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

213 models fit comfortably on Apple Mac Studio (M3 Ultra) with 192 GB system RAM. 1 more run with tradeoffs. Top pick: Llama 3.1 Nemotron Nano 8B.

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 reasoning?

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
213 models

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

#1Llama 3.1 Nemotron Nano 8B

llama

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 174.3 GB

tok/s

Estimated

Weights

4.90 GB

KV cache

4.00 GB

Activations

0.25 GB

Runtime

0.50 GB

Model details →

#2RefinedNeuro RN TR R1

llama

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 174.3 GB

ollama run RefinedNeuro/RN_TR_R1:latest

tok/s

Estimated

Weights

4.90 GB

KV cache

4.00 GB

Activations

0.25 GB

Runtime

0.50 GB

Model details →

#3DeepSeek R1 Distill Llama 8B

deepseek

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 9.4 GBHeadroom: 174.6 GB

tok/s

Estimated

Weights

4.70 GB

KV cache

4.00 GB

Activations

0.24 GB

Runtime

0.50 GB

Model details →

#4NVIDIA Nemotron Nano 9B v2 Japanese

other

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 10.3 GBHeadroom: 173.7 GB

tok/s

Estimated

Weights

5.00 GB

KV cache

4.50 GB

Activations

0.26 GB

Runtime

0.50 GB

Model details →

#5DeepSeek R1 Distill Qwen 7B

deepseek

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 12.5 GBHeadroom: 171.5 GB

ollama run deepseek-r1:7b

tok/s

Estimated

Weights

8.10 GB

KV cache

3.50 GB

Activations

0.41 GB

Runtime

0.50 GB

Model details →

#6Phi-4 Reasoning 14B

14B

phi

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 16.3 GBHeadroom: 167.7 GB

ollama run phi4-reasoning:14b

tok/s

Estimated

Weights

8.40 GB

KV cache

7.00 GB

Activations

0.43 GB

Runtime

0.50 GB

Model details →

#7DeepSeek R1 Distill Qwen 14B

14B

deepseek

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 16.3 GBHeadroom: 167.7 GB

ollama run deepseek-r1:14b

tok/s

Estimated

Weights

8.40 GB

KV cache

7.00 GB

Activations

0.43 GB

Runtime

0.50 GB

Model details →

#8DeepSeek V3 Lite (16B MoE)

16B

deepseek

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 18.5 GBHeadroom: 165.5 GB

304

tok/s

Estimated

Weights

9.50 GB

KV cache

8.00 GB

Activations

0.48 GB

Runtime

0.50 GB

Model details →

#9DeepSeek R1 Distill Mistral 24B

24B

deepseek

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 27.2 GBHeadroom: 156.8 GB

tok/s

Estimated

Weights

14.00 GB

KV cache

12.00 GB

Activations

0.71 GB

Runtime

0.50 GB

Model details →

#10InternLM 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 →

#11Qwen 2.5 Math 7B

qwen

Commercial OK

Quant: Q4_K_MContext: 4,096VRAM: 6.9 GBHeadroom: 177.1 GB

104

tok/s

Estimated

Weights

4.40 GB

KV cache

1.75 GB

Activations

0.22 GB

Runtime

0.50 GB

Model details →

#12Qwen 3 7B

qwen

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 →

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 →

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Runs comfortably213 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
213 models

Runs with tradeoffs
1 models

Won't run
top 5 popular models