The Superintelligence Cloud
San Francisco, CA
Joined July 2012
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Lambda retweeted
NVIDIA introduces the OpenShell Reference Architecture to help businesses run AI agents under control.
The open design combines accelerated compute for agents with hardware-isolated security for deployment at scale.
Announced at GTC Berlin: nvda.ws/4zBujkE
Lambda retweeted
Scale AI securely across global infrastructure while maintaining full control of your models and data.
At #NVIDIAGTC Berlin, we’re introducing Confidential Bare Metal with NVIDIA Confidential Computing, enabling model builders to run sensitive AI workloads on dedicated third-party infrastructure securely.
@googlecloud is extending Confidential Bare Metal support to A4X Max instances, and @cohere plans to integrate it into Cohere Model Vault. ➡️ nvda.ws/4ixVneZ
ALT NVIDIA Confidential Computing Expands, Enabling Frontier Model Builders to Securely Scale Across Global Infrastructure
Lambda retweeted
🚀 Introducing Next-Concept Modeling (NCM): our new paradigm for fast, parallel, coarse-to-fine text generation.
Compared to strong diffusion language models (DLMs) at similar parameter size, NCM achieves:
⚡ Up to 31× faster generation on downstream tasks like summarization and machine translation with comparable or better quality.
🏎️ 10–21× faster unconditional generation across TinyStories, LM1B, and OpenWebText, with comparable or better generative perplexity and using 3–5× fewer training tokens.
🧩 Better performance on reasoning tasks like Sudoku, outperforming both AR and diffusion baselines with fewer sampling steps.
With @ishanskhare, @IddahMlauzi, @ekellbuch, @simran_s_arora, @Azaliamirh, and @HazyResearch.
More details in the thread 🧵👇
Lambda retweeted
Today, we’re open sourcing the full NVIDIA Run:ai platform as part of the NVIDIA DSX AI Factory Platform.
Developers, platform teams, and partners can build on production-grade AI workloads and GPU orchestration to improve GPU utilization, increase workload throughput, and bring AI factory solutions to market faster.
Deploy the integrated NVIDIA Run:ai platform or adopt individual components to fit your needs.
See what’s included, how to get started, and how to contribute: nvda.ws/4hRTubt
ALT NVIDIA Run:ai Is Now Open Source as Part of NVIDIA DSX AI Factory Platform
Lambda retweeted
AI runs on compute. Compute needs scaled infrastructure.
We are teaming up with @LambdaAPI to give AI infrastructure some helping hands.
Lambda retweeted
Today is Lambda's 14th birthday. Incorporated in Delaware on October 2nd, 2012. Happy Birthday Lambda!
Lambda retweeted
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.
Lambda retweeted
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.
Lambda retweeted
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.
Lambda retweeted
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 ➡️ nvda.ws/3VvR1fi
Lambda retweeted
One photo. Photorealistic try-on in seconds.
With Lambda, we made inference 1.7× faster while cutting peak GPU memory by more than half.
Watch the video here: youtube.com/watch?v=4O6HAXbe…
Read the full blog here: lambda.ai/blog/how-spreeai-t…
#SPREEAI #Lambda #tech
Lambda retweeted
so it turns out that @stephenbalaban (Co-Founder and CTO of @LambdaAPI) and i have been mutuals on x (best friends) for years so it was amazing to finally properly hang out.
we covered:
- what it was like to be so early
- the future biggest owners of compute
- is jalapeño green inspired by NVIDIA green?
- data center misinformation
- lambda the overnight success
- Imagine doing the AI thing in 2012
- We don’t name companies like “Tumblr” and “Dribbble” anymore
- @squirtle_says and @deepdishenjoyer mentioned
- What it felt like to be so early to AI
- They used to say that the first true AGIs will be stock market trading AIs
- People were talking about an AI bubble in 2017 and 2018
- Open source vs closed source
- What happens if the Chinese models start making money
- @elonmusk and truth seeking being the highest goal for grok
- The push to own hardware and AI companies becoming power companies
- The pushback against data centers
- The prosperity that will come from the data center buildout
Lambda retweeted
The state of GPUs and handing over the CEO role
@stephenbalaban gave me a masterclass on the current state of GPUs, and where the whole industry goes next.
From co-founding @LambdaAPI 14 years ago to handing over the CEO role this year, he's seen every phase of this industry up close - probably more than any other founder out there?
In our latest podcast episode, @stephenbalaban shares:
1. The current state of GPUs
2. 2026: is this the breakthrough year?
3. Starting a company with your brother
4. Stepping down as CEO
5. Not raising from traditional VCs
6. GPUs becoming an asset class
7. How many AI models will there be?
8. Partnering with Jensen Huang
And much more.
Thanks so much for coming on the show @stephenbalaban!
Available everywhere you listen to podcasts.
(link below)
Lambda retweeted
🎙️Full tech report on how to actually achieve SovereignAI, from data to model training, values, infrastructure, to large-scale deep research.
Bridging up to 7 months of Frontier AI development with a $450k training Continual Learning run 🧠
📜 Paper: tinyurl.com/yk347xb
💻Model: tinyurl.com/2ju6z7dc
Partners: @imperialcollege, @datologyai, @LambdaAPI
Lambda retweeted
This week in ML:
1) Memory is up 500% in a year;
2) Ordinary WiFi can identify you with near-perfectly;
3) A model that reasons in latent space and updates its memory mid-inference
More at @LambdaAPI 's news page: news.lambda.ai/
Lambda retweeted
okay this is beautiful. @Alibaba_Qwen Qwen3.8-27B-FP8 on one @NVIDIA GH200 at @LambdaAPI: 10 real requests streaming together, reasoning + final answers visible, 16K max output each, 262K context on vLLM. first streamed events landed within 10ms; every request finished normally.
Lambda retweeted
Introducing Toast 1, our first specialised search agent.
Toast 1 sets a new Pareto frontier for agentic search models.
Frontier search quality, across all domains, 12x faster, at 1/10th of the price.
Lambda retweeted
We've just made a major finding for SovereignAI 🚨:
Take any open-weight model (here: Qwen3.5-397B), apply our π-shaped Continual Learning, and own a genuine frontier model competitive with Opus 4.8 for ~$450k in compute.
Full tech report + open-source models coming soon!
