What can NVIDIA GeForce RTX 5090 run locally?

216 models fit comfortably on NVIDIA GeForce RTX 5090 with 32 GB system RAM. 26 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 based on windows support and the GPU backend.

What can NVIDIA GeForce RTX 5090 run?

Build: NVIDIA GeForce RTX 5090 + — + 32 GB RAM (windows)

Memory: 32 GB VRAM + 32 GB system RAM

Runner: llama.cpp / Ollama (CUDA)

Any Chat Coding Agents Reasoning Vision Long context Creative

Runs comfortably
216 models

Full-VRAM resident, with room for context. No compromises.

#1Qwen 3 0.6B

0.6B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 2.4 GBHeadroom: 29.6 GBTTFT: instant

3215

tok/s

Estimated

Weights

0.30 GB

KV cache

0.30 GB

Activations

0.02 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~12 ms (instant)

Model details →

#2Llama 3.1 8B Instruct

llama

Commercial OK

Quant: FP16Context: 8,192VRAM: 19.8 GBHeadroom: 12.2 GBTTFT: fast

ollama run llama3.1:8b

tok/s

Estimated

Weights

16.10 GB

KV cache

1.07 GB

Activations

0.81 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~164 ms (fast)

Model details →

#3Qwen 3 30B-A3B

30B

qwen

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 24.5 GBHeadroom: 7.5 GBTTFT: noticeable

ollama run qwen3:30b

tok/s

Estimated

Weights

18.00 GB

KV cache

3.75 GB

Activations

0.90 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~614 ms (noticeable)

Model details →

#4Qwen 2.5 Coder 32B Instruct

32B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 23.9 GBHeadroom: 8.1 GBTTFT: noticeable

ollama run qwen2.5-coder:32b

tok/s

Estimated

Weights

19.00 GB

KV cache

2.15 GB

Activations

0.96 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~655 ms (noticeable)

Model details →

#5Qwen3.6 27B

27B

qwen

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 23.0 GBHeadroom: 9.0 GBTTFT: noticeable

ollama run qwen3.6:27b

tok/s

Estimated

Weights

17.00 GB

KV cache

3.38 GB

Activations

0.85 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~553 ms (noticeable)

Model details →

#6Qwen 3 32B

32B

qwen

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 25.8 GBHeadroom: 6.2 GBTTFT: noticeable

ollama run qwen3:32b

tok/s

Estimated

Weights

19.00 GB

KV cache

4.00 GB

Activations

0.95 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~655 ms (noticeable)

Model details →

#7Gemma 4 31B Dense

31B

gemma

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 24.6 GBHeadroom: 7.4 GBTTFT: noticeable

ollama run gemma4:31b

tok/s

Estimated

Weights

18.00 GB

KV cache

3.88 GB

Activations

0.90 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~635 ms (noticeable)

Model details →

#8Qwen 3 8B

qwen

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 14.4 GBHeadroom: 17.6 GBTTFT: fast

ollama run qwen3:8b

137

tok/s

Estimated

Weights

8.20 GB

KV cache

4.00 GB

Activations

0.42 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~164 ms (fast)

Model details →

#9Gemma 4 12B

12B

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 15.8 GBHeadroom: 16.2 GBTTFT: fast

ollama run gemma4:12b

161

tok/s

Estimated

Weights

7.60 GB

KV cache

6.00 GB

Activations

0.39 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~246 ms (fast)

Model details →

#10Qwen3.5 9B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 13.2 GBHeadroom: 18.8 GBTTFT: fast

ollama run qwen3.5:9b

214

tok/s

Estimated

Weights

6.60 GB

KV cache

4.50 GB

Activations

0.34 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~184 ms (fast)

Model details →

#11Qwen3 Coder 30B-A3B

30B

qwen

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 25.5 GBHeadroom: 6.5 GBTTFT: noticeable

ollama run qwen3-coder:30b

tok/s

Estimated

Weights

19.00 GB

KV cache

3.75 GB

Activations

0.95 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~614 ms (noticeable)

