Can Kimi Linear 48B A3B run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

A79Great

Estimated from fit model

Kimi Linear 48B A3B needs ~45.8 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: TransformersCapacity: 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) — 45.8 GB, 15.8 tok/s, Runs well

45.8 GB required92.2 GB available

50% VRAM used

Fit status

Runs well

Decode

15.8 tok/s

TTFT

12217 ms

Safe context

816K

Memory

45.8 GB / 92.2 GB

Memory breakdown

Weights29.3 GB

KV Cache0.9 GB

Runtime1.8 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsKimi Linear 48B A3B on Mac Studio M2 Ultra 128GB
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: 15.8 tok/s decode · 12.2s TTFT (warm) · 40 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	A	Runs well	15.8 tok/s	6664 ms	816K
Coding	A	Runs well	15.8 tok/s	12217 ms	816K
Agentic Coding	A	Runs well	15.8 tok/s	17770 ms	816K
Reasoning	A	Runs well	15.8 tok/s	14438 ms	816K
RAG	A	Runs well	15.8 tok/s	22212 ms	816K

Quantization options

How Kimi Linear 48B A3B (48B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	18.7 GB	Low	A73
Q3_K_S	3	23.5 GB	Low	A74
NVFP4	4	26.9 GB	Medium	A74
Q4_K_M	4	29.3 GB	Medium	A75
Q5_K_M	5	34.6 GB	High	A76
Q6_K	6	39.4 GB	High	A77
Q8_0Best for your GPU	8	51.4 GB	Very High	A80
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

Your hardware

More models your Mac Studio M2 Ultra 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	5.8 tok/s
Qwen 3.5 122B A10B	122B	S	16.9 tok/s
Mistral Small 4 119B	119B	S	18.1 tok/s
GPT-OSS 120B	117B	S	7.1 tok/s
Command A 111B	111B	S	7.5 tok/s

Frequently asked questions

Can Mac Studio M2 Ultra 128GB run Kimi Linear 48B A3B?

Yes, Mac Studio M2 Ultra 128GB can run Kimi Linear 48B A3B with a A grade (Runs well). Expected decode speed: 15.8 tok/s.

How much VRAM does Kimi Linear 48B A3B need?

Kimi Linear 48B A3B (48B parameters) requires approximately 45.8 GB of memory with Q4_K_M quantization.

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

The recommended quantization for Kimi Linear 48B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Kimi Linear 48B A3B run at on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Kimi Linear 48B A3B achieves approximately 15.8 tokens per second decode speed with a time-to-first-token of 12217ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 128GB run Kimi Linear 48B A3B for coding?

For coding workloads, Kimi Linear 48B A3B on Mac Studio M2 Ultra 128GB receives a A grade with 15.8 tok/s and 816K context.

What context window can Kimi Linear 48B A3B use on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Kimi Linear 48B A3B can safely use up to 816K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for Kimi Linear 48B A3B?

Not always. Mac Studio M2 Ultra 128GB 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 M2 Ultra 128GB See all hardware for Kimi Linear 48B A3B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

15.8 tok/s

6664 ms

816K

Coding

Runs well

15.8 tok/s

12217 ms

816K

Agentic Coding

Runs well

15.8 tok/s

17770 ms

816K

Reasoning

Runs well

15.8 tok/s

14438 ms

816K

RAG

Runs well

15.8 tok/s

22212 ms

816K

Quantization options

How Kimi Linear 48B A3B (48B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	18.7 GB	Low	A73
Q3_K_S	3	23.5 GB	Low	A74
NVFP4	4	26.9 GB	Medium	A74
Q4_K_M	4	29.3 GB	Medium	A75
Q5_K_M	5	34.6 GB	High	A76
Q6_K	6	39.4 GB	High	A77
Q8_0Best for your GPU	8	51.4 GB	Very High	A80
F16	16	98.4 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

5.8 tok/s

Qwen 3.5 122B A10B

122B

16.9 tok/s

Mistral Small 4 119B

119B

18.1 tok/s

GPT-OSS 120B

117B

7.1 tok/s

Command A 111B

111B

7.5 tok/s