RUNLOCALAIv38
→WILL IT RUNBEST GPUCOMPARETROUBLESHOOTSTARTPULSEMODELSHARDWARETOOLSBENCH
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SYS · ONLINEUPTIME · 100%2026 · operator-owned
RUNLOCALAI · v38
Will it run? / NVIDIA GeForce RTX 3090 Ti / vision

What can NVIDIA GeForce RTX 3090 Ti run for vision?

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

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

Runs comfortably
18 models

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

#1Gemma 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 →
#2Phi-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 →
#3Gemma 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 →
#4Gemma 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 →
#5Moondream 2
1.9B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 5.3 GBHeadroom: 18.7 GB
538
tok/s
E
Weights
1.15 GB
KV cache
0.24 GB
Activations
2.11 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#6LLaVA 1.6 Mistral 7B
7B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.9 GBHeadroom: 6.1 GB
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 →
#7LLaVA-OneVision 7B
7B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.9 GBHeadroom: 6.1 GB
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 →
#8Qwen 2.5-VL 3B
3B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.4 GBHeadroom: 10.6 GB
341
tok/s
E
Weights
1.81 GB
KV cache
1.50 GB
Activations
8.28 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#9Molmo 7B-D
8B
other
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 13.0 GBHeadroom: 11.0 GB
128
tok/s
E
Weights
4.83 GB
KV cache
2.00 GB
Activations
4.34 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#10Qwen 2.5-VL 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.9 GBHeadroom: 6.1 GB
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 →
#11MiniCPM-V 2.6 8B
8B
minicpm
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 →
#12Janus-Pro 7B
7B
janus
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 12.1 GBHeadroom: 11.9 GB
146
tok/s
E
Weights
4.23 GB
KV cache
1.75 GB
Activations
4.31 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →

Runs with tradeoffs
10 models

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

Llama 3.2 11B Vision Instruct
11B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 22.5 GBHeadroom: 1.5 GB
  • • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run llama3.2-vision:11b
93
tok/s
E
Weights
6.64 GB
KV cache
5.50 GB
Activations
8.52 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Llama 3.2 11B Vision
11B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 22.5 GBHeadroom: 1.5 GB
  • • Tight VRAM fit — only 1.5 GB headroom left for context growth
93
tok/s
E
Weights
6.64 GB
KV cache
5.50 GB
Activations
8.52 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Gemma 3 12B
12B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 23.6 GBHeadroom: 0.4 GB
  • • Tight VRAM fit — only 0.4 GB headroom left for context growth
ollama run gemma3:12b
85
tok/s
E
Weights
7.25 GB
KV cache
6.00 GB
Activations
8.55 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Pixtral 12B
12B
mistral
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 23.6 GBHeadroom: 0.4 GB
  • • Tight VRAM fit — only 0.4 GB headroom left for context growth
ollama run pixtral:12b
85
tok/s
E
Weights
7.25 GB
KV cache
6.00 GB
Activations
8.55 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 →
InternVL 2.5 26B
26B
other
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
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 →
Gemma 3 27B
27B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 40.6 GBHeadroom: 2.6 GB
  • • Partial CPU offload: ~41% of layers run on CPU
ollama run gemma3:27b
38
tok/s
E
Weights
16.30 GB
KV cache
13.50 GB
Activations
9.01 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
MedGemma 27B
27B
gemma
Quant: Q4_K_MContext: 8,192VRAM: 40.6 GBHeadroom: 2.6 GB
  • • Partial CPU offload: ~41% of layers run on CPU
38
tok/s
E
Weights
16.30 GB
KV cache
13.50 GB
Activations
9.01 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: 66 new comfortable, 61 new tradeoff

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Llama 3.2 3B Instruct
  • • Phi-3.5 Mini Instruct
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: 91 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Llama 3.2 3B Instruct
  • • Phi-3.5 Mini Instruct
Shop this upgrade↗

Add a second NVIDIA GeForce RTX 3090 Ti

~$1199

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: 106 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Llama 3.2 3B Instruct
  • • Phi-3.5 Mini 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.