Model details →

#12DeepSeek R1 Distill Qwen 32B

32B

deepseek

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 25.8 GBHeadroom: 6.2 GBTTFT: noticeable

ollama run deepseek-r1:32b

tok/s

Estimated

Weights

19.00 GB

KV cache

4.00 GB

Activations

0.95 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~655 ms (noticeable)

Model details →

Runs with tradeoffs
26 models

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

Llama 3.3 70B Instruct

70B

llama

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 46.5 GBHeadroom: 4.7 GBTTFT: noticeable

• Partial CPU offload: ~31% of layers run on CPU

ollama run llama3.3:70b

tok/s

Estimated

Weights

40.00 GB

KV cache

2.68 GB

Activations

2.01 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1434 ms (noticeable)

Model details →

Qwen3.6 35B-A3B

35B

qwen

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 31.4 GBHeadroom: 0.6 GBTTFT: noticeable

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

ollama run qwen3.6:35b-a3b

tok/s

Estimated

Weights

24.00 GB

KV cache

4.38 GB

Activations

1.20 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~717 ms (noticeable)

Model details →

Gemma 4 26B MoE

26B

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 31.6 GBHeadroom: 0.4 GBTTFT: noticeable

• Tight VRAM fit — only 0.4 GB headroom left for context growth

ollama run gemma4:26b-moe

tok/s

Estimated

Weights

16.00 GB

KV cache

13.00 GB

Activations

0.81 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~532 ms (noticeable)

Model details →

Gemma 4 26B-A4B

26B

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 31.6 GBHeadroom: 0.4 GBTTFT: noticeable

• Tight VRAM fit — only 0.4 GB headroom left for context growth

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

tok/s

Estimated

Weights

16.00 GB

KV cache

13.00 GB

Activations

0.81 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~532 ms (noticeable)

Model details →

Mistral Small 3 24B

24B

mistral

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 28.5 GBHeadroom: 3.5 GBTTFT: fast

• Tight VRAM fit — only 3.5 GB headroom left for context growth

ollama run mistral-small:24b

tok/s

Estimated

Weights

14.00 GB

KV cache

12.00 GB

Activations

0.71 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~492 ms (fast)

Model details →

Qwen3.5 35B-A3B

35B

qwen

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 31.4 GBHeadroom: 0.6 GBTTFT: noticeable

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

ollama run qwen3.5:35b-a3b

tok/s

Estimated

Weights

24.00 GB

KV cache

4.38 GB

Activations

1.20 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~717 ms (noticeable)

Model details →

Nemotron 3 Nano Omni 33B

33B

other

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 47.7 GBHeadroom: 3.5 GBTTFT: noticeable

• Partial CPU offload: ~33% of layers run on CPU

ollama run nemotron3:33b

tok/s

Estimated

Weights

28.00 GB

KV cache

16.50 GB

Activations

1.41 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~676 ms (noticeable)

Model details →

Ornith 1.0 35B

35B

other

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 28.2 GBHeadroom: 3.8 GBTTFT: noticeable

• Tight VRAM fit — only 3.8 GB headroom left for context growth

ollama run ornith:35b

tok/s

Estimated

Weights

21.00 GB

KV cache

4.38 GB

Activations

1.05 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~717 ms (noticeable)

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: 43 new tradeoff

• Llama 3.3 70B Instruct
• DeepSeek R1 Distill Llama 70B
• Qwen3.6 35B-A3B
• Gemma 4 26B MoE

Shop this upgrade↗

Upgrade to NVIDIA A100 40GB

see current pricing

40 GB VRAM (vs your 32 GB) plus a bandwidth jump from ~1792 GB/s to ~1555 GB/s.

Unlocks: 21 new comfortable

• Qwen3.6 35B-A3B
• Gemma 4 26B MoE
• Gemma 4 26B-A4B
• Mistral Small 3 24B

Shop this upgrade↗

Add a second NVIDIA GeForce RTX 5090

~$2499

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: 39 new comfortable

• Llama 3.3 70B Instruct
• DeepSeek R1 Distill Llama 70B
• Qwen3.6 35B-A3B
• Gemma 4 26B MoE

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