Can LFM2 24B run on MacBook Pro M2 Max 96GB?

YES — Runs Great

A78Great

Estimated from fit model

LFM2 24B needs ~28.3 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~17 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 28.3 GB, 17.0 tok/s, Runs well

28.3 GB required69.1 GB available

41% VRAM used

Fit status

Runs well

Decode

17.0 tok/s

TTFT

11364 ms

Safe context

131K

Memory

28.3 GB / 69.1 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsLFM2 24B on MacBook Pro M2 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: 17.0 tok/s decode · 11.4s TTFT (warm) · 43 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	17.0 tok/s	6199 ms	131K
Coding	A	Runs well	17.0 tok/s	11364 ms	131K
Agentic Coding	A	Runs well	17.0 tok/s	16530 ms	131K
Reasoning	A	Runs well	17.0 tok/s	13431 ms	131K
RAG	A	Runs well	17.0 tok/s	20663 ms	131K

Quantization options

How LFM2 24B (24B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A74
Q3_K_S	3	11.8 GB	Low	A74
NVFP4	4	13.4 GB	Medium	A75
Q4_K_M	4	14.6 GB	Medium	A75
Q5_K_M	5	17.3 GB	High	A75
Q6_K	6	19.7 GB	High	A76
Q8_0	8	25.7 GB	Very High	A77
F16Best for your GPU	16	49.2 GB	Maximum	A81

Get started

Copy-paste commands to run LFM2 24B on your machine.

Run

ollama run lfm2

Your hardware

More models your MacBook Pro M2 Max 96GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	35.1 tok/s
Qwen 3.5 27B	27B	S	15.2 tok/s
Qwen 3.6 27B	27B	S	11.6 tok/s
Qwen 3.6 35B A3B	35B	S	32.4 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	36.3 tok/s

Frequently asked questions

Can MacBook Pro M2 Max 96GB run LFM2 24B?

Yes, MacBook Pro M2 Max 96GB can run LFM2 24B with a A grade (Runs well). Expected decode speed: 17.0 tok/s.

How much VRAM does LFM2 24B need?

LFM2 24B (24B parameters) requires approximately 28.3 GB of memory with Q4_K_M quantization.

What is the best quantization for LFM2 24B?

The recommended quantization for LFM2 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will LFM2 24B run at on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, LFM2 24B achieves approximately 17.0 tokens per second decode speed with a time-to-first-token of 11364ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 96GB run LFM2 24B for coding?

For coding workloads, LFM2 24B on MacBook Pro M2 Max 96GB receives a A grade with 17.0 tok/s and 131K context.

What context window can LFM2 24B use on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, LFM2 24B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Max 96GB as fast as VRAM for LFM2 24B?

Not always. MacBook Pro M2 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.

See all results for MacBook Pro M2 Max 96GB See all hardware for LFM2 24B

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