Can Kimi Linear 48B A3B run on MacBook Pro M4 Pro 64GB?

YES — Tight Fit

A80Great

Estimated from fit model

Kimi Linear 48B A3B needs ~38.9 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q4_K_M quantization, expect ~13 tok/s.

Runtime: TransformersCapacity: TightBandwidth: LowStack: 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) — 38.9 GB, 12.9 tok/s, Tight fit

38.9 GB required46.1 GB available

84% VRAM used

Fit status

Tight fit

Decode

12.9 tok/s

TTFT

14981 ms

Safe context

140K

Memory

38.9 GB / 46.1 GB

Memory breakdown

Weights29.3 GB

KV Cache0.9 GB

Runtime1.8 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsKimi Linear 48B A3B on MacBook Pro M4 Pro 64GB
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: 12.9 tok/s decode · 15.0s TTFT (warm) · 32 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	Tight fit	12.9 tok/s	8172 ms	140K
Coding	A	Tight fit	12.9 tok/s	14981 ms	140K
Agentic Coding	A	Tight fit	12.9 tok/s	21791 ms	140K
Reasoning	A	Tight fit	12.9 tok/s	17705 ms	140K
RAG	A	Tight fit	12.9 tok/s	27239 ms	140K

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 MacBook Pro M4 Pro 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	18.7 GB	Low	A79
Q3_K_S	3	23.5 GB	Low	A81
NVFP4	4	26.9 GB	Medium	A80
Q4_K_M	4	29.3 GB	Medium	A80
Q5_K_MBest for your GPU	5	34.6 GB	High	A80
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

Frequently asked questions

Can MacBook Pro M4 Pro 64GB run Kimi Linear 48B A3B?

Yes, MacBook Pro M4 Pro 64GB can run Kimi Linear 48B A3B with a A grade (Tight fit). Expected decode speed: 12.9 tok/s.

How much VRAM does Kimi Linear 48B A3B need?

Kimi Linear 48B A3B (48B parameters) requires approximately 38.9 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 MacBook Pro M4 Pro 64GB?

On MacBook Pro M4 Pro 64GB, Kimi Linear 48B A3B achieves approximately 12.9 tokens per second decode speed with a time-to-first-token of 14981ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 64GB run Kimi Linear 48B A3B for coding?

For coding workloads, Kimi Linear 48B A3B on MacBook Pro M4 Pro 64GB receives a A grade with 12.9 tok/s and 140K context.

What context window can Kimi Linear 48B A3B use on MacBook Pro M4 Pro 64GB?

On MacBook Pro M4 Pro 64GB, Kimi Linear 48B A3B can safely use up to 140K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Pro 64GB as fast as VRAM for Kimi Linear 48B A3B?

Not always. MacBook Pro M4 Pro 64GB 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 MacBook Pro M4 Pro 64GB See all hardware for Kimi Linear 48B A3B

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