In 2022, Berkeley PhD student Woosuk Kwon thought he could make Meta's new open-weight model, OPT-175B, run faster in two weeks. Four years and 3,000 contributors later, that project is @vllm_project, one of the largest open-source inference engines, running on ~500,000 GPUs at any given time. He and fellow researchers Simon Mo, Kaichao You, and Roger Wang borrowed "virtual memory" from operating systems, handing out memory only as it's needed so each GPU can juggle requests of wildly different sizes. (The "v" in vLLM is for virtual.) Their first user, Berkeley neighbor @arena, cut its GPU needs in half. Today Google, Amazon, and Meta all run vLLM in production. In January, the core maintainers founded @inferact and raised $150M to give vLLM a full-time team. They're hiring across eng, marketing, and devrel in SF and Singapore: @simon_mo_ @woosuk_k @KaichaoYou @rogerw0108

It's been 7 years from GPT-2 to GPT-6, and a lot has changed. The diagram below shows each generation and its key highlights, based on the public information we know. GPT-2 (2019): More like a prototype to validate the potential of the decoder-only transformer. This was a 1.5B-parameter model that only went through pre-training with next-token prediction. The trained model was only capable of continuing text, not answering questions. GPT-3 (2020): The same recipe as GPT-2, just much bigger and trained on more data to test how far scale alone could go. GPT-3 had 175B parameters, more than 100 times the size of GPT-2, and still only went through pre-training. The scale allowed it to show in-context learning capabilities. When shown some examples, the model could follow them. GPT-4 (2023): On top of pre-training, OpenAI fine-tuned it with RLHF (reinforcement learning from human feedback), so it was not just a completion model but could actually answer questions. It was also multimodal, meaning that it could process input images as well as text, and it handled up to 32K tokens of context. GPT-5 (2025): The first GPT generation with built-in reasoning. Given a prompt, tools, and an effort level, the trained model could generate reasoning tokens before the answer. Its context window grew to 400K tokens, with a router in ChatGPT to decide when a prompt needed thinking mode. GPT-6 (2026): This new model has a bigger context window (1.05M), supports async tool calling, and is generally safer. An interesting feature, called mid-turn steering, is that it can take a new instruction while it's thinking.

NETAPP $NTAP JUST REPORTED Q1 FY2027 EARNINGS - Revenue: $2.03B, beating expectations of $1.84B 🟢 - Non-GAAP EPS: $2.58, beating expectations of $2.12 🟢 - GAAP EPS: $1.88, +63% year over year - Billings: $2.06B, +36% - All-flash array revenue: $1.3B, +47%, a record - Public Cloud revenue: $206M, +28%, a record - GAAP operating margin: 23.9% - Non-GAAP operating margin: 31.9% - Free cash flow: $401M, -35% Q2 guidance: - Revenue: $2.025B-$2.175B - Non-GAAP EPS: $2.54-$2.64 Full-year FY2027 guidance, raised per the company: - Revenue: $7.975B-$8.225B - Non-GAAP EPS: $9.73-$10.03 CEO George Kurian said the company exceeded guidance on every metric. NetApp also acquired AI data infrastructure company DataPelago and declared a $0.52 dividend payable October 28.

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