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

Build: Mac Studio M3 Ultra 256GB

Memory: 256 GB unified memory
Runner: MLX-LM (Apple Metal)

Runs comfortably
114 models

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

#1Llama 3.1 Nemotron Nano 8B
8B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 238.3 GB
108
tok/s
Estimated
Weights
4.90 GB
KV cache
4.00 GB
Activations
0.25 GB
Runtime
0.50 GB
#2RefinedNeuro RN TR R1
8B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 238.3 GB
ollama run RefinedNeuro/RN_TR_R1:latest
108
tok/s
Estimated
Weights
4.90 GB
KV cache
4.00 GB
Activations
0.25 GB
Runtime
0.50 GB
#3DeepSeek R1 Distill Llama 8B
8B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.4 GBHeadroom: 238.6 GB
108
tok/s
Estimated
Weights
4.70 GB
KV cache
4.00 GB
Activations
0.24 GB
Runtime
0.50 GB
#4DeepSeek R1 Distill Qwen 7B
7B
deepseek
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 12.5 GBHeadroom: 235.5 GB
ollama run deepseek-r1:7b
70
tok/s
Estimated
Weights
8.10 GB
KV cache
3.50 GB
Activations
0.41 GB
Runtime
0.50 GB
#5DeepSeek V4 Flash (284B MoE)
284B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 206.1 GBHeadroom: 41.9 GB
67
tok/s
Estimated
Weights
162.00 GB
KV cache
35.50 GB
Activations
8.10 GB
Runtime
0.50 GB
#6Phi-4 Reasoning 14B
14B
phi
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 16.3 GBHeadroom: 231.7 GB
ollama run phi4-reasoning:14b
62
tok/s
Estimated
Weights
8.40 GB
KV cache
7.00 GB
Activations
0.43 GB
Runtime
0.50 GB
#7DeepSeek R1 Distill Qwen 14B
14B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 16.3 GBHeadroom: 231.7 GB
ollama run deepseek-r1:14b
62
tok/s
Estimated
Weights
8.40 GB
KV cache
7.00 GB
Activations
0.43 GB
Runtime
0.50 GB
#8Qwen 2.5 Math 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 6.9 GBHeadroom: 241.1 GB
124
tok/s
Estimated
Weights
4.40 GB
KV cache
1.75 GB
Activations
0.22 GB
Runtime
0.50 GB
#9InternLM 2.5 7B Chat
7B
internlm
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 8.6 GBHeadroom: 239.4 GB
124
tok/s
Estimated
Weights
4.40 GB
KV cache
3.50 GB
Activations
0.23 GB
Runtime
0.50 GB
Quant: Q4_K_MContext: 8,192VRAM: 34.0 GBHeadroom: 214.0 GB
28
tok/s
Estimated
Weights
17.10 GB
KV cache
15.50 GB
Activations
0.86 GB
Runtime
0.50 GB
#11QwQ 32B Preview
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 36.5 GBHeadroom: 211.5 GB
ollama run qwq:32b
27
tok/s
Estimated
Weights
19.00 GB
KV cache
16.00 GB
Activations
0.96 GB
Runtime
0.50 GB
#12RefinedNeuro RN TR R2
8B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 238.3 GB
ollama run RefinedNeuro/RN_TR_R2:latest
108
tok/s
Estimated
Weights
4.90 GB
KV cache
4.00 GB
Activations
0.25 GB
Runtime
0.50 GB

What if you upgraded?

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

Move up an Apple memory tier

Varies by configuration. Check Apple's current pricing

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

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 248 GB available after OS overhead.

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

Needs ~420 GB unified memory minimum at smallest quant; you have 248 GB available after OS overhead.

—
DeepSeek V3 (671B MoE)
671B
deepseek
Commercial OK

Needs ~420 GB unified memory minimum at smallest quant; you have 248 GB available after OS overhead.

—
Kimi K2.6
1000B
other
Commercial OK

Needs ~700 GB unified memory minimum at smallest quant; you have 248 GB available after OS overhead.

—
Llama 4 Maverick
400B
llama
Commercial OK

Needs ~280 GB unified memory minimum at smallest quant; you have 248 GB available after OS overhead.

—
Cloud GPU rental

Not enough VRAM for these models? Rent a GPU by the hour

The models above need more memory than 256 GB unified memory. You can rent a GPU with enough VRAM by the hour to run one of them, or to test it before you buy hardware. For daily use, owning a card usually costs less over time.

RunPod

billed per GPU-hour

Hourly GPU pods, in community-cloud and secure-cloud tiers.

The two tiers run on different hosts and terms. Check which tier a pod is on before a long job.

Rent on RunPod

Vast.ai

billed per GPU-hour

Marketplace of host-listed GPUs; price and reliability vary by host.

Host quality varies. Filter for verified hosts and check the host's reliability before a long run.

Rent on Vast.ai

Referral links: RunLocalAI may earn a commission or account credits if you sign up and rent, at no extra cost to you. Hourly rates and which GPUs are listed change all the time, so check the provider's page before you start. Editorial opinions are independent of the referral relationship; see how we make money.

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.

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