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able to the EFA NIC in the sidecar rack. MTIAv4’s SUE72 (featuring a 72-logical GPU with all to all switched scale-up size just like the VR200 NVL144) design similarly can lean on Meta internal inference workloads. Even emerging designs such as OpenAI’s chip with Broadcom stand to compete given they will be codesigned with Frontier Models in mind and with an internal workload as a backstop. Despite enjoying the benefit of internal demand, the MTIAv3 is excluded from this category due to its smal

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fivefold year-to-date to reach $5B annualized. To keep that trajectory, Anthropic is betting hard on Scaling Laws. While Dario’s startup draws fewer headlines than OpenAI, xAI and Meta Superintelligence, it isn’t shy about investment. AWS has well over a gigawatt of datacenter capacity in final stages of construction for its anchor customer. AWS is building datacenters faster than it ever has in its entire history. And there’s much more on the horizon. Trainium vs GPUs While Amazon’s AI Datacent

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ter for Llama 3 405B vs GPT3 175B at 8.8 Joules per token vs 3.6 Joules per token respectively. This means that for the same energy as the average US household consumes in a year, Meta can train 4.4B tokens on Llama3 405B on BF16. To train to convergence using 15T tokens, Meta would require an amount of energy equal to the annual consumption of an entire neighborhood of 3,400 US households. Next, we look at Llama3 70B training performance for different cluster sizes. As we increase the cluster s

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which smaller companies are already benefiting from the shift of monetizable queries away from search to AI. OpenAI is firmly knocking on the door of technology giants Google and Meta and even Amazon. Previous scares about AI have been focused on search query volume, not being replaced in the ad tech stack. ChatGPT can compete with dominant platforms for its place in the ecosystem, and to date this is push into purchasing is the most concrete example of OpenAI coming for advertising at large. I

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s silicidation. DFT-NEGF is then used to calculate contact resistance and current-voltage curves across the interface. For gate oxide design, the complex multi-layer work function metal stack is built using MLFF and simulated to check its structure and chemical composition. Dipole dopants can then be introduced and optimized with DFT, which also does electrostatic analysis to calculate key parameters such as effective work function, Schottky barrier height and equivalent oxide thickness. As we m

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and then complement it with AI functions. Bringing function to form⁠ One way I visualize this relationship between the form of traditional UI and the function of AI is through the metaphor of a ‘workbench’. Just as a carpenter’s workbench is familiar and purpose-built, providing an organized environment for tools and materials, a well-designed interface can create productive context for AI interactions. Rather than being a singular tool, the workbench serves as an environment that enhances the u

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der to model their use cases. Still, while normally the associated item will likely be a small string or a document ID, nothing prevents it from being something more complex, with metadata that can be filtered later (but I’ll resist). I just have the feeling that many use cases don’t really need complex server-side filtering, and can be modeled by pre-partitioning data. What I see with great interest is the addition of a potential STORE option, to store the result into a sorted set instead of re

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Lenovo) they’re too far out of scope: perhaps more importantly, as above, if a large sample of search queries is needed, neither of them have enough. One could consider Amazon and Meta here, but only Apple has both the money and the distribution: it can launch as the default on devices driving 30% of US search, giving it several times more query volume than Bing has ever seen. (Note that Apple has some search activity data already: as per the screenshots above, it sees the queries, and knows whi

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