$3 Million USD dataset open sourced, 1 Mil+ Context Length, Multiturn, Sub Agents 95%+ KVCache HitRate, GB300 NVL72, MI355, B200
Open-Source Agentic Inference Benchmark | InferenceX
Compare AgentX, InferenceX's long-context, multi-turn coding scenario, with fixed-sequence AI inference across chips and frameworks. Public NVIDIA and AMD runs update when configurations change.
Cheng on Twitter / X
We have been expecting this since ollama's first pull request to MLX. It is just the beginning, CUDA & CPU backends are still improving and hopefully we will have one framework unifying inference & training for all platforms. https://t.co/EaBmEaNJhZ— Cheng (@zcbenz) March 31, 2026
NVIDIA Rubin CPX Accelerates Inference Performance and Efficiency for 1M+ Token Context Workloads | NVIDIA Technical Blog
Inference has emerged as the new frontier of complexity in AI. Modern models are evolving into agentic systems capable of multi-step reasoning, persistent memory, and long-horizon context—enabling…
NVIDIA Levels Up Local AI Agents Across RTX PCs and DGX Spark
Announced at GTC Taipei at COMPUTEX, NVIDIA OpenShell brings secure agents to Windows with 2x inference performance on llama.cpp — plus, Adobe rebuilds its apps with performance and memory enhancements, and Blender adds NVIDIA DLSS 4.5 Ray Reconstruction for NVIDIA RTX Spark.
AI Inference Pricing, EU Hosted, Per Token | TensorX
Transparent, pay-as-you-go pricing for private AI inference on TensorX. No lock-in, EU-hosted, with zero data retention and an OpenAI-compatible API.
Advancing AI Infrastructure for Agentic AI with NVIDIA DOCA In-Silicon Security | NVIDIA Technical Blog
The AI era is driving a new class of infrastructure: AI factories that transform data into intelligence for autonomous AI agents operating at unprecedented scale. Powered by accelerated computing…
Release v0.29.0 · ml-explore/mlx
Highlights Support for mxfp4 quantization (Metal, CPU) More performance improvements, bug fixes, features in CUDA backend mx.distributed supports NCCL back-end for CUDA What's Changed [CUDA]...
Open Source AI Inference Benchmark | InferenceX
Compare AI inference performance across GPUs and frameworks. Real benchmarks on NVIDIA GB200, B200, AMD MI355X, and more. Free, open-source, continuously updated.
Dria on Twitter / X
Introducing Inference Arena v2.0.An agentic experience that searches, analyzes, and delivers insights about LLM inference.When we first launched, our goal was simple: make it easier for developers to compare models, engines, and hardware without digging through scattered… pic.twitter.com/fgWgos48lW— Dria (@driaforall) September 30, 2025
The Inference Shift
Agentic inference is going to be different than the inference we use today, and it will change compute infrastructure because speed won’t matter when humans aren’t involved.
OpenAI co-founds the Agentic AI Foundation under the Linux Foundation
OpenAI co-founds the Agentic AI Foundation under the Linux Foundation and donates AGENTS.md to support open, interoperable standards for safe agentic AI.
New data agents across the Agentic Data Cloud | Google Cloud Blog
Learn about new data agents and tools for business analysts, data scientists, and database admins to integrate with the Agentic Data Cloud.
Cua: Scale computer fleets for computer-use agents
Scale Linux, Windows, macOS, and Android computer fleets for computer-use agents with one open-source MCP/CLI driver.
Qwen3-Coder: Agentic Coding in the World
GITHUB HUGGING FACE MODELSCOPE DISCORD Today, we’re announcing Qwen3-Coder, our most agentic code model to date. Qwen3-Coder is available in multiple sizes, but we’re excited to introduce its most powerful variant first: Qwen3-Coder-480B-A35B-Instruct — a 480B-parameter Mixture-of-Experts model with 35B active parameters which supports the context length of 256K tokens natively and 1M tokens with extrapolation methods, offering exceptional performance in both coding and agentic tasks. Qwen3-Coder-480B-A35B-Instruct sets new state-of-the-art results among open models on Agentic Coding, Agentic Browser-Use, and Agentic Tool-Use, comparable to Claude Sonnet 4.
LLMs Can Now Write GPU Kernels That Beat torch.compile - Break AI Scaling Limits in 7 Days
We're now seeing multi-agent systems that take your PyTorch code and produce CUDA or Triton kernels with 2x to 14x speedups over torch.compile(mode='max-autotune-no-cudagraphs'). Not on toy benchmarks. On real models like Llama-3.1-8B, Whisper, and Stable Diffusion. Learn proven techniques to shift the scaling law intercept and achieve 10-50% performance gains.
