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// 49d agoBENCHMARK RESULT
Liquid AI’s LFM2.5 Q8 flies on aging CPUs
The post highlights a benchmark-style result for Liquid AI’s LFM2.5-1.2B-Instruct in a Q8 quantized local run, with the screenshot claiming 109.9 tokens per second on a six-year-old PC. That lines up with Liquid AI’s broader positioning for LFM2.5 as a compact, on-device model family built for fast inference and low memory use, especially in CPU and edge deployments.
// ANALYSIS
Hot take: this is less about raw model intelligence and more about how aggressively Liquid AI has optimized the stack for local inference, and that is the real story.
- –The reported speed is the headline: 109.9 t/s on older desktop hardware is strong enough to make the model interesting for local-first users.
- –The result fits Liquid AI’s published pitch for LFM2.5 as a fast, sub-2B on-device model family with quantized deployment options.
- –Because this is a Reddit screenshot rather than a formal benchmark report, treat the exact number as anecdotal unless replicated on the same hardware/config.
- –For enthusiasts, the practical signal is that Q8 quantization can preserve enough quality while staying very fast on consumer CPUs.
// TAGS
lfm2.5liquid-ailocal-llmquantizationq8cpu-inferenceon-device-aillama-cpp
DISCOVERED
49d ago
2026-04-09
PUBLISHED
49d ago
2026-04-09
RELEVANCE
8/ 10
AUTHOR
reg-kdeneonuser