Can LFM2 24B run on Mac mini M4 64GB?

YES — Runs Great

A78Great

Estimated — low-sample bucket· few comparable runs

LFM2 24B needs ~24.9 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~6 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 24.9 GB, 9.5 tok/s, Runs well

24.9 GB required46.1 GB available

54% VRAM used

Fit status

Runs well

Decode

9.5 tok/s

TTFT

20344 ms

Safe context

131K

Memory

24.9 GB / 46.1 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsLFM2 24B on Mac mini M4 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: 9.5 tok/s decode · 20.3s TTFT (warm) · 24 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	9.5 tok/s	11097 ms	131K
Coding	A	Runs well	5.9 tok/s	32804 ms	131K
Agentic Coding	A	Runs well	9.5 tok/s	29591 ms	131K
Reasoning	A	Runs well	9.5 tok/s	24043 ms	131K
RAG	A	Runs well	9.5 tok/s	36989 ms	131K

Quantization options

How LFM2 24B (24B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A76
Q3_K_S	3	11.8 GB	Low	A77
NVFP4	4	13.4 GB	Medium	A77
Q4_K_M	4	14.6 GB	Medium	A78
Q5_K_M	5	17.3 GB	High	A79
Q6_K	6	19.7 GB	High	A80
Q8_0Best for your GPU	8	25.7 GB	Very High	A82
F16	16	49.2 GB	Maximum	F0

Get started

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

Run

ollama run lfm2

Your hardware

More models your Mac mini M4 64GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	13.1 tok/s
Qwen 3.5 27B	27B	S	9.3 tok/s
Qwen 3.6 27B	27B	S	7.1 tok/s
Qwen 3.6 35B A3B	35B	S	12.1 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	13.5 tok/s

Frequently asked questions

Can Mac mini M4 64GB run LFM2 24B?

Yes, Mac mini M4 64GB can run LFM2 24B with a A grade (Runs well). Expected decode speed: 5.9 tok/s.

How much VRAM does LFM2 24B need?

LFM2 24B (24B parameters) requires approximately 24.9 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 Mac mini M4 64GB?

On Mac mini M4 64GB, LFM2 24B achieves approximately 5.9 tokens per second decode speed with a time-to-first-token of 32804ms using Q4_K_M quantization.

Can Mac mini M4 64GB run LFM2 24B for coding?

For coding workloads, LFM2 24B on Mac mini M4 64GB receives a A grade with 5.9 tok/s and 131K context.

What context window can LFM2 24B use on Mac mini M4 64GB?

On Mac mini M4 64GB, 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.

What should I upgrade first if LFM2 24B feels slow on Mac mini M4 64GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Is unified memory on Mac mini M4 64GB as fast as VRAM for LFM2 24B?

Not always. Mac mini M4 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 Mac mini M4 64GB See all hardware for LFM2 24B

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