What can Apple Mac Studio (M3 Ultra) run locally?

236 models fit comfortably on Apple Mac Studio (M3 Ultra) with 192 GB system RAM. 1 more run with tradeoffs. Top pick: Ministral 3B Instruct.

What's the recommended runner for this hardware?

MLX-LM (Apple Metal). This is the runner that gives best tokens-per-second on Apple Mac Studio (M3 Ultra) based on macos support and the GPU backend.

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

Build: Apple Mac Studio (M3 Ultra) + — + 192 GB RAM (macos)

Memory: 192 GB unified memory

Runner: MLX-LM (Apple Metal)

Any Chat Coding Agents Reasoning Vision Long context Creative

Runs comfortably
236 models

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

#1Ministral 3B Instruct

mistral

Quant: Q4_K_MContext: 8,192VRAM: 4.0 GBHeadroom: 180.0 GB

243

tok/s

Estimated

Weights

1.90 GB

KV cache

1.50 GB

Activations

0.10 GB

Runtime

0.50 GB

Model details →

#2Phi-3.5 Mini Instruct

3.8B

phi

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 6.7 GBHeadroom: 177.3 GB

ollama run phi3.5:3.8b

109

tok/s

Estimated

Weights

4.10 GB

KV cache

1.90 GB

Activations

0.21 GB

Runtime

0.50 GB

Model details →

#3Falcon Mamba 7B

falcon

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 8.4 GBHeadroom: 175.6 GB

104

tok/s

Estimated

Weights

4.20 GB

KV cache

3.50 GB

Activations

0.22 GB

Runtime

0.50 GB

Model details →

#4Codestral Mamba 7B

mistral

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 8.4 GBHeadroom: 175.6 GB

104

tok/s

Estimated

Weights

4.20 GB

KV cache

3.50 GB

Activations

0.22 GB

Runtime

0.50 GB

Model details →

#5Gemma 4 E4B (Effective 4B)

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 7.1 GBHeadroom: 176.9 GB

ollama run gemma4:e4b

104

tok/s

Estimated

Weights

4.40 GB

KV cache

2.00 GB

Activations

0.23 GB

Runtime

0.50 GB

Model details →

#6Command R7B (12-2024)

command-r

Quant: Q4_K_MContext: 8,192VRAM: 9.1 GBHeadroom: 174.9 GB

tok/s

Estimated

Weights

4.40 GB

KV cache

4.00 GB

Activations

0.23 GB

Runtime

0.50 GB

Model details →

#7Ministral 8B Instruct

mistral

Quant: Q4_K_MContext: 8,192VRAM: 9.8 GBHeadroom: 174.2 GB

tok/s

Estimated

Weights

5.00 GB

KV cache

4.00 GB

Activations

0.26 GB

Runtime

0.50 GB

Model details →

#8Jamba 1.5 Mini

52B

other

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 58.0 GBHeadroom: 126.0 GB

tok/s

Estimated

Weights

30.00 GB

KV cache

26.00 GB

Activations

1.51 GB

Runtime

0.50 GB

Model details →

#9Qwen 2.5 7B Instruct

qwen

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 9.5 GBHeadroom: 174.5 GB

ollama run qwen2.5:7b

tok/s

Estimated

Weights

8.10 GB

KV cache

0.47 GB

Activations

0.41 GB

Runtime

0.50 GB

Model details →

#10Qwen 3 8B

qwen

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 13.1 GBHeadroom: 170.9 GB

ollama run qwen3:8b

tok/s

Estimated

Weights

8.20 GB

KV cache

4.00 GB

Activations

0.42 GB

Runtime

0.50 GB

Model details →

#11InternLM 2.5 7B Chat

internlm

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 8.6 GBHeadroom: 175.4 GB

104

tok/s

Estimated

Weights

4.40 GB

KV cache

3.50 GB

Activations

0.23 GB

Runtime

0.50 GB

Model details →

#12DeepSeek V2 Lite Chat

15.7B

deepseek

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 17.4 GBHeadroom: 166.6 GB

304

tok/s

Estimated

Weights

8.60 GB

KV cache

7.85 GB

Activations

0.44 GB

Runtime

0.50 GB

Model details →

Runs with tradeoffs
1 models

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

Llama 3.1 Nemotron Ultra 253B

253B

llama

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 183.3 GBHeadroom: 0.7 GB

• Tight VRAM fit — only 0.7 GB headroom left for context growth

tok/s

Estimated

Weights

144.00 GB

KV cache

31.63 GB

Activations

7.20 GB

Runtime

0.50 GB

Model details →

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.

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

Needs ~1024 GB unified memory minimum at smallest quant; you have 184 GB available after OS overhead.

—

Qwen 3.5 235B-A17B (MoE)

397B

qwen

Commercial OK

Needs ~256 GB unified memory minimum at smallest quant; you have 184 GB available after OS overhead.

—

DeepSeek R1 (671B reasoning)

671B

deepseek

Commercial OK

Needs ~420 GB unified memory minimum at smallest quant; you have 184 GB available after OS overhead.

—

DeepSeek V4 Flash (284B MoE)

284B

deepseek

Commercial OK

Needs ~192 GB unified memory minimum at smallest quant; you have 184 GB available after OS overhead.

—

GLM-5.2

753B

glm

Commercial OK

Needs ~528 GB unified memory minimum at smallest quant; you have 184 GB available after OS overhead.

—

How to read these numbers

Measured here

Measured here - RunLocalAI ran this exact combo on owner hardware with public evidence.

Source-backed

Source-backed / community - a reproduced public source supports the speed, but it is not labeled as owner-measured.

Extrapolated

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

Estimated

Estimated - formula based on VRAM bandwidth and model architecture; not a benchmark row.

RunLocalAI Will-It-Run Framework →

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

Runs comfortably236 models

Runs with tradeoffs1 models

What if you upgraded?

Move up an Apple memory tier

Won't runtop 5 popular models

How to read these numbers

Runs comfortably
236 models

Runs with tradeoffs
1 models

Won't run
top 5 popular models