Can Command A 111B run on MacBook Pro M4 Max 96GB?

BARELY — Tight on Memory

A70Great

Estimated from fit model

Command A 111B needs ~82.9 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~7 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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) — 82.9 GB, 7.4 tok/s, Very compromised (needs ~11.2 GB host RAM)

82.9 GB required69.1 GB available

120% VRAM needed

13.8 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~11.2 GB host RAM)

Decode

7.4 tok/s

TTFT

26232 ms

Safe context

Memory

82.9 GB / 69.1 GB

Offload

20%

Memory breakdown

Weights67.7 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsCommand A 111B on MacBook Pro M4 Max 96GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 7.4 tok/s decode · 26.2s TTFT (warm) · 19 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 11.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Very compromised (needs ~9.9 GB host RAM)	7.6 tok/s	13883 ms	4K
Coding	A	Very compromised (needs ~11.2 GB host RAM)	7.4 tok/s	26232 ms	4K
Agentic Coding	F	Too heavy	7.0 tok/s	40386 ms	4K
Reasoning	A	Very compromised (needs ~11.2 GB host RAM)	7.4 tok/s	31001 ms	4K
RAG	F	Too heavy	7.0 tok/s	50483 ms	4K

Quantization options

How Command A 111B (111B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	43.3 GB	Low	S88
Q3_K_SBest for your GPU	3	54.4 GB	Low	S88
NVFP4	4	62.2 GB	Medium	F0
Q4_K_M	4	67.7 GB	Medium	F0
Q5_K_M	5	79.9 GB	High	F0
Q6_K	6	91.0 GB	High	F0
Q8_0	8	118.8 GB	Very High	F0
F16	16	227.6 GB	Maximum	F0

Get started

Copy-paste commands to run Command A 111B on your machine.

Run

ollama run command-a

Frequently asked questions

Can MacBook Pro M4 Max 96GB run Command A 111B?

Yes, MacBook Pro M4 Max 96GB can run Command A 111B with a A grade (Very compromised (needs ~11.2 GB host RAM)). Expected decode speed: 7.4 tok/s.

How much VRAM does Command A 111B need?

Command A 111B (111B parameters) requires approximately 82.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Command A 111B?

The recommended quantization for Command A 111B is Q4_K_M, which balances quality and memory efficiency.

What speed will Command A 111B run at on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, Command A 111B achieves approximately 7.4 tokens per second decode speed with a time-to-first-token of 26232ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 96GB run Command A 111B for coding?

For coding workloads, Command A 111B on MacBook Pro M4 Max 96GB receives a A grade with 7.4 tok/s and 4K context.

What context window can Command A 111B use on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, Command A 111B can safely use up to 4K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Command A 111B feels slow on MacBook Pro M4 Max 96GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Is unified memory on MacBook Pro M4 Max 96GB as fast as VRAM for Command A 111B?

Not always. MacBook Pro M4 Max 96GB 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.

See all results for MacBook Pro M4 Max 96GB See all hardware for Command A 111B

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