The latest AI news, in focus.

The data center boom has an upside: innovation for the US grid

As the AI boom stresses the existing power grid, all sort of new opportunities for grid modernization are also emerging as a result, industry insiders say.

Memory-Enforced GPU Sharing in Kubernetes with NVIDIA CUDA 13.4

Many AI workloads need only a fraction of a GPU. Giving each one an entire device can leave capacity idle; sharing GPUs lets platform teams serve more workloads...

Analog Superposition Increases Effective PCM Storage Density (IBM Research Europe)

Researchers at IBM Research Europe published “Superposed analog memory for vector storage in neural networks.” Summary: Researchers demonstrated an analog phase-change memory architecture that stores multiple AI vectors in superposition, increasing effective storage density with a tunable accuracy tradeoff. Tests on prototype PCM hardware showed inference accuracy close to full-precision software. Find the technical paper here.... » read more The post Analog Superposition Increases Effective PCM Storage Density (IBM Research Europe) appeared first on Semiconductor Engineering.

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llama : build the k-pool sparse selection independently of the kq_mask shape (#31243) * llama : build the k-pool sparse selection independently of the kq_mask shape qwen4exp (QSA) and glm5-next pick cache cells by index and no longer read the kq_mask values, but they still shaped the selection like the dense kq_mask. Build the selection as [n_kv, n_tokens] over the cache cells instead, and split it into the streams of the K view right before the attention. This lets the dense mask change its layout (e.g. per-sequence row lists) without touching the sparse layers. * improve comment Co-authored-by: Georgi Gerganov * llama : drop the unused build_fused_top_k_mask args, review nits --------- Co-authored-by: Georgi Gerganov **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silico

b11935

rpc : catch std::length_error in recv_msg (#31204) **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11935/llama-b11935-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b11935/llama-b11935-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11935/llama-b11935-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11935/llama-b11935-bin-ubuntu-x64.tar.gz) - [Ubuntu arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11935/llama-b1

Announcing SOCI-indexed images for faster SageMaker AI jobs

Seekable OCI (SOCI) indexing for Amazon SageMaker AI training and processing jobs takes full container image download off the critical path. SageMaker AI lazy-loads your image to start containers in seconds instead of minutes, with no changes to your job definition. See the benchmarks and how to adopt it.

NVIDIA RTX Spark Powers Neural Rendering and AI Characters in Games

NVIDIA RTX Spark is a new Windows PC platform for AI development, creation, and gaming in slim laptops and compact desktops. Powered by an NVIDIA Blackwell RTX...

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tests : speed up tensor data generation and model saving (#31145) * tests : speed up tensor data generation and model saving Refactor save_models to create only the model, not a llama_context, which is not needed for saving. Replace the slow std::normal_distribution draw with a splitmix64 + box-muller normal generator. Assisted-by: dsh:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * tests : use a shared pool of normal draws for tensor data Generate a pool of normal draws once with std::mt19937 and std::normal_distribution, then have every tensor copy a cyclic slice of it instead of running a per-tensor box-muller. The name-keyed slice selection keeps the draw deterministic. Assisted-by: dsh:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studi

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vulkan: fix CI llvmpipe by allowing dim 3 broadcast in MUL_MAT (#31239) * vulkan: allow dim 3 broadcast in MUL_MAT supports_op rejected every MUL_MAT whose src0 and src1 differ in ne3, although mul_mm, mul_mmq and mul_mat_vec already broadcast src0 over dim 3 through broadcast3. Such a matmul then ran on the CPU with a copy of the weight, and the split layout changed with the number of streams in the ubatch. Keep the strict check for MUL_MAT_ID only, and route the broadcast cases away from mul_mat_vec_nc, the one path that requires equal ne3. * tests : harden MUL_MAT with permuted dim 3 broadcast cases Harden the MUL_MAT coverage with a weight broadcast over dim 3 against a permuted per-stream activation, the shape of an MLA output projection with a non unified KV cache, for a single token

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mtmd-cli: Reduced scope of batch pointer (#31155) (#31165) * mtmd-cli: Reduced scope of batch pointer (#31155) * mtmd-cli: Remove CLI regression test **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11932/llama-b11932-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b11932/llama-b11932-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11932/llama-b11932-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11932/llama-b11932-bin-ubuntu-x64.tar

