Can Command A 111B run on Mac Studio M2 Ultra 128GB?

YES — Tight Fit

S86Excellent

○Estimated from fit model

Command A 111B needs ~86.3 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~7 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: HighStack: StandardBottleneck: Memory bandwidth

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) — 86.3 GB, 7.5 tok/s, Tight fit

86.3 GB required92.2 GB available

94% VRAM used

Fit status

Tight fit

Decode

7.5 tok/s

TTFT

25879 ms

Safe context

40K

Memory

86.3 GB / 92.2 GB

Memory breakdown

Weights67.7 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsCommand A 111B on Mac Studio M2 Ultra 128GB
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: 7.5 tok/s decode · 25.9s TTFT (warm) · 19 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	6.9 tok/s	15410 ms	40K
Coding	S	Tight fit	6.9 tok/s	28251 ms	40K
Agentic Coding	S	Runs with offload	6.9 tok/s	41093 ms	40K
Reasoning	S	Tight fit	6.9 tok/s	33388 ms	40K
RAG	S	Runs with offload	6.9 tok/s	51366 ms	40K

Quantization options

How Command A 111B (111B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	43.3 GB	Low	S87
Q3_K_S	3	54.4 GB	Low	S88
NVFP4	4

Get started

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

Run

ollama run command-a

Your hardware

More models your Mac Studio M2 Ultra 128GB can run

Model	Params	Grade	Decode	Capabilities
👁 Mistral Devstral 2 123B Instruct	123B	S	6.3 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S

Frequently asked questions

See all results for Mac Studio M2 Ultra 128GB See all hardware for Command A 111B

URL: https://willitrunai.com/can-run/command-a-111b-on-m2-ultra-128gb

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