Is unified memory on MacBook Pro M4 Max 96GB as fast as VRAM for Kimi Linear 48B A3B?

Not always. MacBook Pro M4 Max 96GB 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.

Can Kimi Linear 48B A3B run on MacBook Pro M4 Max 96GB?

YES — Runs Great

A81Great

Estimated from fit model

Kimi Linear 48B A3B needs ~42.4 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~12 tok/s.

Runtime: TransformersCapacity: RoomyBandwidth: MediumStack: 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, 21.1 tok/s, Runs well

42.4 GB required69.1 GB available

61% VRAM used

Fit status

Runs well

Decode

21.1 tok/s

TTFT

9155 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 MacBook Pro M4 Max 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: 21.1 tok/s decode · 9.2s TTFT (warm) · 53 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	21.1 tok/s	4994 ms	478K
Coding	A	Runs well	11.7 tok/s	16479 ms	478K
Agentic Coding	A	Runs well	21.1 tok/s	13317 ms	478K
Reasoning	A	Runs well	21.1 tok/s	10820 ms	478K
RAG	A	Runs well	21.1 tok/s	16646 ms	478K

Quantization options

How Kimi Linear 48B A3B (48B params) fits at each quantization level on MacBook Pro M4 Max 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

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 MacBook Pro M4 Max 96GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 2.5 VL 72B	72B	S	14.9 tok/s
Qwen3-Coder-Next	80B	S	23.2 tok/s

Frequently asked questions

Can MacBook Pro M4 Max 96GB run Kimi Linear 48B A3B?

Yes, MacBook Pro M4 Max 96GB can run Kimi Linear 48B A3B with a A grade (Runs well). Expected decode speed: 11.7 tok/s.

How much VRAM does Kimi Linear 48B A3B need?

Kimi Linear 48B A3B (48B parameters) requires approximately 42.4 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 Max 96GB?

On MacBook Pro M4 Max 96GB, Kimi Linear 48B A3B achieves approximately 11.7 tokens per second decode speed with a time-to-first-token of 16479ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 96GB run Kimi Linear 48B A3B for coding?

For coding workloads, Kimi Linear 48B A3B on MacBook Pro M4 Max 96GB receives a A grade with 11.7 tok/s and 478K context.

What context window can Kimi Linear 48B A3B use on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, Kimi Linear 48B A3B can safely use up to 478K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.

See all results for MacBook Pro M4 Max 96GB See all hardware for Kimi Linear 48B A3B

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