Can LFM2 24B run on MacBook Pro M4 Max 36GB?

YES — Tight Fit

A83Great

○Estimated from fit model

LFM2 24B needs ~21.9 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q4_K_M quantization, expect ~29 tok/s.

Runtime: llama.cppCapacity: 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) — 21.9 GB, 29.3 tok/s, Tight fit

21.9 GB required25.9 GB available

85% VRAM used

Fit status

Tight fit

Decode

29.3 tok/s

TTFT

6607 ms

Safe context

43K

Memory

21.9 GB / 25.9 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.9 GB

See how fast it feels

See how fast it feelsLFM2 24B on MacBook Pro M4 Max 36GB
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: 29.3 tok/s decode · 6.6s TTFT (warm) · 73 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	S	Runs well	29.3 tok/s	3604 ms	43K
Coding	A	Tight fit	29.3 tok/s	6607 ms	43K
Agentic Coding	A	Tight fit	29.3 tok/s	9610 ms	43K
Reasoning	A	Tight fit	29.3 tok/s	7808 ms	43K
RAG	A	Tight fit	29.3 tok/s	12012 ms	43K

Quantization options

How LFM2 24B (24B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A81
Q3_K_S	3	11.8 GB	Low	A82
NVFP4	4	13.4 GB	Medium	A83
Q4_K_M	4	14.6 GB	Medium	A83
Q5_K_M	5	17.3 GB	High	A83
Q6_KBest for your GPU	6	19.7 GB	High	A82
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

Your hardware

More models your MacBook Pro M4 Max 36GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	39.1 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	28.8 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	21.9 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	A	28.5 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	40.4 tok/s

Frequently asked questions

See all results for MacBook Pro M4 Max 36GB See all hardware for LFM2 24B

URL: https://willitrunai.com/can-run/lfm2-24b-on-m4-max-36gb

⇱ LFM2 24B on MacBook Pro M4 Max 36GB? TIGHT FIT