Quantisation
38 updates on Quantisation.
ROMA keeps a 4-bit 3B model in on-chip ROM and reports 31,800 tok/s in synthesis
A synthesised 7 nm accelerator holds a 4-bit 3B Llama in read-only memory and the LoRA adapter in SRAM, with no chip and no FPGA prototype built.
Gemma 3 adds a 1B size that fits in 0.5 GB as an int4 checkpoint
Google released Gemma 3 at 1B, 4B, 12B and 27B with quantisation-aware int4 checkpoints of 0.5 GB and 2.6 GB for the two smallest sizes.
Raspberry Pi 5 beats human reading speed only up to Phi 3.5 mini
Helsinki and EURECOM researchers measured 11 models on a Raspberry Pi 5 and a Jetson Orin Nano, where memory and battery gave out before speed did.
MobiLLM fine-tunes OPT-1.3B in 4.5 GB on device by moving backpropagation to a server
Researchers fine-tuned OPT-1.3B on a Jetson Xavier NX in 4.5 GB by keeping the frozen model on the device and the trainable side network on a server.
GenAI at the edge survey lists 12 accelerators, 8 of them only simulated
A Johns Hopkins and Duke survey of generative AI on edge devices puts peak accelerator efficiency at 74.34 TOPS/W, with 8 of 12 designs only simulated.
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.
Alibaba MNN runs 4-bit LLMs on phone CPUs and GPUs, with a multimodal Android app
MNN-LLM converts PyTorch checkpoints into a 4-bit MNN format for phones, and Alibaba reports prefill 8.6 times faster than llama.cpp on an Android CPU.
Amazon survey puts some small models at 10 to 100 times their parameter count
A survey of about 160 papers finds 1B to 8B models matching much larger ones on single benchmarks, and shows where models below 1B fall apart.
BlueLM-V-3B runs a multimodal model on a Dimensity 9300 NPU in 2.2 GB
CUHK and vivo AI Lab report 24.4 tok/s and 2.2 GB peak memory for a 3B vision-language model on a MediaTek Dimensity 9300.
PalmBench finds iPhones running local LLMs about three times faster than Android phones
A benchmark of quantised LLMs on eight phones and boards reports throughput, memory, power and heat, with iPhones ahead and 4-bit drawing more power than 3-bit.
torchchat runs Llama 3 8B on a Galaxy S23 and iPhone at more than 8 tok/s
PyTorch's demonstration codebase exports Llama models to ExecuTorch and ships iOS and Android sample apps for running them on a phone.
Spectra ships ternary 3.9B models that match 4-bit quantisation at half the bits
Nolano AI trained 54 models from 99M to 3.9B parameters on the same 300B tokens in ternary, quantised and half-precision form to compare them by bit size.