Can I Run LFM2.5 on 32 GB system RAM?

Written by Jakub Rusinowski · Last updated September 11, 2026

Yes — comfortably

Yes, comfortably — LFM2.5-8B-A1B at Q8_0 needs about 10.8 GB of the 25.6 GB usable on 32 GB system RAM, leaving ~14.8 GB spare and running at ~28.8 tok/s (estimated), with room for about 65,536 tokens of context.

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~28.8 tok/s

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32 GB system RAM — what it gives a model

Usable memory for models	25.6 GB
Memory bandwidth	90 GB/s

LFM2.5 on 32 GB system RAM: memory by quantization

Quant	Memory needed	Fits 25.6 GB?	Max context	Est. speed	Download
F16	18.6 GB	✓ Yes	32K	~18 tok/s	16.6 GB
Q8_0	10.8 GB	✓ Yes	64K	~28.8 tok/s	8.8 GB
Q6_K	8.8 GB	✓ Yes	64K	~34.1 tok/s	6.8 GB
Q5_K_M	7.9 GB	✓ Yes	128K	~37.3 tok/s	5.9 GB
Q4_K_M	7 GB	✓ Yes	128K	~40.9 tok/s	5 GB
Q3_K_M	5.5 GB	✓ Yes	128K	~48.8 tok/s	3.5 GB
Q2_K	4.7 GB	✓ Yes	128K	~54.6 tok/s	2.7 GB

What to watch out for

This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
These figures assume CPU-only inference. Any discrete GPU, even an 8 GB one, will be several times faster for models that fit in its VRAM.

Recommended setup

llama.cpp (CPU build) or Ollama — both run without a GPU

How these numbers are calculated

Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
25.6 GB of the 32 GB is treated as usable for model weights (80% — the rest is the OS and running applications).
DDR5-5600 dual channel at 89.6 GB/s peak. CPU decode is assumed to sustain 35% of that peak, because CPU inference is not purely bandwidth-bound — it also spends real time in compute and thread synchronisation. This figure is an assumption, not a fitted constant: no CPU measurement is in the calibration set.
CPU-only inference: no GPU is assumed. A GPU of any size will beat these figures substantially.
Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
KV cache is inferred from the parameter count because this model does not publish its full attention configuration.

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