What can Apple Mac Studio (M3 Ultra) run for vision?

Build: Apple Mac Studio (M3 Ultra) + — + 32 GB RAM (windows)

Memory: 32 GB unified memory
Runner: llama.cpp (Metal)

Runs comfortably
4 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: 12.1 GBHeadroom: 11.9 GB
ollama run gemma4:e2b
277
tok/s
E
Weights
2.13 GB
KV cache
1.00 GB
Activations
8.30 GB
Runtime
0.70 GB
#2Phi-3.5 Vision
4.2B
phi
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.7 GBHeadroom: 10.3 GB
232
tok/s
E
Weights
2.54 GB
KV cache
2.10 GB
Activations
8.32 GB
Runtime
0.70 GB
#3Gemma 4 E4B (Effective 4B)
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 15.4 GBHeadroom: 8.6 GB
ollama run gemma4:e4b
139
tok/s
E
Weights
4.25 GB
KV cache
2.00 GB
Activations
8.40 GB
Runtime
0.70 GB
#4Gemma 3 4B
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 15.4 GBHeadroom: 8.6 GB
ollama run gemma3:4b
139
tok/s
E
Weights
4.25 GB
KV cache
2.00 GB
Activations
8.40 GB
Runtime
0.70 GB

Runs with tradeoffs
6 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: 21.4 GBHeadroom: 2.6 GB
  • Tight VRAM fit — only 2.6 GB headroom left for context growth
ollama run llama3.2-vision:11b
89
tok/s
E
Weights
6.64 GB
KV cache
5.50 GB
Activations
8.52 GB
Runtime
0.70 GB
Gemma 3 12B
12B
gemma
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 gemma3:12b
81
tok/s
E
Weights
7.25 GB
KV cache
6.00 GB
Activations
8.55 GB
Runtime
0.70 GB
Pixtral 12B
12B
mistral
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 pixtral:12b
81
tok/s
E
Weights
7.25 GB
KV cache
6.00 GB
Activations
8.55 GB
Runtime
0.70 GB
Gemma 4 26B MoE
26B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 22.5 GBHeadroom: 1.5 GB
  • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run gemma4:26b-moe
38
tok/s
E
Weights
15.70 GB
KV cache
3.25 GB
Activations
2.83 GB
Runtime
0.70 GB
Gemma 3 27B
27B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 23.2 GBHeadroom: 0.8 GB
  • Tight VRAM fit — only 0.8 GB headroom left for context growth
ollama run gemma3:27b
36
tok/s
E
Weights
16.30 GB
KV cache
3.38 GB
Activations
2.86 GB
Runtime
0.70 GB
MedGemma 27B
27B
gemma
Quant: Q4_K_MContext: 2,048VRAM: 23.2 GBHeadroom: 0.8 GB
  • Tight VRAM fit — only 0.8 GB headroom left for context growth
36
tok/s
E
Weights
16.30 GB
KV cache
3.38 GB
Activations
2.86 GB
Runtime
0.70 GB

What if you upgraded?

Hypothetical scenarios. We re-ran the compatibility engine for each.

Move up an Apple memory tier

~$200–400 over base

On Apple Silicon, more unified memory is the only path forward — VRAM and system RAM are the same pool.

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Won't run
top 5 popular models

Need more memory than you have. Shown for orientation.

Qwen 3 235B-A22B
235B
qwen
Commercial OK

Needs ~160 GB unified memory minimum at smallest quant; you have 24 GB available after OS overhead.

Llama 4 Scout
109B
llama
Commercial OK

Needs ~80 GB unified memory minimum at smallest quant; you have 24 GB available after OS overhead.

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

Needs ~420 GB unified memory minimum at smallest quant; you have 24 GB available after OS overhead.

Qwen 3 30B-A3B
30B
qwen
Commercial OK

Needs ~22 GB unified memory minimum at smallest quant; you have 24 GB available after OS overhead.

Llama 3.3 70B Instruct
70B
llama
Commercial OK

Needs ~48 GB unified memory minimum at smallest quant; you have 24 GB available after OS overhead.

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 benchmarks@runlocalai.co and we'll prioritize it.