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Lambda

@LambdaAPI

The Superintelligence Cloud

San Francisco, CA Se unió Temmuz 2012
240 Siguiendo20K Seguidores
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Lambda
Lambda@LambdaAPI·
AI infrastructure is the largest industrial buildout of our lifetime. Lambda is assembling the leadership team to match the opportunity ahead. Today, Lambda welcomes global infrastructure operator Michel Combes as CEO and former AT&T Communications CEO John Donovan as Chairman of the Board. Co-founder Stephen Balaban takes on the CTO role full-time, shaping the technology that will define the next decade of AI compute. Read the exclusive from @business: bloomberg.com/news/articles/…
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Lambda@LambdaAPI·
Lambda, NVIDIA, and Kodiak are hosting AI After Dark. The talks end, and the real conversations start. June 6. Spots are limited. Save yours now: luma.com/in9oubm7
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Lambda@LambdaAPI·
Most agent frameworks are built around one cloud model. Swap in a local model, performance drops. @OpenJarvisAI fixes the harness, not the model. Result: 77% of the accuracy gap recovered, 800x lower cost per query, 4x lower latency. Built on Lambda. Open-sourced from Stanford.
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NVIDIA AI Infrastructure
NVIDIA AI Infrastructure@NVIDIAAIInfra·
📣 NVIDIA Blackwell sets a STAC-AI LANG6 record for LLM inference in quantitative research and algorithmic trading. Record performance on Llama 3.1, with the highest compute per watt and lowest token cost. See the results from @HPE, @LambdaAPI, and @supermicro. 👉 nvda.ws/42YJJRW
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Kodiak AI
Kodiak AI@KodiakRobotics·
We'll be at #CVPR2026. Find us at the @LambdaAPI booth. Saturday night: AI After Dark with @NVIDIA and Lambda. Drinks, food, and good conversation in Denver. June 6 | 6:30 to 9:30 PM | The Kitchen American Bistro RSVP: luma.com/in9oubm7
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Lambda@LambdaAPI·
We're at #CVPR2026 Denver, Booth 523 (June 3-7). Workshops on world models and human-AI learning. Two accepted papers. Live demos on 3D vision, embodied AI, and research agents.
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Robert Brooks IV
Robert Brooks IV@boborado·
Mega Bitter Lesson-pilled
The Deep View@theDeepView

⚙️ New podcast episode: Robert Brooks IV of Lambda In this episode of The Deep View Conversations, we talked with Brooks, Lambda's chief commercial officer, about the massive AI infrastructure buildout now underway. Lambda’s mission is to build supercomputers for superintelligence. But Brooks argues that the story is bigger than GPUs, data centers, and rising demand. It is about why compute is becoming one of the most strategically important resources in the AI economy, and why Lambda believes compute is not a commodity. The conversation goes deep on Lambda’s vision for democratizing AI, why the company invests in research, and how its experience building physical infrastructure shapes what it can offer AI labs, hyperscalers, enterprises, and researchers. Topics covered include: + Why Lambda thinks “one GPU per person” is achievable + The hidden complexity behind modern AI data centers + Why compute demand keeps surprising the industry + His $40,000 robot experiment and what it taught him about the future of work + How AI is changing the way leaders spend their time If you want to better understand the physical and economic foundations powering the AI boom, this conversation is worth your time. 📺 Watch on YouTube: youtube.com/watch?v=BvnQb0… 🎧 Listen in your favorite podcast player: tdv.transistor.fm/episodes/44-ho… Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology. And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

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Lambda@LambdaAPI·
DeepSeek V4 was the most anticipated open-source model release of the year. It also landed in the most competitive landscape yet. A few months ago, a 1.6T-parameter open model under MIT license would’ve dominated the news cycle. Now it shares the headline with infrastructure announcements, closed-model updates, and younger labs reaching new heights.
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Lambda@LambdaAPI·
The real story in V4 is the engineering, not the capability. A hybrid attention mechanism combining Compressed Sparse Attention and Heavily Compressed Attention cuts single-token inference FLOPs and KV-cache memory at long context. Cost-to-serve drops over 10x versus v3.2. The 1M context window works on price. The capability uplift hasn't followed yet. This is also the floor. V4 is still in pre-release, just as V3 was. The full model is what the community is waiting for.
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NVIDIA
NVIDIA@nvidia·
@LambdaAPI is partnering with Hudson River Trading to power quantitative research and development using NVIDIA HGX B200 systems — the platform that delivers the highest compute per watt, lowest token cost, and longest useful life. Read the announcement: nvda.ws/4uo4d2F
Lambda@LambdaAPI

Hudson River Trading is partnering with Lambda to accelerate quantitative research and development. Lambda will help power @WeAreHRT's research roadmap with a full-stack architecture, including @nvidia HGX B200 systems, advanced networking, storage, orchestration, and uptime. Read more: lambda.ai/blog/lambda-pa…

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Lambda@LambdaAPI·
@TheZachMueller Post-prod should be light. Awesomeness is awesomeness.
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Zach Mueller
Zach Mueller@TheZachMueller·
Also goes without saying, but Lambda is being wonderfully amazing and supporting my decision to do this. Some of the topics from the last few cohorts will be blended and released for free on YouTube soon-ish (different vibe, you’ll see what I mean) from both our accounts together. You’ll still get me as it has been, just with a hair more professionalism and post production :)
Zach Mueller@TheZachMueller

Apparently 30+ of you agree. Things are slower come August/September, TBD. Would still be maven, cheaper, smaller, and with some lambda compute. But we’ll do something fun.

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Lambda@LambdaAPI·
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: lambda.ai/blog/lambda-ba…
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Lambda@LambdaAPI·
Hudson River Trading is partnering with Lambda to accelerate quantitative research and development. Lambda will help power @WeAreHRT's research roadmap with a full-stack architecture, including @nvidia HGX B200 systems, advanced networking, storage, orchestration, and uptime. Read more: lambda.ai/blog/lambda-pa…
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Lambda@LambdaAPI·
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 @stacresearch's STAC-AI LANG6 on @nvidia HGX B200. Read more: lambda.ai/blog/lambdas-n…
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