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Programmable Data Centers: SF Compute CEO Evan Conrad on Physical Delivery of GPU Contracts and the Independent Compute Market

SF Compute is expanding from subleasing GPU clusters to building its own cloud operations capability, in order to achieve physical delivery of GPU contracts—buyers receive usable compute rather than cash settled against an index. CEO Evan Conrad believes that companies with large hardware or inference businesses find it difficult to operate a neutral exchange, and therefore NVIDIA cannot build this market itself. SF Compute makes compute tradable during technology iteration cycles by matching orders with different constraint conditions, and supports on-demand partitioning of InfiniBand network fabrics.

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Daytona raises a $60 million Series B at a $1 billion valuation

Daytona, which provides cloud sandboxes for AI agents, has raised a $60 million Series B at a $1 billion valuation, led by FirstMark, bringing its total funding to about $91 million. Its customers include Anthropic, OpenAI, Nvidia, Cursor, Cognition, Meta, and Cohere, and CEO Burazin said the company's revenue run rate grew 25-fold between January and September. Daytona says it can provision a new sandbox for an agent within 60 milliseconds by pre-distributing snapshots of configured machines to servers' physical disks.

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AMD: Real-time control and reliability are the key to physical AI

AMD executive Salil Raje proposed that performance evaluation for physical AI should shift from raw compute power to real-time control and system-level reliability. AMD builds heterogeneous architectures with Kria AI SOM, Versal, Zynq, and Spartan FPGAs; in third-party tests, Kria AI SOM achieved inference times under 0.1 seconds and supported 8,000 control decisions per second; in open navigation benchmarks, the Ryzen AI Embedded configuration delivered control-timing reliability up to 3.4 times that of Nvidia Jetson T5000, with 1.6 times more available compute headroom.

DeepSeek Open-Sources High-Performance Inference Framework DeepInfer

DeepSeek has open-sourced DeepInfer, the high-performance inference framework supporting its online services, under the Apache 2.0 license. This release focuses on offline inference, is built primarily on vLLM, and draws on SGLang and TensorRT-LLM. It integrates previously separately open-sourced kernels such as FlashMLA, DeepGEMM, DeepEP, and EPLB into a complete engine, filling in system layers such as scheduling, parallelism, and speculative decoding, while supporting NVIDIA SM100 GPUs and Huawei Ascend 950 NPUs. The original article also breaks down designs including DSpark speculative decoding, YoCo SWA bounded replay, asynchronous scheduling, batch-invariant deterministic inference, and regex/JSON schema/EBNF structured output.

Anthropic in talks for a second $45 billion compute deal with Nscale, West Virginia reservation doubles to 920 MW

Anthropic is in talks with Nscale for a second $45 billion compute agreement that would double its West Virginia reservation to 920 MW, adding another 460 MW building at the Monarch Compute Campus running Nvidia's Vera Rubin chips; Anthropic also holds a right of first refusal on the campus's expected third building. The first contract was signed in August, a six-year 460 MW lease with delivery starting at the end of 2027, averaging about $7.5 billion a year, or roughly $16.3 million per MW. Nscale's IPO filing lists the agreement with Anthropic at a value of up to about $44.6 billion, and the value of the second deal depends on whether the first Monarch building and its Vera Rubin racks can be powered on schedule by the end of 2027.

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Nebius commits $10 million to support European AI developers

Nebius's AI Builder Program will expand to Europe, offering a total of $10 million in support spanning compute grants, capital, and community programs. The compute grants target areas such as open models, coding agents, post-training, and evals; in addition, the program offers connections with European VCs such as Accel, as well as hackathons and demo days co-hosted with communities such as NVIDIA and AI Tinkerers.

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Laplace Intelligence conducts pre-deployment red team testing for robots and autonomous vehicles

Laplace Intelligence, incubated at the University of Michigan, is building automated red team testing for robots and physical AI. It uses high-fidelity simulation environments post-trained on Nvidia Cosmos plus AI agents to automatically design corner cases, finding and fixing problems before deployment. The company has already generated revenue and is slightly profitable; its customers include autonomous trucking company Bot Auto and a major Japanese automaker, and it is running a free smart city pilot with Nvidia and T-Mobile in San Jose, California.