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:

.@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."

Wearable AR has had a decade of attempts and still hasn't found the product. The pattern is familiar: a technology looks inevitable long before it's actually usable. Worth remembering the next time something seems like it should obviously work. More from our conversation with @stephenbalaban of Lambda on Founders in Arms. Link in bio.

More GPUs do not guarantee more progress. Better orchestration does. With @lambdaAPI, we increased GPU utilization from ~20% to 43% and cut queue starvation by 74%. Make every GPU count. #SPREEAI# #Lambda# #AIInfrastructure#

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.

Huh, this is rather fascinating. Lambda Labs, the AI neocloud company, originally sold an HD camera-enabled baseball cap. The idea, as I surmise, was to acquire images for convolutional neural networks, a different technology from the now more well known large language models.

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).

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:

More productive AI capacity from the power already available. In a proof of concept, @LambdaAPI used NVIDIA DSX MaxLPS to run 19 nodes within the power budget of a 16-node baseline, delivering 24% more token throughput and 23% higher performance per watt. Read the success story ➡️

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:

Most teams lose sight of what exists outside the world of frontier APIs when it comes to their everyday work. Lambda’s @TheZachMueller sat down with @HamelHusain on when an open model is the right call, and how to serve it well once you’ve committed. It also builds on @_xjdr’s point that open weights can handle ~90% of tasks for ~90% of people. The rest stays on frontier work.

Lambda has raised over $1.5B in equity to build superintelligence cloud infrastructure. TWG Global, USIT, and existing investors led the Series E round to position Lambda to execute on its mission to give everyone the power of superintelligence. One person, One GPU. Press release: