Reddit user reports LFM3-1.7B local inference results
A Reddit user testing Liquid's LFM3-1.7B in Q8_0 GGUF with llama.cpp and ROCm reported 4.1k tokens per second prefill and about 78 tokens per second decode on a Strix Halo iGPU. The user also reported roughly 7.1k prefill and 151 decode tokens per second when tensor-splitting the model across a Radeon RX 7900 XTX and an unspecified “XT” device. They described LFM3-1.7B as much more coherent at 32k–64k-token prompt sizes than a 1.7B model.
The post provides concrete community figures for local LFM3-1.7B inference and describes the model as a candidate for compression and summarization work, but it is a single user's report rather than a documented comparative benchmark.
Been testing Liquid's LFM3-1.7B (Q8_0 GGUF, llama.cpp, ROCm) as a candidate for compression and summarization work
4.1k t/s prefill and ~78 t/s decode on a Strix Halo iGPU
~7.1k / ~151 if you tensor-split it across a 7900 XTX + XT
its way more coherent at decent sized prompts (32k-64k) then 1.7b deserves to be imo
submitted by /u/mumblerit [link] [comments]
#Liquid AI#LFM3-1.7B#llama.cpp#ROCm#Strix Halo#Radeon RX 7900 XTX