LFM2 24B-A2B Instruct
LFM2 24B-A2B Instruct loads in 14 GB at Q4_K_M and is comfortable from 32 GB of memory, Macs included from the MacBook Air M5 24GB. Below: the full memory math, the cheapest card that runs it well, 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
Memory math
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).
Hardware snapshot
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.
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).