OpenClaw, the popular open-source workflow scheduling platform for AI developers, has released version 2026.2.3 with major updates including a new announce delivery mode for Cron tasks, Moonshot model integration, and Cloudflare AI Gateway support.
In February 2026, Anthropic officially released the Claude Cowork Legal Plugin, a desktop-level intelligent assistant module that quickly ignited the legal tech sphere. This plugin is no longer a simple contract summarization tool, but an intelligent agent with agentic capabilities that can directly access enterprise internal systems like Slack, Box, and Microsoft 365 to automate complex legal processes.
NVIDIA officially announces mass production of its highly anticipated Blackwell GB200 AI chips, igniting the AI tech community with overwhelming orders from data center giants. With performance improvements up to 30x over the previous Hopper architecture, Blackwell not only solves AI model training bottlenecks but also drives NVIDIA's stock price to new highs.
Kling AI 3.0 launches with stunning photorealistic video generation capabilities including native audio synthesis, sparking global discussions about the future of filmmaking. The technology achieves 100% photography-grade realism with multi-camera control, physics-based lighting, and 4K output, potentially revolutionizing content creation across industries.
Anthropic launched a bold Super Bowl ad campaign positioning Claude as "forever ad-free," directly challenging OpenAI's recent introduction of ads in ChatGPT's free version. The 30-second spot sparked massive online engagement and reignited debates about AI monetization strategies.
KTransformers, developed by Tsinghua University's MadSys and Approaching.AI, optimizes CPU/GPU collaborative inference for sparse MoE models through AMX-optimized kernels, efficient device coordination, and expert deferral mechanisms, now integrated into SGLang for enhanced performance.
SGLang-Diffusion has achieved 2.5x performance improvements since its launch in November 2025, with support for new models, LoRA, parallel processing, and ComfyUI integration.
SGLang launches a highly optimized Pipeline Parallelism implementation designed for ultra-long context inference challenges. Through integrated optimizations and a clean design, it achieves a 3.31x speedup in prefill throughput for DeepSeek V3 on multi-node H20 clusters, demonstrating strong scalability for trillion-parameter models.
We developed Petit, a collection of FP16/BF16 × FP4 mixed-precision GPU kernels for AMD GPUs, enabling 1.74× faster Llama 3.3 70B inference on existing MI250/MI300 hardware without upgrades.
SGLang implements fully deterministic inference with only 34.35% performance overhead and enables 100% reproducible RL training in collaboration with slime, providing reliable solutions for rigorous scientific experiments.
The SGLang team shares their optimization progress on DeepSeek V3/R1 inference performance using GB200 NVL72, achieving 26,156 input tokens/s for prefill and 13,386 output tokens/s for decode per NVIDIA Blackwell GPU through techniques like FP8 attention, NVFP4 MoE, and large-scale expert parallelism.
This article presents comprehensive optimization strategies for deploying DeepSeek-R1 on H20 GPUs, achieving state-of-the-art performance of 16.5k input tokens/s and 5.7k output tokens/s per node through hardware-aware parallelization, kernel optimizations, and advanced scheduling techniques.
This article introduces PD-Multiplexing, a new serving paradigm in SGLang that leverages NVIDIA's GreenContext technology to achieve higher goodput for LLM services through efficient intra-GPU resource sharing between prefill and decode phases.
SGLang announces Day 0 support for DeepSeek-V3.2, implementing DeepSeek Sparse Attention (DSA) mechanism that significantly improves training and inference efficiency, especially in long-context scenarios.
We conducted an in-depth review of NVIDIA DGX Spark, a compact all-in-one system that brings supercomputing-level performance to desktop workstation form factor. While its unified memory design enables running ultra-large models, performance is constrained by memory bandwidth, making it ideal for prototyping and experimentation rather than production deployment.
SGLang collaborates with NVIDIA to leverage Blackwell architecture innovations, achieving breakthrough performance on DeepSeek models with up to 4x improvements, and is selected as the default inference engine for NVIDIA and AMD hardware in the InferenceMAX benchmark.
We introduce SGLang-Jax, a state-of-the-art open-source inference engine built entirely on Jax and XLA, achieving fast native TPU inference with advanced features like continuous batching, prefix caching, and speculative decoding.
We successfully optimized GPT-OSS 20B and 120B models on NVIDIA DGX Spark using SGLang, achieving state-of-the-art performance of ~70 tokens/s and ~50 tokens/s respectively, enabling fully local AI applications including coding agents.
SGLang announces first-day support for MiniMax M2, a flagship MoE model that returns to full attention after empirical findings show efficient attention methods face significant production deployment challenges.
SGLang Diffusion brings SGLang's top performance to diffusion model image and video generation, supporting mainstream open-source models with 1.2x to 5.9x speedups across diverse workloads.