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server: add router device memory margin parameter for dynamic unloading (#21231) * server: add --models-memory-max parameter to allow dynamically unloading models when they exceed a memory size threshold estimate with to-be-loaded model size included use no_alloc to get memory requirements for model load only set model memory_mb if not previously calculated use memory margin instead of total size limit, apply to each device separately add server memory debug logging move llama_context_device_memory function to llama-ext.h fix model count exceeded check improve memory_per_device map naming improve variable naming, fix style also strip models memory margin from child processes cont : clean-up replace device memory map with buft memory map. Use llama_get_memory_breakdown extract duplicated ch

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common : --model or -hf is required (#31242) Assisted-by: Codex **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11929/llama-b11929-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b11929/llama-b11929-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11929/llama-b11929-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11929/llama-b11929-bin-ubuntu-x64.tar.gz) - [Ubuntu arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b1

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model: Add MiniCPM-OCR-Jev (#30980) * model : add MiniCPM-OCR-Jev support The model is a Qwen3.5-VL trunk with a value head read at the last token: it scores an OCR text against an image rendered from it, it does not generate. Add the openbmb-minicpm-ocr-jev decision type, served by POST /v1/systemone. The class scores are the pooled output of the sequence, read with last pooling, so the conversion turns score.weight into cls.output and sets embedding_length_out. The checkpoint has no LM head, so text generation is rejected instead of using the output weights duplicated from the token embeddings. The task is fixed, so its prompt is entirely in the systemone template, the same way h2o-lightning keeps its default there. The processor of the checkpoint allows up to 16384 merged tokens per ima

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metal : add GGML_OP_TOP_K_MASK (#31230) * metal : add GGML_OP_TOP_K_MASK Port the fused top-k mask from the CUDA backend: a grid-stride kernel fills the output with -inf, then one threadgroup per row copies the mask values of the kept cells over it. Function constants drop the score, blocks and extra paths that a given graph does not use. Assisted-by: dsh:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB) * metal : clean up top-k mask kernel formatting Assisted-by: dsh:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB) **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11927/llama-b11927-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](

Nvidia backs Indian startup helping companies run AI models with fewer GPUs

Simplismart says its annualized revenue run rate has grown 14-fold in six months, with the U.S. accounting for more than half its revenue.

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glm5-next : QSA without kq_mask (+ DSA) (#31009) * glm5-next : QSA without kq_mask * DSA: dsa top-k mask change tested with layers 0,3 of DeepSeek-V3.2 **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11926/llama-b11926-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b11926/llama-b11926-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11926/llama-b11926-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11926/llama-b11926-bin-ubuntu-x64.t

Rokid has unveiled three new AI-powered wearable devices, expanding its display and interaction solutions.

On October 27, Rokid launched the AI glasses Rokid Glasses 2, the full-color AI glasses Rokid Color, and the interactive ring Rokid Ring.

The HKEX AI Index is here, and Southern Fund is the first to take the stage: How significant is this move?

Index implementation plus ETF authorization have provided Hong Kong‑listed AI and semiconductor stocks with new allocation tools and a fresh source of capital.

How can developers capitalize on the foundational benefits of the AI era? The CEIC × 1024 Shenzhen event unveils the new infrastructure for AI and HarmonyOS development.

During the 2026 Changsha · 1024 Programmers’ Day event, the CEIC Consumer Electronics Innovation Conference and CSDN joined forces in Shenzhen to present a cutting-edge tech extravaganza for developers. As Yang Tao, Executive Deputy Director of the CEIC Organizing Committee, noted in an interview: “Ultimately, every experience innovation in the consumer electronics field is brought to life by the code written by our programmers.” At this pivotal moment in technological advancement, the evolution has moved from plain hand-written code to leveraging intelligent agents (Agents) and powerful on-device APs...

Data center expansion has sparked local opposition across the United States, making it a key issue in the 2026 midterm elections.

According to a report by The Verge on October 27, 2026, communities across the United States have mounted widespread opposition to the large-scale construction of data centers and the soaring costs of associated infrastructure, with this issue deeply entangled in numerous congressional and gubernatorial races. Tech capital is accelerating its entry into the fray: the super PAC “Leading the Future,” backed by Marc Andreessen and Greg Brockman, has already spent $45 million within a year, while “Public First Action,” supported by Anthropic, has also joined the effort.

Why it mattersThe source of the material is clearly identified (The Verge), and the publication date matches the current system time (2026-10-27). The content focuses on verifiable public policies and electoral developments, including specific entities, monetary amounts, timelines, and organizational relationships, thereby meeting the requirements for timeliness and factual grounding.