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LFM2 24B-A2B Instruct

LFM2 24B-A2B Instruct loads in 14 GB at Q4_K_M and decodes at the speed of its 2B active path, comfortably from 32 GB of memory — Macs included, starting at the MacBook Air M5 24GB. Below is the memory math we ran for it, the cheapest card that fits, and the fastest machine we track.

PARAMETERS
24B (2B active)
FORMAT
Q4_K_M
MIN MEMORY
24 GB
BEST FOR
Local AI agents, privacy-first tool calling, MCP workflows

How much memory does LFM2 24B-A2B Instruct need?

LFM2 24B-A2B Instruct is a 24B mixture-of-experts model (2B active per token) built for local ai agents, privacy-first tool calling, mcp workflows. The weights alone take 14 GB at Q4_K_M; add context and the OS, and it belongs in the 32 GB memory class. Every number below comes from the ModelFit engine — speeds are estimates, labeled est., because we do not benchmark hardware ourselves.

Weights (Q4_K_M)
14 GB
+ KV cache (16k)
~4.0 GB
Total at 16k
~18.0 GB
Comfortable from
32 GB

KV = fp16 estimate (q8_0 cache roughly halves it). "Comfortable" = weights + KV within the engine's tiered budget (~70-85% of memory).

Which machines run LFM2 24B-A2B Instruct?

Cheapest GPU
~84 tok/s est. — ~$550 used
Fastest
~164 tok/s est.
Runs on a Mac?
~29 tok/s est.

Verdicts come from the same engine that powers our fit checker — memory capacity first, bandwidth for speed. How we compute these numbers · Hugging Face model card

Go deeper

More LFM2 models

Frequently asked questions

How much memory does LFM2 24B-A2B Instruct need?

14 GB for the Q4_K_M weights, plus ~4.0 GB of KV-cache at 16k context — about 18.0 GB total. Comfortable from 32 GB of VRAM or unified memory.

Does LFM2 24B-A2B Instruct run on a Mac?

Yes — from the MacBook Air M5 24GB (~29 tok/s est.). Unified memory means the RAM budget is the only limit.

What is the cheapest GPU for LFM2 24B-A2B Instruct?

The AMD Radeon RX 7900 XT is the cheapest tracked card that runs LFM2 24B-A2B Instruct comfortably — ~84 tok/s est. at ~$550 used.

Which LFM2 24B-A2B Instruct quant should I pick?

The tracked Q4_K_M build at 14 GB is the one we recommend for most machines. Every published build, ranked by real GGUF file size rather than a formula, is on the quant comparison page linked below.

Cite this page

ModelFit: LFM2 24B-A2B Instruct — specs, memory math and hardware verdicts.
https://modelfit.io/models/lfm2-24b-a2b/ (dataset updated 2026-09-03, CC BY 4.0).