Lambda

Every GPU cloud puts a hypervisor between your workload and the silicon. At rack scale, that overhead stops being a rounding error and becomes a tax on every training run. Lambda Bare Metal Instances remove it. Direct hardware access. API-driven lifecycle. No abstraction layer. NVIDIA BlueField DPUs run in Zero Trust Mode, inaccessible from the host OS. The host is yours. Production deployments start with NVIDIA GB300 NVL72. Read more: https://lnkd.in/dWXzYvHs

Hudson River Trading is partnering with Lambda to accelerate quantitative research and development. Lambda will help power HRT’s research roadmap with a full-stack architecture, including NVIDIA HGX B200 systems, advanced networking, storage, orchestration, and uptime. Read more: https://lnkd.in/gKaGf5Yr

Quants and FSI teams don't need generic AI benchmarks. They need to know if the model holds up when the full desk is querying during earnings season. Lambda is the first to publish audited STAC Research's STAC-AI LANG6 on NVIDIA HGX B200. Read more: https://lnkd.in/gUf8A9N2

Today at AI Council 2026, Zachary Mueller, Lambda's Head of Dev Rel, hosts the Model Systems track: how models are built, trained, and evaluated. Pre-training, RLHF, alignment, multimodal systems, and the systems-level decisions behind frontier models. He's joined by speakers from the teams actually building them: - Lucas Atkins, CTO, Arcee AI - Sami Jaghouar, Head of Research, Prime Intellect - Chris Alexiuk, Product Research Engineer, NVIDIA - Ezi Ozoani, Co-founder and CTO, Aethon Zach is also giving a talk: "Optimizing model training end-to-end: a tiny MoE case study." The premise: "just scale it" is expensive and hides errors. The optimizations that matter most (minimizing communications, choosing effective algorithms, maximizing FLOPs) start small. Zach walks through what worked at home, then what held up when he scaled it to the cloud. Recordings go live after the conference. Read more: https://lnkd.in/dE6veKkY

Is compute really the biggest hurdle in the AI race? Marc Boroditsky, CRO of Nebius, Nicholas Robbins, VP of Corporate Development CoreWeave, and Charles Fisher, CFO Lambda argue that the focus is shifting. While the industry has been obsessed with GPU supply, “capital—not compute—is emerging as the primary bottleneck in the AI build-out”. More from our Financing the AI Revolution event: https://thein.fo/4d3GGxB

Lambda closed a $1 billion senior secured credit facility, oversubscribed and upsized from $275 million in August 2025, to accelerate the expansion of our AI factory footprint and meet growing demand from the world’s leading AI teams. Read more: https://lnkd.in/gWZiJi-Q

100 year plan

Lambda co-founder and CTO Stephen Balaban + CEO Michel Combes are live on TBPN today, talking about what comes next: 3GW of AI compute under management by 2030, and the team built to deliver it. Tune in at 12:20 PM PT: https://lnkd.in/gpBeKBtR

Lambda names former Sprint chief Michel Combes as CEO. Co-founder and former CEO Stephen Balaban moves to CTO as the company's looking to raise more funds to invest in data center capacity, develop its own properties. The company, which Bloomberg has reported is mulling an IPO, also named John Donovan, former CEO of AT&T Communications, as board chair. The AI buildout will be as large as the $5 trillion mobile infrastructure buildout, but in a shorter timeframe, Donovan told me. "This is the worst time to be sitting on the sidelines.” https://lnkd.in/gw_bVVHT