Can Kimi Linear 48B A3B run on Mac Studio M3 Ultra 96GB?

YES — Runs Great

A82Great

○Estimated from fit model

Kimi Linear 48B A3B needs ~42.4 GB VRAM. Mac Studio M3 Ultra 96GB has 69.1 GB. With Q4_K_M quantization, expect ~19 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) — 42.4 GB, 19.0 tok/s, Runs well

42.4 GB required69.1 GB available

61% VRAM used

Fit status

Runs well

Decode

19.0 tok/s

TTFT

10178 ms

Safe context

478K

Memory

42.4 GB / 69.1 GB

Memory breakdown

Weights29.3 GB

KV Cache0.9 GB

Runtime1.8 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsKimi Linear 48B A3B on Mac Studio M3 Ultra 96GB
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: 19.0 tok/s decode · 10.2s TTFT (warm) · 48 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	19.0 tok/s	5552 ms	478K
Coding	A	Runs well	19.0 tok/s	10178 ms	478K
Agentic Coding	A	Runs well	19.0 tok/s	14805 ms	478K
Reasoning	A	Runs well	19.0 tok/s	12029 ms	478K
RAG	A	Runs well	19.0 tok/s	18506 ms	478K

Quantization options

How Kimi Linear 48B A3B (48B params) fits at each quantization level on Mac Studio M3 Ultra 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	18.7 GB	Low	A75
Q3_K_S	3	23.5 GB	Low	A76
NVFP4	4	26.9 GB	Medium	A77
Q4_K_M	4	29.3 GB	Medium	A78
Q5_K_M	5	34.6 GB	High	A79
Q6_K	6	39.4 GB	High	A80
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 M3 Ultra 96GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 2.5 VL 72B	72B	S	13.8 tok/s
👁 Alibaba Qwen3-Coder-Next	80B	S	37.6 tok/s
👁 Meta Llama 3.3 70B	70B	A	14.2 tok/s

Frequently asked questions

See all results for Mac Studio M3 Ultra 96GB See all hardware for Kimi Linear 48B A3B

URL: https://willitrunai.com/can-run/kimi-linear-48b-a3b-on-m3-ultra-96gb

⇱ Kimi Linear 48B A3B on Mac Studio M3 Ultra 96GB? YES