¿Podría ser "Data Center Fornicator" la canción del verano?

OpenAI posted a job opening for "Power Trading Lead", responsible for developing and executing hedging strategies for power and gas for data centers. First job like this I've seen them advertise.

[opens the portal to the godlike superintelligence that solves 87-year-old math problems and carries out autonomous cyberattacks] “how long peanut butter good in fridge”

You are far more dangerous to your startup than competitors are. A hundred times more startups die from poor execution by their founders than are killed by competitors.

SSDs have never been more valuable. Let us help you get the most from what you own. @VAST_Data vastdata.com/resources/solut….

The cafeteria lady at the SpaceX canteen realizing she became a millionaire after 10 years of selling coffee to Elon Musk

This cluster is powering one of the world‘s most popular AI coding and reasoning services. It’s a great milestone for @VAST_Data … and testament to our game-changing DASE architecture. Next stop, one trillion yottabytes!!!!!!!!

The shiny silver rack door for NERSC's upcoming Doudna is quite different from the Cray racks that NERSC historically had. Wonder how the rack graphics will work. End-of-row? (also shown: an 11-node VAST cluster in the second rack) #HPC Source: linkedin.com/posts/michael-l…

I've spent the past few weeks reading 100s of public data sources about AI development. I now believe that recursive self-improvement has a 60% chance of happening by the end of 2028. In other words, AI systems might soon be capable of building themselves.

Number of landmines accidentally delivered to an IKEA. (2020-2026)

real talk. what's an average token/FTE?

This isn't what we expected Carlos would admire about Alex... 😆 #F1

currently prepping content for the @VAST_Data Sales Kickoff + VAST FWD in a sensory depravation tank of a hotel room. found myself reminiscing about presentations of old... this is a throwback to SKO 2023. IYKYK

Look at these handsome fellows. @VAST_Data @WilliamsF1

According to @tomshardware , SSDs are now more valuable than gold (by the lb)! @VAST_Data probably 'reclaimed' a little under an Exabyte in the last year - mostly from other companies' data lake storage that doesn't scale well... but this was all before the supply chain crisis became what it is today. Talk to VAST, we can help turn one petabyte of space into two or even more petabytes by replacing inefficient SW with our innovative, scalable and hyper-efficient AI Operating System. tomshardware.com/pc-componen…

If you have teams attending WEF in Davos and want to have a strategic sync with @VAST_Data 's management team, DM me and let's see if we can set something up.

In the blink of an eye, AI storage explodes in capacity by 12,300% (see math below). This week, NVIDIA introduced a massive unlock to GPU efficiency: a new specialized AI storage architecture that extends context/tokens that are processed in HBM - and can now spill context down into shared NVMe storage. By saving context in a KV Cache, inference systems avoid the cost of context recomputing (for large context inference), lowering time-to-first-token by 20x or more. What people don't realize is that this is an altogether new data generator - and not only does the market need a new approach to storage speed and efficiency, but many (regulated) AI labs will still need enterprise data management capability which cannot be sacrificed for raw speed. NVIDIA calls this Inference Context Memory Storage (ICMS) Platform. We've been working with them for weeks now to pioneer a new way to configure VAST systems that provides ultimate efficiency, by embedding the core logic of VAST systems directly into a GPU machines BlueField DPU. **The 12x is no joke. I did the math today ** - A standard VAST system, minimally configured for a NCP (NVIDIA Cloud Partner), has roughly 1.3TB of data per every GPU in a GB200-class cluster. - When we add additional infrastructure for context memory extension, GPUs will require an additional 16TB as we step into the Vera Rubin era. 12.3x. Why @VAST_Data , you might ask? 1. our parallel DASE architecture allows us to embed VAST servers directly into each BlueField server. This not only reduces infrastructure requirements vs. conventional configurations where separate x86 servers were shared by GPU clients, it also changes the fundamental client:server paradigm... where for the first time every GPU client machine now has their own dedicated server. VAST's parallel Disaggregated, Shared-Everything architecture makes it possible to embed servers in each client without introducing cross-talk across VAST servers as would be the case for any other storage technology. Each server then connects directly to all of the cluster's SSDs, requiring a single zero-copy hop to get to all of the shared context- so any machine can retrieve context in real-time. The efficiency and scale of this architecture is unprecedented. 2. While we can get great performance by stripping down data services that run In BlueField, our embarrassingly-parallel architecture allows us to hang additional servers off the same fabric to provide optional background enterprise data management... bringing capabilities such as data protection, audit, encryption and up to 2:1 KVCache data reduction to a cluster that has an ultra-streamlined data path to the GPU. With VAST, AI labs don't have to choose... They can get performance and killer global data management features. This space is evolving right now... lots of room to invent. DM me to co-develop the future of accelerated inference systems with us. vastdata.com/blog/more-infer…