https://llmobile.kavents.com/tags/mediatek/A concise news ticker covering AI on mobile devices, local models, apps and hardware.en-GBhttps://llmobile.kavents.com/ticker/mediatek-dimensity-9600-pro/https://llmobile.kavents.com/ticker/mediatek-dimensity-9600-pro/Tue, 15 Sep 2026 00:00:00 +0200MediaTek announced the Dimensity 9600 Pro, a flagship smartphone chip built on a 2nm process. According to the company, its NPU 1090 supports on-device applications with models of up to 30B parameters. MediaTek reports 51% higher LLM prefill performance, the phase where the model reads the prompt, and 55% higher token generation per watt, measured on demo devices in its own labs. The chip pairs the NPU 1090 with a second-generation Super Efficient NPU, which MediaTek says cuts power consumption for always-on AI by 40%. The platform supports LPDDR6 memory and UFS 5.0 storage. In September 2025, MediaTek announced that it had completed the tape-out, the final design handoff to the fab, of a flagship chip on TSMC’s N2P 2nm process, with volume production expected in late 2026. The CPU uses a 2+3+3 layout of eight big cores, with two C2-Ultra cores at up to 4.55 GHz. MediaTek states up to 17% higher single-core and up to 15% higher multi-core performance over the previous generation, and up to 61% lower multi-core power consumption. The first smartphones with the Dimensity 9600 Pro and the related Dimensity 9600M are expected to launch this quarter, according to MediaTek. Source: https://www.mediatek.com/press-room/mediatek-dimensity-9600-pro-sets-new-standard-for-flagship-smartphone-chips Read the article: https://llmobile.kavents.com/ticker/mediatek-dimensity-9600-pro/ChipsAndroidSmartphoneMediaTekhttps://llmobile.kavents.com/ticker/fblayout/https://llmobile.kavents.com/ticker/fblayout/Tue, 07 Jul 2026 12:18:25 +0200Researchers at the University of Macau, the University of Georgia and the Hong Kong University of Science and Technology published FBLayout on July 7, 2026, a system that fine-tunes transformer models on a phone’s own GPU, accepted at ACM MobiSys 2026. On a OnePlus Ace 5 Pro with a Snapdragon 8 Elite and its Adreno 830 GPU, the authors measure training on Llama 3.2 1B, Qwen2.5 1.5B and Gemma 2 2B at 3.9 to 4.1 times the speed of MNN, 4.3 to 4.9 times TFLite and 5.4 to 5.7 times TVM. Over the same three baselines they count 3.5 times fewer global memory reads and 4.2 times fewer cache misses. The authors time inference on the Snapdragon 8 Elite at roughly 6.5 times faster on the GPU than on the CPU, while fine-tuning models of 0.5B to 2B parameters on the GPU comes out level with the CPU or up to 43 percent worse. Their explanation is memory layout, meaning the order in which a tensor’s numbers physically sit in memory. Mobile GPUs hold tensors in a 2D texture grid whose cache rewards reading neighbouring cells, and training reuses forward-pass activations and weights in the backward pass, which sums along a different dimension and so walks the same tensor in strides instead of in rows. Each stride that misses the cache becomes a fetch from DRAM at under 100 GB/s of mobile memory bandwidth, and the GPU stalls waiting for it. Existing frameworks take one of two ways out, and the paper prices both. MNN reuses the forward layout in the backward pass, which the authors put at up to a 5.3 times slowdown on key operations and at under 21 percent arithmetic-unit utilisation in a matrix-multiply benchmark. TFLite instead inserts explicit transpose and reshape operators, and the paper puts that route at 40.8 to 50.0 percent of total runtime across the three LLMs and at roughly a third of all memory allocations. FBLayout defines one tile-based layout, called R-Tile, that keeps both directions contiguous, then rewrites the remaining reshapes as coordinate arithmetic so no data moves, which they report covers over 86 percent of all layout transformations and over 95 percent of those in the LLMs. The evaluation covers seven models, adding BERT-Large, ViT-Large, Whisper-Large and Stable Diffusion 1.5 to the three LLMs, with LoRA adapters in the attention and MLP blocks for everything except ViT. Beyond the Snapdragon 8 Elite phone the authors test a OnePlus Ace 10 Pro with a Snapdragon 8 Gen 1 and an Adreno 730 and a OnePlus Ace 5 Ultra with a Dimensity 9400+ and an Arm Mali Immortalis-G925, and report gains on both. Preparing the Llama 3.2 1B training graph takes 6.1 seconds in their measurement, against 723 seconds in MNN, 862 in TFLite and over 4,800 in TVM, and a Monsoon power monitor puts energy use over five training rounds at 3.5 to 6.3 times below the baselines. FBLayout is built on top of Alibaba’s MNN as about 9.2K lines of C++ and OpenCL, extending it with automatic