A self-evolving agent + a 428B model + 3 Macs = ? Your own AI lab. We ran @MiniMax_AI M3 locally with @tryParallax, right on our desk. Then @GA_agent_ai took over to create a 5-stock portfolio and write it to disk. No cloud. No API bills. Nothing left the machine. Wild to see a ~3K-line agent drive all this with a 400B+ model on local hardware. Thanks to the GenericAgent and MiniMax teams for making local AI feel real.

This is why we built Parallax @tryParallax As open models get stronger and agents get more capable, local-first AI becomes much more than a privacy story. It becomes a new way to build with open intelligence that stays close to your data, your tools, and your machines.

This is a glimpse of where local AI is heading and we are glad to be part of it. Really impressive work by all the teams involved @Gradient_HQ, @tryParallax, and @GA_agent_ai

nvidia going all in on local ai. here's our take: it shouldn't depend on which chip you bought. sparks, macs, the 5090 already on your desk, we cluster across all of it and split your favorite model pipeline-parallel so it runs fully private and local.

We're hiring at Gradient. Building open-source environment infrastructure for our distributed RL training stack — reproducible, scalable to thousand-GPU runs Looking for 1–2 RL Environments engineers / tech leads: You've designed verifiers, built sandboxes for agentic RL rollouts, or shipped RL training data pipelines that survived contact with real training. Domain depth in math, code, agent, tool, or GUI is a plus. PhD not required. Also hiring research interns: PhD / Masters students with hands-on RLHF / RLVR / GRPO / DPO / agentic RL experience. Open-source footprint matters more than paper count. Most intern roles convert post-grad. No age cap. Founding-team-level equity for the right people. DMs open.

Thrilled to see @tryParallax live in production on @Theta_Network. This is exactly why @Gradient_HQ built Parallax: turning the world’s GPU mesh into a sovereign, distributed token factory. Congrats on the milestone! 🫡

glad we could help! with the agentic adoption soaring, privacy and token cost are already the top concerns for both agent and human users. that's what parallax's built for.

Catch @alex_mirran on DevNTell this Friday. He’ll break down the infrastructure we're building at Gradient and show you exactly how to get started today. RSVP below👇

When you scale parallel agents, prompt updates degrade fast. The more trajectories you process concurrently, the more generic your learned prompts become. Our researchers worked with @lihanc02 and team on Combee to rethink how aggregation works at scale. Results held up across GEPA and ACE even past 80 concurrent agents. Read more on this research👇

If software no longer needs you to operate it, what does an “application” even mean? That’s what we’re digging into at The Agentic Shift with panels, demos, and speakers from Google, PixVerse, MiniMax + more. SF | Apr 8 Sign up here: lu.ma/y07o6vuo

As fellow training nerds, UniPat AI’s approach caught our eyes. Synthesizing future-event data to fix outcome bias and actually beat human prediction markets is defined something worth checking out!

Everyone's scrambling for more compute. The harder problem is making distributed compute actually work for multi-agent systems. That's what Echo-2 is built for.

Benchmarks that test what models have memorized are saturating fast. ARC-AGI-3 is asking a harder question: can AI actually learn something new on the fly? One direction we've been exploring: multi-agent orchestration. In our study, coordinating four frontier LLMs across multiple turns consistently matched or outperformed the strongest single model, even on tasks none of them could solve alone. The gap between "best single model" and "best coordination of models" is where a lot of the real progress is hiding. More on our multi-turn, multi-agent orchestration study: arxiv.org/abs/2509.23537

Great to see multi-agent systems getting serious engineering attention. One thing we think about a lot: as agents get more capable, the orchestration layer matters just as much as the models themselves. Our work on Symphony explores what happens when you remove the central controller entirely and let agents coordinate across consumer hardware through decentralized task allocation and weighted voting. We've achieved up to 41.6% accuracy gains over centralized frameworks, running on commodity GPUs with <5% orchestration overhead. Find out more in our Symphony paper: arxiv.org/abs/2508.20019

local ai has picked up fast since openclaw dropped. with the latest wave of small capable models, more people are running serious workloads on their own hardware. if you missed this good local ai tutorial from @yacinelearning or want a refresher on how distributed scheduling actually works under the hood, it's worth the rewatch over the weekend!

Dobby is a free elf now. Open models, open orchestration, open compute. The agentic RL stack that used to live inside walled gardens just showed up on hardware you can order and frameworks you can fork. No masters needed.

Our GTC takeaway is clear: NVIDIA is betting hard on open. - NemoClaw turns OpenClaw into enterprise infrastructure. - Nemotron 4 will be open-sourced. - Nemotron Coalition puts eight labs on a shared open frontier model. This is what we've been building toward. Open infrastructure for open intelligence is the direction the biggest AI companies are taking.