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 GPUsZenDNN 5.2.1 builds on the modular multi-backend architecture introduced in 5.2, extending it with deeper quantization paths and kernel-level optimizations across the ZenDNN runtime.
ZenDNN 5.2.1: Deepening Quantization and Expanding the AI Inference Frontier on AMD EPYC™ CPUsMiniMax M3 is a new open-weight model for coding, agentic, and multimodal workloads. MiniMax describes M3 as combining three frontier capabilities in one model: strong coding and agentic task performance, long-context MiniMax Sparse Attention (MSA), and native multimodality for text, image, and video understanding. The 1M-token context window of MiniMax M3 enables sophisticated, long-horizon application workflows, including autonomous software engineering agents, repository-wide reasoning, and native multimodal document analysis alongside tool-driven automation. The AMD day-zero enablement foc
Day 0 Support for MiniMax M3 on AMD Instinct GPUs…Agent backend mini-SWE agent. Claude Code SDK/CLI with dynamic Workflows. Orchestration Deterministic Python orchestration. Deterministic JavaScript Workflow controlling budget, fan-out, verification, and stop logic. Architecture Orchestrator plus generic workers…
…AMD) today announced that its next-generation AMD EPYC™ processor, codenamed “Venice,” is ramping production in Taiwan on TSMC’s advanced 2nm process technology, with future plans to ramp production at TSMC…
…The LLM produced synthesizable code at roughly 80% first-attempt correctness . The generated code was structurally sound, with correct stream interfaces, proper loop labels, and appropriate pragma placement. The remaining 20% required…
…with KV Cache Reuse on AMD Ryzen AI Jun 10, 2026 Local large language models are enabling a new class of AI PC applications, including private document assistants, coding helpers, conversational agents…
…July 23, 2026 Agentic AI Workflows Simplified Rocm.AI simplifies how developers build and serve with ROCm on AMD GPU’s July 23, 2026 AMD Launches Helios™: The Highest Performing Rackscale AI…
…August 05, 2026 Autoregressive Drift on AMD GPUs Explore AI-driven quantum circuit optimization with transformer models trained on AMD Instinct™ GPUs. August 05, 2026 Introducing AMD Instinct™ Coder: A Turnkey AI…
…July 16, 2026 From Vector Search to Agentic RAG: Building an Enterprise Research Analyst with hipVS — ROCm Blogs Learn how to build an agentic RAG research assistant using hipVS GPU-accelerated vector…
…models with Triton Inference Server and MIGraphX on AMD GPUs May 21, 2026 Deploying Hermes Agent for Free on AMD Developer Cloud with open models and vLLM Deploy Hermes Agent for free…
…and quantization, and it’s fully compatible with PyTorch. Because ROCm is open source, we can examine the kernels directly, take advantage of community contributions, and port our code directly with only…
…The experimental setup required hosting the 24B main model, the 3B auxiliary model, the 72B quantized reserve model, runtime buffers, active key-value/state buffers, and the additional overhead associated with compact…