Can MiniMax M2.7 run on Mac Studio M3 Ultra 256GB?

YES — Tight Fit

S85Excellent

Estimated from fit model

MiniMax M2.7 needs ~172.6 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With UD-IQ4_XS quantization, expect ~20 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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

F16 (Maximum quality) — 503.8 GB, exceeds 184.3 GB available

503.8 GB required184.3 GB available

273% VRAM needed

319.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.7 tok/s

TTFT

51672 ms

Safe context

Memory

503.8 GB / 184.3 GB

Offload

60%

Memory breakdown

Weights471.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom27.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMiniMax M2.7 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: 3.7 tok/s decode · 51.7s TTFT (warm) · 9 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

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	20.0 tok/s	5284 ms	65K
Coding	S	Tight fit	20.0 tok/s	9687 ms	65K
Agentic Coding	S	Runs with offload	20.0 tok/s	14090 ms	65K
Reasoning	S	Tight fit	20.0 tok/s	11448 ms	65K
RAG	S	Runs with offload	20.0 tok/s	17612 ms	65K

Quantization options

How MiniMax M2.7 (230B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	89.7 GB	Low	A83
Q3_K_S	3	112.7 GB	Low	A84
NVFP4	4	128.8 GB	Medium	A84
Q4_K_MBest for your GPU	4	140.3 GB	Medium	A84
Q5_K_M	5	165.6 GB	High	F0
Q6_K	6	188.6 GB	High	F0
Q8_0	8	246.1 GB	Very High	F0
F16	16	471.5 GB	Maximum	F0

Get started

Copy-paste commands to run MiniMax M2.7 on your machine.

Run

lms load MiniMax-M2.7 && lms server start

Your hardware

More models your Mac Studio M3 Ultra 256GB can run

Model	Params	Grade	Decode	Capabilities
DeepSeek V4 Flash	284B	S	17.8 tok/s
Qwen 3 235B A22B	235B	S	11.3 tok/s

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run MiniMax M2.7?

Yes, Mac Studio M3 Ultra 256GB can run MiniMax M2.7 with a S grade (Tight fit). Expected decode speed: 20.0 tok/s.

How much VRAM does MiniMax M2.7 need?

MiniMax M2.7 (230B parameters) requires approximately 172.6 GB of memory with UD-IQ4_XS quantization.

What is the best quantization for MiniMax M2.7?

The recommended quantization for MiniMax M2.7 is UD-IQ4_XS, which balances quality and memory efficiency.

What speed will MiniMax M2.7 run at on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, MiniMax M2.7 achieves approximately 20.0 tokens per second decode speed with a time-to-first-token of 9687ms using UD-IQ4_XS quantization.

Can Mac Studio M3 Ultra 256GB run MiniMax M2.7 for coding?

For coding workloads, MiniMax M2.7 on Mac Studio M3 Ultra 256GB receives a S grade with 20.0 tok/s and 65K context.

What context window can MiniMax M2.7 use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, MiniMax M2.7 can safely use up to 65K tokens of context. The model's official context limit is 205K, but available memory constrains the safe maximum.

What should I upgrade first if MiniMax M2.7 feels slow on Mac Studio M3 Ultra 256GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for MiniMax M2.7?

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 MiniMax M2.7

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