Tracked topic
For the first time, Qwen3.8 brings a Qwen-Max-class model to open release. Built on the architectural foundation of Qwen3.5, Qwen3.8 delivers substantial gains across coding, professional work, research, and long-horizon agentic tasks. Beyond answering harder questions, Qwen3.8 is designed to carry complex, multi-step tasks through to completion with greater reliability. Qwen3.8 features the following enhancements: Core Capabilities: Comprehensive improvements across coding, professional work, research, and long-horizon agentic tasks. Agent Execution: Stronger autonomous planning and better ha
Day 0 Support for Qwen 3.8 on AMD Instinct GPUs…vLLM compatibility through v0.23.0 keeps both current and future deployments aligned with the fast-moving open-source ecosystem. What’s Under the Hood ZenDNN 6.0 builds on the modular…
…302 local sources / 801 prepared chunks; orchestrator Qwen3.6-35B-A3B (mixture-of-experts, ~3B active, 4-bit) served via llama.cpp; FastEmbed ONNX nomic-embed-text-v1.5 embeddings and real…
…This allows AI frameworks to benefit from MRC’s performance and resiliency improvements without requiring significant changes to existing code. Production-Ready Implementations and Deployment Flexibility AMD has implemented the MRC transport…
…In those deployments, a CPU acted as the head node for a server with four to eight GPUs. The one head node CPU handled the scheduling, I/O and system management, while…
…Model coverage has also doubled, with added support for GPT-OSS-20B , Qwen 2.5, and other models. The AI toolkit (AITK) now provides per-operator execution timelines and NPU utilization breakdowns…
To show you the most relevant results, we’ve omitted some entries very similar to those already shown. Repeat the search with the omitted results included.