Qualcomm
19 updates on Qualcomm.
llama.cpp Hexagon NPU backend tested on a Snapdragon 8 Gen 3 phone
A user report puts Gemma 3 4B at 12.5 tokens per second of generation on a OnePlus 12 using llama.cpp’s Hexagon NPU backend, at about CPU speed but without the heat.
FBLayout fine-tunes transformers on phone GPUs 2.2 to 5.7 times faster
A MobiSys 2026 paper fine-tunes seven transformer models on phone GPUs 2.2 to 5.7 times faster than MNN, TFLite and TVM, with 4.2 times fewer cache misses.
Qualcomm CEO says agents will become the new app, cites more than 40 device designs
Cristiano Amon tells CNBC that Qualcomm has over 40 designs for AI wearables and that smart glasses shipments could reach hundreds of millions a year.
MLPerf Mobile v6.0 adds Llama tests in 1B, 3B and 8B sizes
MLCommons added Llama tests in 1B, 3B and 8B sizes to its mobile benchmark app, reporting token throughput next to the existing vision and image tests.
llada.cpp runs a diffusion LLM on a Snapdragon NPU up to 42 times faster
Tsinghua and Beihang researchers report LLaDA-8B generating 128 tokens 17 to 42 times faster on a Hexagon NPU than on the phone CPU.
Quant.npu runs 4-bit LLMs up to 15.1 percent faster on a Qualcomm NPU
Eight authors report a fully static integer quantisation method that runs 4-bit models up to 15.1 percent faster on a Qualcomm SM8650 NPU.
ShadowNPU scores attention on the Snapdragon NPU and reports 4.5 times faster inference
Peking University and BUPT move attention token scoring onto a Snapdragon NPU and report up to 4.5 times faster inference using a single CPU core.
Google's ML Drift runs Llama 3.1 8B on a phone GPU at 12.7 tokens per second
The paper measures 37.1 decode tokens per second for Gemma2 2B and 12.7 for Llama 3.1 8B on the Adreno 750 GPU of a Samsung S24.
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.
ElastiLM resizes a shared phone LLM per request and switches in 0.31 seconds
Peking University and BUPT built an on-device LLM service that fits model and prompt to each request, switching sub-models in 0.31 seconds.
llm.npu prefills 1,106 tok/s for Qwen1.5-1.8B on a Snapdragon 8 Gen 3
Peking University and BUPT move prompt processing onto the Hexagon NPU and report up to 43.6x faster prefill and up to 59.5x lower energy use.
BUPT measures 22 LLMs on four Android phones at about 200 ms per token
A BUPT team ran 22 models from 0.5B to 7B on four Android phones with llama.cpp and found 7B models take about 4 GB and 200 ms per token.