Day 2 raises a different question. If you are building an AI-native enterprise, when do you rent compute, when do you reserve dedicated capacity, and when does owning it become the competitive advantage? Lambda President of Cloud Services David Ward joins a panel on exactly that. It takes place Wednesday, September 30, 11:40 a.m.–12:25 p.m., in the Future of Compute track. Panel page at yotta-event.com/the-ai-nativ…

The argument in one line. AI infrastructure is moving from traditional data centers to gigawatt-scale campuses, and the companies building it are changing with it.

Session details: “Building a Cloud for the Age of Superintelligence” Tuesday, September 29, 2026 2:00–2:25 p.m., Main Stage Caesars Forum, Las Vegas Lambda Co-Founder Stephen Balaban will discuss the infrastructure buildout driven by frontier-model revenue, the shift from traditional data centers to gigawatt-scale AI campuses, and Lambda’s roadmap toward three gigawatts of AI compute capacity by 2030. Session page at yotta-event.com/the-gpu-myth…

AI compute is not becoming a commodity. The industry is building vertically integrated infrastructure, reaching from software and networking to the physical sites and power that make large-scale compute possible. The stakes are physical. Lambda Co-Founder & CTO Stephen Balaban makes that case on the Main Stage at Yotta 2026. He will also discuss our target of 3 GW of AI compute capacity by 2030.

Stephen Balaban (@stephenbalaban) has been through 5 pivots in 14 years. He started with facial recognition and AI filters before building the workstation and GPU compute business. The interview is a look at the long, non-linear path behind Lambda, and the decisions that shaped it along the way. Listen to the full conversation with @LambdaAPI’s CTO below on the @FoundersInArms podcast with @immad and @rajatsuri. foundersinarms.substack.com/…

Ask a 3D vision-language model what's near the table and in front of the curtain, and it might guess "sewing machine." The right answer is a tray rack. CVP (UC San Diego + Lambda, WACV 2026) fixes this with a target-affinity token for task-relevant objects and an allocentric grid for global context. Against Video-3D-LLM: • SQA3D EM: 58.6 → 62.3 • Scan2Cap CIDEr: 83.8 → 90.5 • Better on all 5 benchmarks tested Full results across ScanQA, SQA3D, ScanRefer, Multi3DRefer, and Scan2Cap, plus how the central/peripheral split works: lambda.ai/blog/cvp-spatial-r…

.@stephenbalaban on why Lambda prices compute like a utility, dollars per GPU, not per token, in Georgia Butler's latest Tokenomics piece for @dcdnews: "…in the same way that a utility provider might look at selling dollars per kilowatt hour, we look at dollars per GPU."

Training and evaluation ran on Lambda GPU infrastructure Paper: arxiv.org/abs/2604.03911v1 Blog: lambda.ai/blog/eginterpolato…

AI for molecular dynamics has a data problem. The trajectories you need to train on are expensive. EGInterpolator (ICLR 2026, with Stanford) learns molecular structure first from abundant conformer data, then uses scarce MD data to learn motion. On the DRUGS benchmark, it reduced the gap to reference simulations by 73% for bond angles, 78% for bond lengths, and 24% for torsional motion. The structure-first ablation also matters. Removing pretraining increased mean JSD from 0.173 to 0.332 for bond angles and from 0.142 to 0.386 for bond lengths.

GPU utilization increased from ~20% to 43% on a reservation of 96 NVIDIA H100 GPUs, while cutting queue starvation by 74%, with blocked jobs falling from roughly 10 per day to around 4. That’s the concrete result @SpreeAI saw after fixing their orchestration. When a unified diffusion model requires 80–100 GB of memory, you can’t simply throw workloads at a cluster and expect to use those GPUs efficiently. SPREEAI was dealing with workload fragmentation, ad-hoc submissions, and storage I/O blocking that left expensive GPUs idle. Working with Lambda’s ML engineering team, they implemented MLflow-based experiment orchestration with structured queuing and workload matching. They also connected Lambda’s Prometheus APIs to Grafana for real-time visibility into utilization gaps. The video testimonial covers how they diagnosed the bottlenecks and what the remediation looked like.

The failure that stands out is how quickly multi-turn editing exposes weak preservation. Each new instruction has to land without undoing earlier edits or changing unrelated content. Errors compound even when single-turn results look strong.

Evaluating image editing models with a single score hides the nuance behind a lower score. EdiVal-Agent (ICLR 2026) splits evaluation across instruction following, content consistency, and visual quality. Its agentic judge reached 81.3% agreement with human judgments, compared with 75.2% for a VLM-only evaluator and 68.9% for CLIP_dir. lambda.ai/blog/edival-agent-…

Build or buy AI? Wrong question. Lambda's @boborado is on the main stage at AI Infra Summit 2026 with @JLL CTO Yao Morin, @usbank EVP & Chief AI Officer Prashant Mehrotra, and @carrier Chief Data & AI Officer Arun Nandi, and the room's landing on the same answer: it's build AND buy. New term coined: valuemaxxing. Using value per task as a lens to govern resource allocation, while architecting around agility (easy model swaps) and availability (SLAs and model lifecycle).

MLPerf Inference v6.1 is out, and we have two firsts: the first agentic inference workload on datacenter hardware, and the first MLPerf deployment of a model over a trillion parameters. On 4x NVIDIA Blackwell Ultra GPUs, we posted the leading Offline throughput on GPT-OSS 120B among all Blackwell Ultra GPU submissions, plus an 8.85% throughput gain over v6.0 on identical hardware. That's six months of pure software optimization. On NVIDIA HGX B200, we swapped in Kimi K2.6 for the open-division agentic benchmark, running a 1T+ parameter model where the reference workload expects 27B. Same harness, no memory ceiling. Full results and methodology in the blog. Link below. lambda.ai/blog/mlperf-infere…

Behind the scenes at AI Infra Summit 2026: @lambdaapi's Dave Ward, president of cloud services, shares how our @nvidia DSX MaxLPS testing let us run more nodes inside the same power budget, turning stranded capacity into usable compute.

.@NVIDIA's Ian Buck at AI Infra Summit 2026: "The power profile varies by workload. That gap is an opportunity." We can confirm. In our testing, @NVIDIA DSX MaxLPS ran 19 nodes using the power we'd normally spend on 16: nvidia.com/en-us/case-studie…

Power is becoming a major constraint on AI infrastructure. In a proof of concept on NVIDIA HGX B200 systems, Lambda used NVIDIA DSX MaxLPS to run 19 nodes within the same aggregate power budget as a 16-node baseline, observing ~24% more token throughput and ~23% higher performance per watt. More productive AI capacity from the power already available. Read more via @NVIDIA: nvidia.com/en-us/case-studie…

Today, Lambda signed the @WhiteHouse Ratepayer Protection Pledge. We believe that the infrastructure supporting America's AI future should deliver meaningful benefits for local communities while protecting consumers and ratepayers. Learn more: lambda.ai/blog/lambda-signs-…

The researchers released the paper and their data, along with the model checkpoints and offline environment. NVIDIA NeMo Data Designer also builds on the same approach. Full writeup on our blog: lambda.ai/blog/openresearche…

The work comes from Texas A&M, Waterloo, UC San Diego, Verdent AI, NetMind AI, and Lambda, and was accepted to EMNLP 2026. @NVIDIAAI has already incorporated OpenResearcher trajectories into the SFT data for Nemotron 3 Ultra.