Can Command R 35B run on MacBook Pro M3 Max 64GB?

Q: Is unified memory on MacBook Pro M3 Max 64GB as fast as VRAM for Command R 35B?

Not always. MacBook Pro M3 Max 64GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

YES — Runs Great

A76Great

Estimated from fit model

Command R 35B needs ~31.6 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~11 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 31.6 GB, 12.2 tok/s, Runs well

31.6 GB required46.1 GB available

69% VRAM used

Fit status

Runs well

Decode

12.2 tok/s

TTFT

15837 ms

Safe context

111K

Memory

31.6 GB / 46.1 GB

Memory breakdown

Weights21.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsCommand R 35B on MacBook Pro M3 Max 64GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 12.2 tok/s decode · 15.8s TTFT (warm) · 31 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	11.2 tok/s	9394 ms	111K
Coding	A	Runs well	11.2 tok/s	17222 ms	111K
Agentic Coding	A	Runs well	11.2 tok/s	25051 ms	111K
Reasoning	A	Runs well	11.2 tok/s	20354 ms	111K
RAG	A	Runs well	11.2 tok/s	31313 ms	111K

Quantization options

How Command R 35B (35B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	A71
Q3_K_S	3	17.2 GB	Low	A72
NVFP4	4

Get started

Copy-paste commands to run Command R 35B on your machine.

Run

ollama run command-r

Your hardware

More models your MacBook Pro M3 Max 64GB can run

Model	Params	Grade	Decode	Capabilities
Kimi Linear 48B A3B	48B	A	8.2 tok/s

Frequently asked questions

Can MacBook Pro M3 Max 64GB run Command R 35B?

Yes, MacBook Pro M3 Max 64GB can run Command R 35B with a A grade (Runs well). Expected decode speed: 11.2 tok/s.

How much VRAM does Command R 35B need?

Command R 35B (35B parameters) requires approximately 31.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Command R 35B?

The recommended quantization for Command R 35B is Q4_K_M, which balances quality and memory efficiency.

What speed will Command R 35B run at on MacBook Pro M3 Max 64GB?

On MacBook Pro M3 Max 64GB, Command R 35B achieves approximately 11.2 tokens per second decode speed with a time-to-first-token of 17222ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 64GB run Command R 35B for coding?

For coding workloads, Command R 35B on MacBook Pro M3 Max 64GB receives a A grade with 11.2 tok/s and 111K context.

What context window can Command R 35B use on MacBook Pro M3 Max 64GB?

On MacBook Pro M3 Max 64GB, Command R 35B can safely use up to 111K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Max 64GB as fast as VRAM for Command R 35B?

See all results for MacBook Pro M3 Max 64GB See all hardware for Command R 35B

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