If you're at AI Infra Summit, this is the panel you shouldn't miss! The Speed Problem: What It Actually Takes to Stand Up Capacity ⏰ Wed, Sept 16, 3:00 PM PT 📍Compute Track at AI Infra Summit in Santa Clara vCluster CEO Lukas Gentele is moderating this one live with Jay Jubran (@ZyphraAI), Kasra Danesh (@sfcompute), and Yujing Qian (@gmi_cloud) Three companies. Three completely different bets on the same problem. Zyphra trained a frontier mixture-of-experts model end to end on AMD silicon, zero NVIDIA in the stack, then turned that build into an AI Cloud. SF Compute built a physically settled compute market. Long-term contracts get supercomputers financed. Resale lets customers recover an average of 25% of what they spend on capacity they don't use. They just moved $245M of Blackwell B300 across two deals. GMI Cloud stayed on the default stack and bet everything on speed: NVIDIA Reference Architecture, a 99.9% uptime SLA, $500M in new capex behind nine-figure enterprise contracts. Silicon. Capital. Software. What's actually standing between "we announced capacity" and "a workload is running on it,". Let's find out. #AIInfraSummit# #AIInfrastructure# #AICloud# #Compute# #Inference#

New episode with @johnschulman2, @oneill_c and @BerenMillidge. I got together with some of the most insightful AI researchers I know who are at the openish companies, because I wanted to hear the details of what's actually happening at the frontier and what comes next. 0:00:00 – Steelmanning the case against RSI 0:18:39 – What’s driving the Chinese labs’ progress 0:28:06 – How will automated AI researchers be trained 0:33:51 – Will long-horizon RL elicit AGI? 0:45:24 – The sim-to-real gap 1:00:33 – How much progress is explained by data? 1:18:03 – Why is RL working so well? 1:24:54 – Move 37 and entropy collapse 1:28:31 – Rapid-fire timelines

Zyphra's @BerenMillidge was on the @dwarkesh_sp Podcast discussing reinforcement learning, AI progress from data and architecture innovations, and their predictions for recursive self improvement with @johnschulman2 and @oneill_c.

Zyphra Research releases PUFFER, a novel incremental fuzzy deduplication system for LLM-scale datasets. PUFFER runs entirely on CPU and achieves 11-35x speedup over existing methods. We use PUFFER internally for our own training dataset and release it under Apache 2.0 license.

Excited to share updates to ZUNA1.1, Zyphra's open-source foundation model for our thought-to-text efforts: • Full technical report • EEG Playground UI updates • Tutorial walkthrough video • More efficient inference Try it at 🧵

Excited to have Zyphra's @BerenMillidge @rawsh0 and @rishiiyer01 at AMD’s annual Advancing AI conference speaking about how Zyphra built the first MoE Diffusion LLM on @AMD. Registration still open:

Introducing ZUNA1.1, a far more flexible version of our open EEG foundation model and a further advancement toward noninvasive thought-to-text. It reconstructs, denoises, and upsamples messy real-world EEG. Apache 2.0, available free in the Zyphra Cloud EEG Playground🧵

Excited to share ZONOS2 updates: • Demo showcasing unique features: expressive speech, multilingual output, and mid-sentence code-switching • Updated local inference code • Full tech report • New pricing and higher concurrency Try ZONOS2 now on 🧵

Today we're releasing ZONOS2, our next-generation real-time TTS model with high-fidelity voice cloning. ZONOS2 is the most expressive open-source TTS model, released under Apache 2.0 and available on Zyphra Cloud on @AMD. 🧵