Can LFM2 24B run on MacBook Pro M4 Pro 24GB?

BARELY — Tight on Memory

B66Good

Estimated — low-sample bucket· few comparable runs

LFM2 24B needs ~20.6 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 20.6 GB, 17.8 tok/s, Very compromised (needs ~2.3 GB host RAM)

20.6 GB required17.3 GB available

119% VRAM needed

3.3 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.3 GB host RAM)

Decode

17.8 tok/s

TTFT

10872 ms

Safe context

Memory

20.6 GB / 17.3 GB

Offload

20%

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsLFM2 24B on MacBook Pro M4 Pro 24GB
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: 17.8 tok/s decode · 10.9s TTFT (warm) · 45 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Very compromised (needs ~1.6 GB host RAM)	19.3 tok/s	5478 ms	4K
Coding	B	Very compromised (needs ~2.3 GB host RAM)	17.8 tok/s	10872 ms	4K
Agentic Coding	F	Too heavy	15.5 tok/s	18126 ms	4K
Reasoning	B	Very compromised (needs ~2.3 GB host RAM)	17.8 tok/s	12849 ms	4K
RAG	F	Too heavy	15.5 tok/s	22658 ms	4K

Quantization options

How LFM2 24B (24B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A84
Q3_K_SBest for your GPU	3	11.8 GB	Low	A84
NVFP4	4	13.4 GB	Medium	F0
Q4_K_M	4	14.6 GB	Medium	F0
Q5_K_M	5	17.3 GB	High	F0
Q6_K	6	19.7 GB	High	F0
Q8_0	8	25.7 GB	Very High	F0
F16	16	49.2 GB	Maximum	F0

Get started

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

Run

ollama run lfm2

Opções de upgrade

Hardware que roda bem LFM2 24B

MacBook Pro M4 32GBOpção econômica

32 GB Unified (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.9.5 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Adds memory headroom for longer context windows and future model growth.

~$799 MSRP

Mac mini M4 32GBMelhor custo-benefício

32 GB Unified (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.9.5 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Adds memory headroom for longer context windows and future model growth.

~$1,099 MSRP

Mac mini M4 64GBUpgrade Apple

64 GB Unified (+40)

Removes host-memory offload, which is usually the single biggest latency and throughput win.9.5 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Adds memory headroom for longer context windows and future model growth.

~$1,099 MSRP

RTX 5090 32GBMaior salto

32 GB VRAM (+8)1792 GB/s (+1519)

Removes host-memory offload, which is usually the single biggest latency and throughput win.70.5 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 296%.

~$1,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Pro 24GB run LFM2 24B?

Yes, MacBook Pro M4 Pro 24GB can run LFM2 24B with a B grade (Very compromised (needs ~2.3 GB host RAM)). Expected decode speed: 17.8 tok/s.

How much VRAM does LFM2 24B need?

LFM2 24B (24B parameters) requires approximately 20.6 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 M4 Pro 24GB?

On MacBook Pro M4 Pro 24GB, LFM2 24B achieves approximately 17.8 tokens per second decode speed with a time-to-first-token of 10872ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 24GB run LFM2 24B for coding?

For coding workloads, LFM2 24B on MacBook Pro M4 Pro 24GB receives a B grade with 17.8 tok/s and 4K context.

What context window can LFM2 24B use on MacBook Pro M4 Pro 24GB?

On MacBook Pro M4 Pro 24GB, LFM2 24B can safely use up to 4K 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 MacBook Pro M4 Pro 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Is unified memory on MacBook Pro M4 Pro 24GB as fast as VRAM for LFM2 24B?

Not always. MacBook Pro M4 Pro 24GB 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 24GB See all hardware for LFM2 24B

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