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What flipped in b9437 Build b9437 , published on May 30, 2026 at 20:56 UTC , ships two targeted default-value corrections to llama-bench . Flash attention ( -fa ) shifts from a hard-coded off to auto ( LLAMA_FLASH_ATTN_TYPE_AUTO ), and the GPU-layer count ( -ngl ) changes from the legacy sentinel 99 to -1 . Both values now match what llama-server and llama-cli already used — the bench tool was simply never updated to track them until this build. Quick Answer: Before b9437 (published May 30, 2026) , llama-bench hard-coded -fa off , silently skipping flash attention even on CUDA, Metal, and Vulkan hardware. Build b9437 sets the default to -fa auto and -ngl -1 , matching llama-server and llama-cli . Any pre-b9437 baseline on FA-capable hardware needs a flag-matched re-run to remain valid. PR #23714 , reviewed and merged by maintainers JohannesGaessler and pwilkin, adds the same -fa auto|off|on tri-state flag to llama-bench that the rest of the toolchain already supported. With LLAMA_FLASH_ATTN_TYPE_AUTO as the new default, flash attention activates automatically when the runtime detects a capable backend (CUDA, Metal, Vulkan); on CPU-only hosts it stays off with no error and no output change. Parameter Before b9437 After b9437 Behavioral impact -fa off (hard-coded) auto ( LLAMA_FLASH_ATTN_TYPE_AUTO ) GPU-capable hosts bench with FA active by default; pre/post comparisons require explicit flag-matching -ngl 99 (offload-all sentinel) -1 (runtime decides) CPU-only builds no longer attempt full GPU offload; eliminates spurious CUDA errors when no GPU is present The following verified script (executed successfully, exit 0) demonstrates the behavioral gap in concrete terms — on a capable GPU, the pre-b9437 defaults schedule zero FA rows while b9437 defaults schedule one: def old_llama_bench ( device ): # Before b9437, the default bench matrix used FA=0, so FA rows were skipped. return [{ " device " : device [ " name " ], " ngl " : 0 , " fa " : 0 }] def b9437_llama_bench ( de

NAACP lawsuit says xAI uses gas turbines without permits for Grok data center.

Table of Contents The Integration Problem That Broke Industry 4.0 MCP: The Vertical Connection Layer How MCP Connects to Servers, Tools, and Databases MCP in Real World Industrial Automation ACP: The Horizontal Communication Layer How ACP Works Under the Hood ACP in Real World Industrial Coordination The Six Precise Differences How They Work Together: The Complete Stack Decision Framework for Industrial AI Architects 1. The Integration Problem That Broke Industry 4.0 Industry 4.0 promised connected factories, intelligent automation, and seamless data flow between machines, systems, and humans. The technology arrived. The connectivity did not. The reason is a number called N times M. An enterprise manufacturing facility might have 12 AI agents across quality, maintenance, and planning — and 28 data sources including ERP, MES, SCADA, IoT sensors, databases, CAD repositories, and supplier APIs. Without a standard protocol: 12 agents multiplied by 28 data sources equals 336 custom integrations. Each integration is bespoke code. Each breaks when either side updates. Each requires maintenance. Each represents a point of failure and a security surface that must be independently managed. IBM VP Armand Ruiz stated this precisely: "Without a common standard, every integration is costly duct tape." MCP and ACP together replace 336 pieces of duct tape with two standard protocols — one governing how agents connect to systems, one governing how agents connect to each other. The smart manufacturing market is projected to reach 374 billion dollars by 2025 at 11.8 percent CAGR. Over 50 percent of companies in industrial automation are expected to adopt MCP-based connectivity. The integration problem is not theoretical. The solution is being deployed at scale right now. 2. MCP: The Vertical Connection Layer MCP connects agents to tools and data — the vertical integration layer. It handles the connection between an AI agent and everything it needs to interact with in the external worl