Can Command A 111B run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

S86Excellent

Estimated from fit model

Command A 111B needs ~100.2 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~8 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 100.2 GB, 9.0 tok/s, Runs well

100.2 GB required184.3 GB available

54% VRAM used

Fit status

Runs well

Decode

9.0 tok/s

TTFT

21561 ms

Safe context

262K

Memory

100.2 GB / 184.3 GB

Memory breakdown

Weights67.7 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom27.6 GB

See how fast it feels

See how fast it feelsCommand A 111B on Mac Studio M3 Ultra 256GB
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: 9.0 tok/s decode · 21.6s TTFT (warm) · 22 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	S	Runs well	9.0 tok/s	11761 ms	262K
Coding	S	Runs well	8.2 tok/s	23538 ms	262K
Agentic Coding	S	Runs well	9.0 tok/s	31362 ms	262K
Reasoning	S	Runs well	9.0 tok/s	25482 ms	262K
RAG	S	Runs well	9.0 tok/s	39202 ms	262K

Inference speed

Command A 111B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Command A 111B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~10 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	9.7	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	9.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	7.5	Tight
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	7.1	Tight
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	5.2	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	4.8	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	3.7	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	3.1	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	2.8	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.7	Too big
RTX 5090 32GB	32 GB	Q4_K_M	2.0	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.0	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Command A 111B (111B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	43.3 GB	Low	A81
Q3_K_S	3	54.4 GB	Low	A83
NVFP4	4	62.2 GB	Medium	A84
Q4_K_M	4	67.7 GB	Medium	A84
Q5_K_M	5	79.9 GB	High	S86
Q6_K	6	91.0 GB	High	S87
Q8_0Best for your GPU	8	118.8 GB	Very High	S88
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

Your hardware

More models your Mac Studio M3 Ultra 256GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	8.1 tok/s
Qwen 3.5 122B A10B	122B	S	34.7 tok/s
DeepSeek V4 Flash	284B	S	17.8 tok/s
Mistral Small 4 119B	119B	S	37.6 tok/s
GPT-OSS 120B	117B	S	8.5 tok/s

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run Command A 111B?

Yes, Mac Studio M3 Ultra 256GB can run Command A 111B with a S grade (Runs well). Expected decode speed: 8.2 tok/s.

How much VRAM does Command A 111B need?

Command A 111B (111B parameters) requires approximately 100.2 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 Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Command A 111B achieves approximately 8.2 tokens per second decode speed with a time-to-first-token of 23538ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 256GB run Command A 111B for coding?

For coding workloads, Command A 111B on Mac Studio M3 Ultra 256GB receives a S grade with 8.2 tok/s and 262K context.

What context window can Command A 111B use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Command A 111B can safely use up to 262K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for Command A 111B?

Not always. Mac Studio M3 Ultra 256GB 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 Mac Studio M3 Ultra 256GB See all hardware for Command A 111B

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