differentiation so the training graphs exist in the first place. The paper names no code release and points to no repository. It appeared at MobiSys 2026 in Cambridge in June 2026 and is on arXiv under a CC BY-NC-ND 4.0 license. Source: https://arxiv.org/abs/2607.21624 Read the article: https://llmobile.kavents.com/ticker/fblayout/ResearchMemoryQualcommMediaTekAndroidhttps://llmobile.kavents.com/ticker/bluelm-v/https://llmobile.kavents.com/ticker/bluelm-v/Sat, 16 Nov 2024 01:14:51 +0100Researchers at CUHK MMLab and vivo AI Lab published BlueLM-V-3B on November 16, 2024, a vision-language model designed together with the code that runs it on a phone. The authors deployed it on the NPU of a MediaTek Dimensity 9300 in a vivo X100 and report a peak memory requirement of 2.2 GB and a generation speed of 24.4 tok/s with the language model weights quantised to 4-bit. The model pairs a 2.7B in-house BlueLM language model with a 400M SigLIP vision encoder that takes 384 by 384 image tiles. The authors report an average score of 66.1 on OpenCompass, a suite of eight multimodal benchmarks, which they state is the highest among models of 4B parameters or fewer, ahead of the 8B models MiniCPM-V-2.6 and InternVL2-8B. Most of the speed comes from how the model decides what resolution to feed the encoder. Dynamic resolution schemes pick a grid of tiles to fit an image, and the authors show that existing choices enlarge pictures a lot, with LLaVA-NeXT resizing and padding a 394 by 390 image to 768 by 768, four times the area, and InternVL 1.5 stretching a 380 by 76 image to 1920 by 384, twenty-five times the area. More pixels mean more tiles and more image tokens, so BlueLM-V-3B uses what the authors call relaxed aspect ratio matching, which tolerates a slightly smaller effective resolution when it wastes less of the canvas. Both examples then land on a single 1:1 tile, and across the 665k-sample LLaVA training set the method picks a smaller grid than InternVL 1.5 in 523k cases. The rest is shaped around what an NPU is bad at. The authors encode image tiles in batches of four at a time, which they measured as the fastest setting, and run the SigLIP convolution layer on the CPU in parallel with the transformer blocks on the NPU to hide 200 ms of latency. A downsampler merges every 2 by 2 patch into one token, cutting 729 tokens per tile to 196, and the prefill runs in chunks of 128 tokens rather than all at once. Precision is mixed, with INT4 for the language model weights, INT8 for the vision encoder and projector weights and for the KV cache, and INT16 and FP16 activations, which the authors say keeps accuracy stable because activations are more sensitive to quantisation than weights. Against the 8B MiniCPM-V 2.5 running on the CPU of the same chip through llama.cpp, the authors report 2.53 s to load and encode a 768 by 1536 image and 2.7 s of prefill, versus 4.0 s and 13.9 s, at 24.4 tok/s against 4.9 tok/s. The paper describes no public release of weights or code, and the authors say future work will target a broader range of mobile devices. Source: https://arxiv.org/abs/2411.10640 Read the article: https://llmobile.kavents.com/ticker/bluelm-v/MediaTekNPUQuantisationResearchBenchmarkshttps://llmobile.kavents.com/ticker/llama-3-2-1b-3b/https://llmobile.kavents.com/ticker/llama-3-2-1b-3b/Wed, 25 Sep 2024 19:00:00 +0200Meta released Llama 3.2 on September 25, 2024, including text-only 1B and 3B models built for phones and edge hardware. Both carry a 128K token context window and are aimed at summarisation, instruction following and rewriting that run locally, with the data staying on the device. Meta built them by structured pruning from Llama 3.1 8B, then recovered quality through knowledge distillation using logits from the 8B and 70B models during pretraining. The company reports the 3B model ahead of Gemma 2 2.6B and Phi 3.5-mini on instruction following, summarisation, prompt rewriting and tool use, and puts the 1B model on a par with Gemma. In Meta’s own table the 3B model scores 77.4 on IFEval against 61.9 for Gemma 2 2B and 59.2 for Phi-3.5-mini, and 67.0 on BFCL V2 for tool use against 27.4 and 58.4. Table: Meta. The company measured the Gemma and Phi results itself. The models shipped with day-one support for Qualcomm and MediaTek silicon and run on Arm, which Meta says covers 99 percent of mobile devices. Weights are on llama.com and Hugging Face, with deployment paths through PyTorch ExecuTorch for devices and Ollama for single-node setups, and the company lists more than 25 partner platforms at launch. Source: https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/ Read the article: https://llmobile.kavents.com/ticker/llama-3-2-1b-3b/MetaLlamaOpen weightsQualcommMediaTek