s

Estimated

Weights

28.00 GB

KV cache

5.88 GB

Activations

1.40 GB

Runtime

1.80 GB

Model details →

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 →

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 →

What 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: 5 new comfortable, 48 new tradeoff

• Gemma 3 270M
• SmolLM2 135M Instruct
• SmolLM2 360M Instruct
• VBART Large (Turkish Summarization)

Shop this upgrade↗

Upgrade to NVIDIA GeForce RTX 5090

Launch MSRP $1,999 (2025). Check the current price.

32 GB VRAM (vs your 24 GB) plus a bandwidth jump from ~? GB/s to ~1792 GB/s.

Unlocks: 29 new comfortable

• Qwen 3 30B-A3B
• Qwen 2.5 Coder 32B Instruct
• Qwen 3 32B
• Gemma 4 31B Dense

Shop this upgrade↗

Add a second NVIDIA GeForce RTX 5090 Mobile

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: 37 new comfortable

• Qwen 3 30B-A3B
• Qwen 2.5 Coder 32B Instruct
• Qwen 3 32B
• Gemma 4 31B Dense

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