Meta
10 updates on Meta.
Meta AI builds MobileMoE, 5.3B parameters with 0.9B active per token
Meta AI trained three on-device mixture-of-experts models that store 1.3B to 5.3B parameters and run 272M to 922M of them per token.
Tempo compresses hour-long video with a 2B vision model so a 4B LLM can answer
Meta AI and KAUST report 52.7 on LVBench for a 6B system in which a 2B vision-language model squeezes long video down to about 3 tokens per frame.
Meta's MobileLLM-Flash goes shallow and wide for 1.8 times faster prefill
Meta designed 350M, 650M and 1.4B models by measuring latency on a Galaxy S25, reversing the deep-and-thin rule of the first MobileLLM.
Meta's MobileLLM-Pro runs a 128k context from a 590 MB 4-bit build
Meta Reality Labs released a 1.08B on-device model with a 128k context window, measured at 33.6 tok/s decode on a Galaxy S25 CPU.
ExecuTorch 1.0 reaches general availability for on-device PyTorch models
The PyTorch edge runtime promotes Core ML, Qualcomm Hexagon, Arm Ethos-U, Vulkan and XNNPACK backends to production status.
Meta trains 140M to 950M reasoning models on 4.2T tokens
MobileLLM-R1 spans 140M to 950M parameters, trained on 4.2T tokens, and Meta scores the 950M model at 74.0 on MATH500.
Meta's ParetoQ compares five bit widths and puts the accuracy cliff at 1 bit
Meta trained the same models at 1, 1.58, 2, 3 and 4 bits under one recipe and reports 1.58-bit to 3-bit beating 4-bit at matched memory.
Meta ships Llama Stack with Swift and Kotlin clients for on-device inference
Meta standardised its Llama APIs so mobile apps can swap a cloud endpoint for an on-device ExecuTorch runtime without changing the calling code.
Meta releases Llama 3.2 1B and 3B for phones and edge devices
The two lightweight models carry a 128K context window, were pruned and distilled from Llama 3.1, and shipped with day-one Qualcomm and MediaTek support.
Meta's MobileLLM trades width for depth and gains 2.7 and 4.3 points
Meta Reality Labs built 125M and 350M models around deep and thin layers and profiled them on an iPhone 13 through ExecuTorch.