Alibaba has announced plans to develop an artificial intelligence model containing between 5 trillion and 10 trillion parameters. Alongside this roadmap, the company also unveiled a new custom chip designed to support its infrastructure needs.
Huawei has paused its international expansion of artificial intelligence chips because domestic demand within China is exceeding the available supply. This strategic shift means that competitors AMD and Nvidia no longer face immediate pressure from Huawei's global market entry.
A proposal on Hugging Face discusses two interconnected problems in AI: the reliance on training separate large models and the ecosystem's dependence on general-purpose GPUs.
The llama.cpp project released build b11059, which introduces significant updates to the Metal backend for Apple Silicon hardware. The primary change is the addition of F16 input support to the Fast Walsh-Hadamard Transform (FWHT) kernel, allowing it to read F16 sources directly without requiring a converted copy.
The llama.cpp project has increased the hoisted row-id limit for the Vulkan mul_mat_id operation from 256 to 1024 experts. This change addresses performance bottlenecks in models with large expert counts, such as Qwen3.8-Flash-Next, which previously ran on a slower code path due to shared array size constraints.
The llama.cpp project released version b10997, which includes a change to the Mixture of Experts (MoE) ncols_opt tile heuristic to support the RDNA3.5 architecture.
The llama.cpp b10994 release includes a fix for the Metal backend that resolves NaN outputs in the `mul_mm_id` operation when activation values exceed the f16 range. This defect previously caused models like Mistral Small 4 to produce entirely NaN vocabularies during prefill steps of 32 tokens or more on Apple Silicon devices.
Researchers present ShareMMU, an IOMMU design that allows multiple untrusted accelerators to securely reuse pre-translated addresses within a shared virtual address space. This approach mitigates side-channel risks associated with large Shared Translation Lookaside Buffers (TLBs) while maintaining high performance.
The author has built MOLT, a Windows-first runtime for thermally aware QLoRA fine-tuning on consumer NVIDIA GPUs, and benchmarked it against Unsloth using one million Qwen 1.5B training targets on an RTX 4060 Laptop GPU.
A user has developed a custom implementation that allows the DeepSeek V4.1 model to run on NVIDIA A100 GPUs with performance exceeding the official API.
A proposal on the Hugging Face forums outlines a common GPU execution and memory-management layer designed to decouple application code from specific hardware vendors like NVIDIA, AMD, and Intel. The goal is to allow users to specify a memory budget rather than manually configuring backend-specific flags, device maps, or offloading strategies.
Researchers propose Onyx, a cost-efficient approach to disk-oblivious approximate nearest neighbor (ANN) search that balances bandwidth and access count by inverting the design of state-of-the-art ORAM-ANN systems.
Researchers from NVIDIA have introduced GPIR, a system that accelerates Private Information Retrieval (PIR) on GPUs by addressing the high server-side computation and memory traffic inherent in lattice-based protocols. The system employs a stage-aware hybrid execution model that dynamically switches between operation-level and stage-level kernels to maximize on-chip data reuse.
A new study presents a unified system-level characterization of secure multiparty computation (MPC) and fully homomorphic encryption (FHE) for privacy-preserving machine learning inference. The work evaluates two MPC variants—arithmetic/binary sharing conversion and function secret sharing—alongside FHE across multiple CNN and Transformer models.
The kernels team at Together AI has optimized the ThunderKittens library to leverage new features in the NVIDIA Vera Rubin NVL72 platform, specifically targeting improved performance for NVFP4 and FP8 matrix multiplications.
Together AI announces the public preview of preemptible compute for Together GPU Clusters on Kubernetes, offering a lower-cost option for interruption-tolerant workloads. Preemptible nodes utilize unused NVIDIA accelerated capacity and are billed at a flat 50% of the on-demand rate.
The llama.cpp project released version b10891, which addresses a critical stability issue on PowerVR GPUs. The update modifies the Vulkan backend to fall back to shared-memory reduction for dequantized matrix-vector multiplication (dmmv) operations.
Beatriz Yankelevich of MIT’s Engineering Quantum Systems Group used GPT‑5.6 Sol, integrated with Codex, to autonomously run and refine routine measurements on superconducting qubits. This integration allows the AI agent to operate laboratory software, analyze results, and decide on subsequent steps without constant human supervision.
Anthropic has secured $517 billion in compute capacity leases over the past 11 months, amounting to 14.8GW, primarily with Google and AWS. This expansion includes large deals with cloud providers like Akamai and Fluidstack and a $45bn agreement with Nscale.
The llama.cpp project released version b10839, which resolves hard crashes in the Vulkan backend caused by misaligned offsets during tensor row retrieval operations. Previously, models like Qwen3-TTS and Qwen3-VL would fail when using `ggml_view` with non-zero view offsets because the shader asserted on alignment violations relative to `minStorageBufferOffsetAlignment`. This update implements native support for aligned offsets in both quantized and non-quantized paths, eliminating the need for CPU fallbacks.