Pentagon Awards Scale AI a $500M Contract, a Five-Fold Expansion of Military AI Use

The U.S. Department of Defense (the Pentagon) has signed a $500 million (roughly ¥75 billion) contract with Meta-backed Scale AI. The deal brings the company's data analysis and decision-support AI into full-scale military use, marking a dramatic five-fold expansion from the $100 million contract awarded in September 2025.

Dan Tadross, head of Scale AI's public sector, said in an interview that the original $100 million contract was being 'stretched to its limits.' The expansion comes against a backdrop of rapidly growing volumes of military data and a rising need for AI adoption. It's a sign that AI-driven data processing in the defense sector has moved beyond the experimental stage to an operational scale.

That said, expanding military AI also raises ethical and social debates. Questions such as how far AI decision support should substitute for human judgment, and how to ensure transparency and accountability, need to be discussed in depth as the technology advances.

Zyphra's ZAYA1-8B — A Small MoE Trained on AMD Closes In on Large Models

ZAYA1-8B, released by Zyphra, is a small, reasoning-focused MoE (Mixture of Experts) model trained on AMD hardware. Despite having fewer than 1B active parameters, it outperforms open models with several to dozens of times more parameters on math and reasoning tasks.

Particularly noteworthy is that, by leveraging test-time compute, it posts scores approaching those of frontier models such as DeepSeek-V3.2 and GPT-5-High. From an 'intelligence density' perspective, its design philosophy of maximizing performance per model size could greatly expand practical use on edge devices and in local environments.

The fact that it was trained on AMD also matters. Amid moves to break away from NVIDIA dependence, demonstrating that high-quality models can be trained on AMD infrastructure is a meaningful step toward diversifying AI infrastructure.

DeepSeek V4's Measured Agent Costs Are Striking — Just 3% of Opus 4.7

An analysis of 922 agent task traces has been drawing attention in the r/LocalLLaMA community on Reddit. According to the analysis, DeepSeek V4 Flash's input token price via OpenRouter is about 0.03x that of Claude Opus 4.7, yet its actual cost per agent task is even lower, showing overwhelming cost efficiency compared to Opus 4.7.

Because input tokens account for the bulk of the cost in agent tasks, DeepSeek V4's cheap API pricing translates directly into a large cost gap. Having V4 — a model with top-tier performance among open source models — available at this cost level could fundamentally change the economics of running agents.

That said, a comprehensive real-world evaluation covering not just cost but also latency and output quality stability awaits further verification.

Atlas Inference Engine Goes Open Source — Qwen3.5-35B at 111 tok/s on GB10

Atlas, an inference engine developed by Avarok Cybersecurity, has been open sourced. It features a pure Rust + CUDA implementation with no dependency on PyTorch or Python runtimes, and reportedly runs Qwen3.5-35B (NVFP4, MTP K=2) on the NVIDIA DGX Spark (GB10) at a peak of 130 tok/s with sustained performance of 111 tok/s.

It achieves 3.0–3.3x throughput compared to conventional vLLM, attributed to hand-written CUDA kernels optimized for the SM120/121 of NVIDIA Blackwell. The Docker image is about 2.5GB, with cold starts in under two minutes.

For the local LLM community, the arrival of a fast inference engine independent of the Python ecosystem is a meaningful development that broadens deployment options. It is especially promising for edge environments and resource-constrained settings.

SpaceX Files Plans for New 'Terafab' Semiconductor Plant — A $55 Billion Project

SpaceX has reportedly filed plans for a 'Terafab' semiconductor plant in Grimes County, Texas, with an initial investment of $55 billion (roughly ¥8.2 trillion). The move appears aimed at securing semiconductor production capacity to meet AI demand.

As the compute resources needed for AI model training and inference surge, major tech companies have been investing heavily in in-house semiconductor sourcing and production, one after another. SpaceX has now made clear its intent to push forward with vertical integration of AI infrastructure. Details such as plan approval and the actual production start date, however, await future developments.

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