RUNLOCALAIv38
→WILL IT RUNBEST GPUCOMPARETROUBLESHOOTSTARTPULSEMODELSHARDWARETOOLSBENCH
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SYS · ONLINEUPTIME · 100%2026 · operator-owned
RUNLOCALAI · v38
Will it run? / NVIDIA GeForce RTX 5090 Mobile

What can NVIDIA GeForce RTX 5090 Mobile run?

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

Memory: 24 GB VRAM + 32 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
84 models

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

#1Gemma 3 1B
1B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 12.9 GB
ollama run gemma3:1b
1023
tok/s
E
Weights
0.60 GB
KV cache
0.50 GB
Activations
8.22 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#2Llama 3.2 1B Instruct
1B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 11.6 GBHeadroom: 12.4 GB
ollama run llama3.2:1b
581
tok/s
E
Weights
1.06 GB
KV cache
0.50 GB
Activations
8.25 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#3Gemma 4 E2B (Effective 2B)
2B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 13.2 GBHeadroom: 10.8 GB
ollama run gemma4:e2b
291
tok/s
E
Weights
2.13 GB
KV cache
1.00 GB
Activations
8.30 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#4Llama 3.2 3B Instruct
3B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 14.8 GBHeadroom: 9.2 GB
ollama run llama3.2:3b
194
tok/s
E
Weights
3.19 GB
KV cache
1.50 GB
Activations
8.35 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#5Phi-3.5 Vision
4.2B
phi
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 14.8 GBHeadroom: 9.2 GB
244
tok/s
E
Weights
2.54 GB
KV cache
2.10 GB
Activations
8.32 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#6Phi-3.5 Mini Instruct
3.8B
phi
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 16.1 GBHeadroom: 7.9 GB
ollama run phi3.5:3.8b
153
tok/s
E
Weights
4.04 GB
KV cache
1.90 GB
Activations
8.39 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#7Gemma 4 E4B (Effective 4B)
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 16.5 GBHeadroom: 7.5 GB
ollama run gemma4:e4b
145
tok/s
E
Weights
4.25 GB
KV cache
2.00 GB
Activations
8.40 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#8Qwen 3 4B
4B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 16.5 GBHeadroom: 7.5 GB
ollama run qwen3:4b
145
tok/s
E
Weights
4.25 GB
KV cache
2.00 GB
Activations
8.40 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#9Gemma 3 4B
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 16.5 GBHeadroom: 7.5 GB
ollama run gemma3:4b
145
tok/s
E
Weights
4.25 GB
KV cache
2.00 GB
Activations
8.40 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#10Llama 3.1 Nemotron Nano 8B
8B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 19.1 GBHeadroom: 4.9 GB
128
tok/s
E
Weights
4.83 GB
KV cache
4.00 GB
Activations
8.43 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#11Mistral 7B Instruct v0.3
7B
mistral
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 18.5 GBHeadroom: 5.5 GB
ollama run mistral:7b
128
tok/s
E
Weights
4.81 GB
KV cache
3.50 GB
Activations
8.43 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#12CodeGemma 7B
7B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.9 GBHeadroom: 6.1 GB
ollama run codegemma:7b
146
tok/s
E
Weights
4.23 GB
KV cache
3.50 GB
Activations
8.40 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →

Runs with tradeoffs
49 models

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

Qwen 3 30B-A3B
30B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 26.6 GBHeadroom: 16.6 GB
  • • Partial CPU offload: ~10% of layers run on CPU
ollama run qwen3:30b
34
tok/s
E
Weights
18.11 GB
KV cache
3.75 GB
Activations
2.95 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Qwen 2.5 Coder 32B Instruct
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 32.4 GBHeadroom: 10.8 GB
  • • Partial CPU offload: ~26% of layers run on CPU
ollama run qwen2.5-coder:32b
32
tok/s
E
Weights
19.32 GB
KV cache
2.15 GB
Activations
9.16 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Gemma 4 31B Dense
31B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 27.4 GBHeadroom: 15.8 GB
  • • Partial CPU offload: ~12% of layers run on CPU
ollama run gemma4:31b
33
tok/s
E
Weights
18.72 GB
KV cache
3.88 GB
Activations
2.98 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Qwen 3 32B
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 28.1 GBHeadroom: 15.1 GB
  • • Partial CPU offload: ~15% of layers run on CPU
ollama run qwen3:32b
32
tok/s
E
Weights
19.32 GB
KV cache
4.00 GB
Activations
3.01 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Qwen 3 8B
8B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 22.9 GBHeadroom: 1.1 GB
  • • Tight VRAM fit — only 1.1 GB headroom left for context growth
ollama run qwen3:8b
73
tok/s
E
Weights
8.50 GB
KV cache
4.00 GB
Activations
8.62 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
DeepSeek R1 Distill Qwen 32B
32B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 28.1 GBHeadroom: 15.1 GB
  • • Partial CPU offload: ~15% of layers run on CPU
ollama run deepseek-r1:32b
32
tok/s
E
Weights
19.32 GB
KV cache
4.00 GB
Activations
3.01 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Gemma 4 26B MoE
26B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 23.6 GBHeadroom: 0.4 GB
  • • Tight VRAM fit — only 0.4 GB headroom left for context growth
ollama run gemma4:26b-moe
39
tok/s
E
Weights
15.70 GB
KV cache
3.25 GB
Activations
2.83 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Nemotron 3 Nano (30B-A3B)
30B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 26.6 GBHeadroom: 16.6 GB
  • • Partial CPU offload: ~10% of layers run on CPU
ollama run nemotron3:nano
34
tok/s
E
Weights
18.11 GB
KV cache
3.75 GB
Activations
2.95 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →

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: 61 new tradeoff

  • • Qwen 3 30B-A3B
  • • Qwen 2.5 Coder 32B Instruct
  • • Llama 3.3 70B Instruct
  • • Gemma 4 31B Dense
Shop this upgrade↗

Upgrade to NVIDIA GeForce RTX 5090

~$2499

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

Unlocks: 28 new comfortable

  • • DeepSeek R1 Distill Qwen 7B
  • • Qwen 3 8B
  • • Hermes 3 Llama 3.1 8B
  • • Gemma 2 9B Instruct
Shop this upgrade↗

Add a second NVIDIA GeForce RTX 5090 Mobile

see current pricing

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: 40 new comfortable

  • • DeepSeek R1 Distill Qwen 7B
  • • Qwen 3 8B
  • • Hermes 3 Llama 3.1 8B
  • • Gemma 2 9B Instruct
Shop this upgrade↗

Some links above are affiliate links. We may earn a commission at no extra cost to you. How we make money.

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

Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.

—
Qwen 3.5 235B-A17B (MoE)
397B
qwen
Commercial OK

Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.

—
Qwen 3 235B-A22B
235B
qwen
Commercial OK

Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.

—
DeepSeek V4 Flash (284B MoE)
284B
deepseek
Commercial OK

Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.

—
Llama 4 Scout
109B
llama
Commercial OK

Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.

—

How to read these numbers

M
Measured — we ran this exact combo on owner hardware.

~
Extrapolated — predicted from a measured benchmark on similar-bandwidth hardware.

E
Estimated — pure formula based on VRAM bandwidth and model architecture.

Full methodology →

Want a specific benchmark we don't have? Email support@runlocalai.co and we'll prioritize it.