RUNLOCALAIv38
→WILL IT RUNBEST GPUCOMPARETROUBLESHOOTSTARTPULSEMODELSHARDWARETOOLSBENCH
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RUNLOCALAI · v38
Will it run? / NVIDIA GeForce RTX 4090 Mobile / coding

What can NVIDIA GeForce RTX 4090 Mobile run for coding?

Build: NVIDIA GeForce RTX 4090 Mobile + — + 32 GB RAM (windows)

Memory: 16 GB VRAM + 32 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
38 models

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

#1CodeGemma 7B
7B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GB
ollama run codegemma:7b
89
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#2Llama 3.1 8B Instruct
8B
llama
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GB
ollama run llama3.1:8b
78
tok/s
E
Weights
4.83 GB
KV cache
0.27 GB
Activations
2.29 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#3Qwen 3 8B
8B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.9 GBHeadroom: 6.1 GB
ollama run qwen3:8b
78
tok/s
E
Weights
4.83 GB
KV cache
1.00 GB
Activations
2.29 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#4StarCoder 2 7B
7B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GB
89
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#5Qwen 2.5 Coder 7B Instruct
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GB
ollama run qwen2.5-coder:7b
89
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#6Codestral Mamba 7B
7B
mistral
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GB
89
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#7OpenCoder 8B
8B
opencoder
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.9 GBHeadroom: 6.1 GB
78
tok/s
E
Weights
4.83 GB
KV cache
1.00 GB
Activations
2.29 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#8Yi Coder 9B
9B
yi
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 10.7 GBHeadroom: 5.3 GB
69
tok/s
E
Weights
5.43 GB
KV cache
1.13 GB
Activations
2.32 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#9Nemotron Mini 4B Instruct
4B
other
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 9.4 GBHeadroom: 6.6 GB
155
tok/s
E
Weights
2.42 GB
KV cache
1.00 GB
Activations
4.22 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#10DeepSeek R1 Distill Qwen 7B
7B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GB
ollama run deepseek-r1:7b
89
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#11Mistral 7B Instruct v0.3
7B
mistral
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GB
ollama run mistral:7b
89
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#12Qwen 3 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GB
89
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →

Runs with tradeoffs
71 models

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

DeepSeek Coder V2 Lite (16B)
16B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 16.0 GBHeadroom: 0.0 GB
  • • Tight VRAM fit — only 0.0 GB headroom left for context growth
ollama run deepseek-coder-v2:16b
39
tok/s
E
Weights
9.66 GB
KV cache
2.00 GB
Activations
2.53 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Codestral 22B
22B
mistral
Quant: Q4_K_MContext: 8,192VRAM: 34.9 GBHeadroom: 0.3 GB
  • • Partial CPU offload: ~54% of layers run on CPU
ollama run codestral:22b
28
tok/s
E
Weights
13.28 GB
KV cache
11.00 GB
Activations
8.86 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Qwen 2.5 7B Instruct
7B
qwen
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 15.5 GBHeadroom: 0.5 GB
  • • Tight VRAM fit — only 0.5 GB headroom left for context growth
ollama run qwen2.5:7b
78
tok/s
E
Weights
4.81 GB
KV cache
0.47 GB
Activations
8.43 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Qwen 3 14B
14B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 14.5 GBHeadroom: 1.5 GB
  • • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run qwen3:14b
44
tok/s
E
Weights
8.45 GB
KV cache
1.75 GB
Activations
2.47 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Qwen 2.5 14B Instruct
14B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 14.5 GBHeadroom: 1.5 GB
  • • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run qwen2.5:14b
44
tok/s
E
Weights
8.45 GB
KV cache
1.75 GB
Activations
2.47 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: 2.8 GB
  • • Partial CPU offload: ~51% of layers run on CPU
ollama run qwen2.5-coder:32b
19
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 →
Qwen 3 30B-A3B
30B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 26.6 GBHeadroom: 8.6 GB
  • • Partial CPU offload: ~40% of layers run on CPU
ollama run qwen3:30b
21
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 →
Gemma 4 31B Dense
31B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 27.4 GBHeadroom: 7.8 GB
  • • Partial CPU offload: ~42% of layers run on CPU
ollama run gemma4:31b
20
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 →

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: 15 new comfortable, 82 new tradeoff

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Whisper Large v3
  • • SmolLM 2 1.7B Instruct
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 ~576 GB/s to ~616 GB/s.

Unlocks: 58 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Gemma 4 E2B (Effective 2B)
  • • Llama 3.2 3B Instruct
Shop this upgrade↗

Add a second NVIDIA GeForce RTX 4090 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: 71 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Gemma 4 E2B (Effective 2B)
  • • Llama 3.2 3B 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 (16 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 (16 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 (16 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 (16 GB) + 60% of system RAM (19 GB) combined.

—
DeepSeek R1 (671B reasoning)
671B
deepseek
Commercial OK

Even with CPU offload, needs more memory than your VRAM (16 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.