Can Kimi Linear 48B A3B run on Mac mini M4 32GB?

YES — With Q2_K

B68Good

○Estimated from fit model

Kimi Linear 48B A3B needs ~24.9 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q2_K quantization, expect ~6 tok/s.

Runtime: TransformersCapacity: OffloadBandwidth: Very lowStack: 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.

Kimi Linear 48B A3B at Q4_K_M needs 35.5 GB — too much for Mac mini M4 32GB (23.0 GB). Runs at Q2_K (24.9 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 35.5 GB, exceeds 23.0 GB available

35.5 GB required23.0 GB available

154% VRAM needed

12.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.7 tok/s

TTFT

72802 ms

Safe context

Memory

35.5 GB / 23.0 GB

Offload

40%

Memory breakdown

Weights29.3 GB

KV Cache0.9 GB

Runtime1.8 GB

Headroom3.5 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsKimi Linear 48B A3B on Mac mini M4 32GB
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: 2.7 tok/s decode · 72.8s TTFT (warm) · 7 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 10% 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 1.4 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	F	Too heavy	2.7 tok/s	38991 ms	4K
Coding	F	Too heavy	2.7 tok/s	72802 ms	4K
Agentic Coding	F	Too heavy	2.6 tok/s	109761 ms	4K
Reasoning	F	Too heavy	2.7 tok/s	86039 ms	4K
RAG	F	Too heavy	2.6 tok/s	137201 ms	4K

Quantization options

How Kimi Linear 48B A3B (48B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	18.7 GB	Low	F0
Q3_K_S	3	23.5 GB	Low	F0
NVFP4	4

Get started

Copy-paste commands to run Kimi Linear 48B A3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "moonshotai/Kimi-Linear-48B-A3B-Instruct" \
 --hf-file "Kimi-Linear-48B-A3B-Instruct-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Upgrade options

Hardware that runs Kimi Linear 48B A3B well

Mac mini M4 64GBBudget pick

64 GB Unified (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.5.3 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.

~$1,099 MSRP

MacBook Pro M4 Pro 64GBBest value

64 GB Unified (+32)273 GB/s (+153)

Makes the model fit on the accelerator instead of staying completely out of reach.12.9 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.

~$1,599 MSRP

Mac Studio M3 Ultra 96GBApple upgrade

96 GB Unified (+64)819 GB/s (+699)

Makes the model fit on the accelerator instead of staying completely out of reach.19 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.

~$3,999 MSRP

Frequently asked questions

See all results for Mac mini M4 32GB See all hardware for Kimi Linear 48B A3B

URL: https://willitrunai.com/can-run/kimi-linear-48b-a3b-on-m4-mini-32gb

⇱

Can Kimi Linear 48B A3B run on Mac mini M4 32GB?

YES — With Q2_K

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Kimi Linear 48B A3B well

Frequently asked questions