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

Inference speed

Kimi Linear 48B A3B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Kimi Linear 48B A3B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~40 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?
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	40.1	Fits
2× RTX 4090 24GB	48 GB	Q4_K_M	29.3	Fits
RTX 5090 32GB	32 GB	Q4_K_M	25.8	Heavy offload
2× RTX 3090 24GB	48 GB	Q4_K_M	25.1	Fits
4× RTX 3060 12GB	48 GB	Q4_K_M	22.1	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	21.1	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	21.1	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	19.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	15.8	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	15.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	10.6	Offloads
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	8.2	Tight
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	7.5	Tight
RTX 4090 24GB	24 GB	Q4_K_M	7.1	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	6.4	Too big
RTX 3090 24GB	24 GB	Q4_K_M	6.1	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.5	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

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 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	26.9 GB	Medium	F0
Q4_K_M	4	29.3 GB	Medium	F0
Q5_K_M	5	34.6 GB	High	F0
Q6_K	6	39.4 GB	High	F0
Q8_0	8	51.4 GB	Very High	F0
F16	16	98.4 GB	Maximum	F0

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

升级选项

能流畅运行 Kimi Linear 48B A3B 的硬件

Mac mini M4 64GB经济之选

64 GB Unified (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.5.3 tok/s 解码

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 64GB最佳性价比

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 解码

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 升级

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 解码

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

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

Yes, Mac mini M4 32GB can run Kimi Linear 48B A3B at Q2_K quantization (Very compromised (needs ~1.4 GB host RAM)). The recommended Q4_K_M requires 35.5 GB which exceeds available memory, but at Q2_K it needs only 24.9 GB. Expected decode speed: 5.8 tok/s.

How much VRAM does Kimi Linear 48B A3B need?

Kimi Linear 48B A3B (48B parameters) requires approximately 35.5 GB at Q4_K_M quantization. On Mac mini M4 32GB, it fits at Q2_K using 24.9 GB.

What is the best quantization for Kimi Linear 48B A3B?

The recommended quantization is Q4_K_M, but on Mac mini M4 32GB the best fitting quantization is Q2_K, which uses 24.9 GB.

What speed will Kimi Linear 48B A3B run at on Mac mini M4 32GB?

On Mac mini M4 32GB, Kimi Linear 48B A3B achieves approximately 5.8 tokens per second decode speed with a time-to-first-token of 33501ms using Q2_K quantization.

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

For coding workloads, Kimi Linear 48B A3B on Mac mini M4 32GB receives a F grade with 2.7 tok/s and 4K context.

What context window can Kimi Linear 48B A3B use on Mac mini M4 32GB?

On Mac mini M4 32GB, Kimi Linear 48B A3B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 1.0M, but available memory constrains the safe maximum.

What should I upgrade first if Kimi Linear 48B A3B feels slow on Mac mini M4 32GB?

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.

Is unified memory on Mac mini M4 32GB as fast as VRAM for Kimi Linear 48B A3B?

Not always. Mac mini M4 32GB 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 M4 32GB See all hardware for Kimi Linear 48B A3B

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