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⑲6 records
On-Device & Local Inference
- Aug 31, 2026HUMAIN, Qualcomm — Unveiled the Horizon Ultra AI PC at LEAP 2026 (Riyadh) — based on the Snapdragon X2 Elite (18-core Oryon CPU, Adreno GPU, Hexagon NPU), it spreads AI work across the CPU, GPU and NPU. Enterprise purchases of the Windows version begin on Sep 20, 2026; a HUMAIN OS version is planned for 2027. The press release gives no NPU TOPS figure or price
- May 19, 2026Google (AI Edge) — LiteRT-LM generally available (GA) — added a native Swift API for iOS and a WebGPU-accelerated JavaScript API. Supports Android CPU, OpenCL GPU and NPU, iOS Metal and browser WebGPU. With Gemma 4: 52 tok/s decode on Android GPU, 56 tok/s on iOS GPU, 76 tok/s on the web on a MacBook Pro M4 Max, up to 2.2× with Multi-Token Prediction, 607MB of memory on iOS
⑩5 records
Safety, Alignment & Interpretability
- Sep 2, 2026Google — Announced the Fairwind program — provides Gemini 3.8 Flash Cyber and CodeMender, after screening, only to governments, national cyber authorities, critical infrastructure operators and large platforms. Requires, as conditions of participation, access limited to in-house security teams and multi-factor authentication. Said it has more than 650 partners and has given more than $100 million in cumulative cybersecurity support through Google.org, of which $36 million has been allocated to 35 cyber clinics
- Sep 1, 2026OpenAI — Published "Path to Astra: critical capabilities and frontier safeguards" — announced that Astra is the company's first model to meet the Critical capability threshold for cybersecurity under the Preparedness Framework. Defines Critical as the ability (1) to discover and develop working zero-day exploits against hardened real-world systems without human intervention, or (2) to plan and execute novel attack strategies against hardened targets end to end when given only high-level goals. In response, it deployed stronger refusal of harmful requests (91.5% by its own measurement, vs 59% for the previous version), system-level cyber-misuse classifiers, real-time monitoring and blocking, and initial access restrictions, and explained that it delayed some development stages to strengthen safeguards
⑪5 records
Model Architecture & Training Methods
- Sep 3, 2026Sergii Kozyrev, Davyd Maiboroda — Minima released — to test the practice, in early community 4-bit quantizations of the hybrid LLM Qwen3.8-27B (48 GDN layers, 16 attention layers), of leaving the GDN blocks — especially the decay and write-strength gates — at 8/16 bits for fear of accumulating recurrent error, applies NVFP4 W4A4 to all 496 linear layers. Reports that it matches BF16 within the range of seed noise (an average of -0.52 across 5 tasks) on 4K/32K perplexity, MMLU-Pro, GSM8K, AIME'25, GPQA-Diamond, LiveCodeBench and 64K RULER retrieval, while at 17.5 GiB it is the smallest of those compared and its prefill is 14–19% faster. Also fixes the global-scale mismatch that arises when a kernel that fuses modules into a single GEMM serves NVFP4 checkpoints calibrated per module
- Sep 1, 2026Shaowen Wang et al. — SMELT released — points out that evaluations of looped transformers, which repeat a block of shared layers to increase effective depth, usually compare at a fixed model size and so mix up architectural advantage with extra FLOPs; presents a recipe (Sparse MoE Transformer, middle layers Loop Twice) that runs the middle half of an MoE transformer's layers twice while matching all three budgets — FLOPs per token, total non-embedding parameters and KV cache. Scaled to four sizes of up to 54B non-embedding parameters and fitted Chinchilla-style scaling laws separately for each architecture
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