The Lambda Deep Learning Blog

Lambda at GTC 2026: an early preview

khushboo.goel@lambda.ai (Khushboo Goel) — Tue, 10 Mar 2026 12:13:00 GMT

Join Lambda at NVIDIA GTC 2026

Built for AI. Ready for Superintelligence.
Lambda is heading to NVIDIA GTC 2026 as a Platinum sponsor.

Open model, open metrics: How Lambda and the Olmo team trained Olmo Hybrid

Lambda — Thu, 05 Mar 2026 16:04:09 GMT

Open-source models are now one of the main engines of progress in AI. When strong models like Nemotron, Llama 3.1, and Qwen3-Next are released openly, the community can reproduce results, improve training recipes, and build new products on top of them faster than any single lab could alone.

Mila World Modeling Workshop: wrap-up

amirali.zadeh@lambda.ai (Amir Zadeh) — Sat, 28 Feb 2026 00:18:24 GMT

What if large language models had eyes, ears, and other sensors that let them perceive the real world? What kind of internal representation would they build? Could such systems become fully autonomous, and what scientific and technical breakthroughs would it take to get there?

Lambda appoints Charles Fisher as Chief Financial Officer

Lambda — Thu, 19 Feb 2026 20:26:36 GMT

Seasoned finance executive brings decades of experience to support Lambda’s continued growth

Lambda appoints tech pioneer Jerry Hunter as Vice Chairman, Compute Delivery and Special Advisor to the Board

Lambda — Thu, 12 Feb 2026 22:32:18 GMT

Former AWS infrastructure leader and Snap COO brings 30 years of Hyperscale expertise to accelerate Lambda's growth

Lambda and Oumi partner for end-to-end custom model development

Lambda — Thu, 05 Feb 2026 14:00:00 GMT

Enterprises can now build and deploy custom models for their specific use cases 100x faster, with 10x better cost efficiency, and superior accuracy

TL;DR: Enterprises are stuck using large, closed off-the-shelf models that deliver unreliable, slow, and costly solutions with limited privacy, security, and control. Small, custom models are the solution, but they typically require deep expertise and months of effort. Today, we’re announcing a partnership between Oumi and Lambda to provide a complete solution for end-to-end model development and deployment, enabling custom AI in hours, not months, with real results such as 70% cost reduction and full control over technology and data privacy.

Kimi K2 Thinking: what 200+ tool calls mean for production

Lea Alcantara — Wed, 04 Feb 2026 14:45:18 GMT

TL;DR: Kimi K2 Thinking is Moonshot AI's open-source reasoning model, scoring 44.9% on Humanity's Last Exam with the ability to chain 200-300 sequential tool calls while maintaining coherent reasoning. The 1-trillion parameter MoE architecture activates only 32B parameters per inference. Fully open weights mean you can inspect reasoning chains, fine-tune on domain data, and deploy on your infrastructure; though production deployment requires serious GPU capacity.

From building AI infrastructure to shaping its standards: Lambda joins OCP

Alee Fong — Tue, 27 Jan 2026 20:41:32 GMT

AI infrastructure is moving faster than the data centers designed to support it.

Compute density is increasing. Power consumption is rising sharply. Thermal complexity is no longer an edge case. With every new generation of AI hardware, the gap between what modern workloads demand and what traditional infrastructure can deliver continues to widen.

How to deploy ML jobs on Lambda Cloud with SkyPilot

Cody Brownstein — Tue, 20 Jan 2026 13:15:01 GMT

TL;DR: SkyPilot is an open-source orchestration tool that automates ML job deployment on Lambda Cloud. This tutorial covers installation, configuration, and running an example job that evaluates DeepSeek-R1-Distill-Qwen-7B on multiplication tasks with automatic instance termination when the job completes.

2025 AI wrapped

Lea Alcantara — Tue, 13 Jan 2026 18:57:20 GMT

2025 was a year of momentum in AI. Intelligence progressed through new, innovative methods. Open-source communities released competitive models. Research labs and companies shipped new architectures, reasoning models, and optimization techniques. The pace was relentless: weekly releases, rapidly climbing benchmarks, innovations emerging from unexpected sources.

For those in the machine learning (ML) community, the real challenge was deciphering what actually mattered and separating genuine progress from noise.

In this report, we’ll uncover what defined AI in 2025. The perspective comes from working across hundreds of production deployments—from research experiments to systems serving billions of tokens daily, to direct insights gathered from real customer implementations and use cases observed at Lambda, spanning diverse industries and scale profiles.