Can MiniMax M2.7 run on Mac mini M2 24GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

MiniMax M2.7 needs ~147.6 GB but Mac mini M2 24GB only has 17.3 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity

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) — 478.8 GB, exceeds 17.3 GB available

478.8 GB required17.3 GB available

2768% VRAM needed

461.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

478.8 GB / 17.3 GB

Offload

100%

Memory breakdown

Weights471.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMiniMax M2.7 on Mac mini M2 24GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable shared or unified memory is the main blocker for this model.

Not enough usable memory

The model needs 147.6 GB, but this setup only exposes 17.3 GB of usable shared or unified memory.

Best improvement path

Move to a larger memory pool

A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Quantization options

How MiniMax M2.7 (230B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	89.7 GB	Low	F0
Q3_K_S	3	112.7 GB	Low	F0
NVFP4	4	128.8 GB	Medium	F0
Q4_K_M	4	140.3 GB	Medium	F0
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

Upgrade options

Hardware that runs MiniMax M2.7 well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+232)819 GB/s (+719)

Makes the model fit on the accelerator instead of staying completely out of reach.20 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$6,999 MSRP

AMD Instinct MI350X 288GBBest value

288 GB VRAM (+264)8000 GB/s (+7900)

Makes the model fit on the accelerator instead of staying completely out of reach.135.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$8,000 MSRP

Frequently asked questions

Can Mac mini M2 24GB run MiniMax M2.7?

No, MiniMax M2.7 requires more memory than Mac mini M2 24GB provides.

How much VRAM does MiniMax M2.7 need?

MiniMax M2.7 (230B parameters) requires approximately 147.6 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 mini M2 24GB?

On Mac mini M2 24GB, MiniMax M2.7 achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using UD-IQ4_XS quantization.

Can Mac mini M2 24GB run MiniMax M2.7 for coding?

For coding workloads, MiniMax M2.7 on Mac mini M2 24GB receives a F grade with 2.0 tok/s and 4K context.

What context window can MiniMax M2.7 use on Mac mini M2 24GB?

On Mac mini M2 24GB, MiniMax M2.7 can safely use up to 4K 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 mini M2 24GB?

Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Is unified memory on Mac mini M2 24GB as fast as VRAM for MiniMax M2.7?

Not always. Mac mini M2 24GB 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 mini M2 24GB See all hardware for MiniMax M2.7

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

2.0 tok/s

52800 ms

Coding

Too heavy

2.0 tok/s

96800 ms

Agentic Coding

Too heavy

2.0 tok/s

140800 ms

Reasoning

Too heavy

2.0 tok/s

114400 ms

RAG

Too heavy

2.0 tok/s

176000 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

89.7 GB

Low

Q3_K_S

112.7 GB

Low

NVFP4

128.8 GB

Medium

Q4_K_M

140.3 GB

Medium

Q5_K_M

165.6 GB

High

Q6_K

188.6 GB

High

Q8_0

246.1 GB

Very High

F16

471.5 GB

Maximum

Frequently asked questions

Can Mac mini M2 24GB run MiniMax M2.7?

No, MiniMax M2.7 requires more memory than Mac mini M2 24GB provides.

How much VRAM does MiniMax M2.7 need?

MiniMax M2.7 (230B parameters) requires approximately 147.6 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 mini M2 24GB?

On Mac mini M2 24GB, MiniMax M2.7 achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using UD-IQ4_XS quantization.

Can Mac mini M2 24GB run MiniMax M2.7 for coding?

For coding workloads, MiniMax M2.7 on Mac mini M2 24GB receives a F grade with 2.0 tok/s and 4K context.

What context window can MiniMax M2.7 use on Mac mini M2 24GB?

On Mac mini M2 24GB, MiniMax M2.7 can safely use up to 4K 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 mini M2 24GB?

Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Is unified memory on Mac mini M2 24GB as fast as VRAM for MiniMax M2.7?