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for many tests, the actual top score may be unachievable because there are many errors in the test questions and measures are often reported in unusual ways. Every benchmark has flaws, but they are all trending the same way - up and to the right. The AIME is a hard math exam, GPQA tests scientific and legal knowledge, the MMLU is a general knowledge test, SWE-bench and LiveBench test coding, Terminal-Bench tests agentic ability. Data from Epoch AI . Despite these issues, all of these benchmarks,

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e-up size just like the VR200 NVL144) can lean on Anthropic’s demand to codesign and gain adoption for their inference workloads. There will be major time to market challenges for AWS for its VR 144 CPX due to the fact that their 1U compute tray is already tightly integrated and packed with 4 large Rubin GPU packages and 8 CPX GPU packages leaving no space to add AWS’s custom EFA NICs into the compute tray. Amazon does not want to use ConnectX-9. AWS LOVES EFA! We believe that they will continue

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yond the working recipe. It is hard because humans have inertia and seldom question basic assumptions - you just take them for granted without realizing they are assumptions, not laws. To explain inertia, suppose you invented one of the most successful evals in history based on human exams. It was an extremely bold idea in 2021, but 3 years later it’s saturated. What would you do? Most likely create a much harder exam. Or suppose you solved simply coding tasks. What would you do? Most likely fin

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esources are chiefly training compute, data, and inference compute. It appears that you can spend arbitrary amounts of money and get continuous and predictable gains; the scaling laws that predict this are accurate over many orders of magnitude. 2. The cost to use a given level of AI falls about 10x every 12 months, and lower prices lead to much more use. You can see this in the token cost from GPT-4 in early 2023 to GPT-4o in mid-2024, where the price per token dropped about 150x in that time p

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lopment of powerful AI, and would like to see more work on fleshing out and implementing these policies. ↩ For examples of recent work in this area, see the observational scaling laws described in Ruan, Maddison, and Hashimoto 2024 or the test-time compute scaling laws of Snell et al 2024 . ↩ In the same way that responsibly disclosing zero-day exploits does not mean immediately announcing the exploits as they are discovered, responsible disclosure does not necessarily mean immediately announcin

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ction applications (e.g., powering Amazon Rufus and LinkedIn AI features). Our bi-monthly meetups have become strategic gatherings for partnerships with industry leaders like IBM, AWS, and NVIDIA, marking our progress toward becoming the universal serving solution for the open-source AI ecosystem. Read on for more details about vLLM's 2024 achievements and 2025 roadmap! This blog is based on the 16th session of the bi-weekly vLLM Office Hours. Watch the recording here. Community Contributions an

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w era of heightened material prosperity across the planet; Scientific breakthroughs and new technologies and medicines will dramatically expand, as AI helps us further decode the laws of nature and harvest them for our benefit; The creative arts will enter a golden age, as AI-augmented artists, musicians, writers, and filmmakers gain the ability to realize their visions far faster and at greater scale than ever before; I even think AI is going to improve warfare, when it has to happen, by reduci

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