Qwen3.8-Flash-Next Preview Runs on NVIDIA GB300 NVL72
Alibaba released Qwen3.8-Flash-Next model weights as a preview of the upcoming Qwen4 architecture. The multimodal MoE model has 125B parameters plus 51B N-gram embeddings, with 6B activated per token. It supports a 262,144-token context, extensible to 1M. NVIDIA demonstrates it on GB300 NVL72 for agentic coding.
Sources and evidence
Summary last validated Aug 31, 2026
Reader actions
Report an issue
Use this for an incorrect summary, wrong source, duplicate story, or wrong category. Submissions are private and do not change the story automatically.
Related coverage
- models
Cloudflare adds Clef-omni multimodal model, speeds Clef and cuts Clef-flash pricing
Cloudflare expanded its Clef decision model family with Clef-omni, which natively processes audio, video, images, and text in a single pipeline. The company also increased Clef inference speeds by up to 2.0x and lowered pricing for Clef-flash.
- models
AWS Adds Bedrock Models, Faster AgentCore Agents, Knowledge Base Sync
AWS's September 2026 recap covers Amazon Bedrock, Bedrock AgentCore, and Strands updates: broader model choice, faster serverless agents with built-in evaluation, and automated knowledge base syncing via native enterprise connectors. The post aggregates the month's releases for AI builders across AWS's model and agent tooling.
- agents
NVIDIA Highlights Developers Using Frontier AI Agents With Omniverse Libraries
NVIDIA published a blog post describing how developers combine frontier AI models with NVIDIA Omniverse libraries to build simulation applications. The post says developers direct AI agents to assemble assets, connect physics and rendering, and verify scene behavior, supporting work such as exploring scenarios, investigating failures and improving designs.
- open source
NVIDIA Details Five-Step Workflow for SimReady Robotics Assets
NVIDIA published a five-step workflow for converting CAD assets into SimReady robotics simulation assets using Omniverse libraries, SimReady Foundation specifications, and agentic NVIDIA skills. The process covers configuring and validating materials, collision geometry, joints, and physics properties beyond simple OpenUSD geometry conversion, preparing assets before robot behavior testing.
- agents
NVIDIA KGMON Team Places Second in KDD Cup 2026 Data Agents Competition
NVIDIA's KGMON team placed second in the KDD Cup 2026 Data Agents competition. The team built a system around making an agent's harness smaller, clearer, and easier to verify. The competition required agents to answer natural-language questions across heterogeneous sources including databases, CSV and JSON files, prose documents, PDFs, and briefing videos.