This leaves a few questions: Why isn’t ASML taking ~70% margin like $NVDA or $MU? These machine’s manufacturing is artisan-like and are not scalable commodities like DRAM or even GPUs, but as an SPOF monopoly why not take advantage of this unprecedented spend by hyperscalers?
If these estimates from McKinsey hold true, we will be spending approx $7T for ~216 GW of incremental compute by 2030. For us to keep pace with this unprecedented buildout, walking down the full supply chain picture gets pretty insane
The margin of error on these machines is quite thin, and as advanced packagers like TSMC are forced to upgrade to newer machines, I believe these fab machine players will experience a similar crunch memory players are seeing today, but with much much stronger pricing power.
Introducing the Agent Arena by 🦍 Gorilla X LMSYS Chatbot Arena 🎯
How do different agents stack up in tasks like search, finance, RAG, and beyond? Which model is the most effective for agentic tasks? What tools do users prefer? Explore these questions and more!
✏️Blog: https://t.co/TUSVjqicps
🏟️Arena: https://t.co/ADrrmAs6T2
📊Leaderboard: https://t.co/dKVVr1bUKT
⚱️2k pair-wise battles dataset: https://t.co/chHAX5GiUW
❓What model, framework, or tools do users prefer? Which agents excel at financial and numerical analysis? What are the best agents to find the needle in the haystack in massive corpuses of data? Which agents are best integrated with online platforms (Gmail, Yelp, etc)? Agents = LLMs + Tools + Frameworks. With Agent Arena, you can compare combinations of large language models, tools (like code interpreters and APIs), and frameworks (including LangChain, LlamaIndex, CrewAI) to find the best agentic-mix for your needs. With a novel ranking system, we evaluate agents based on their performance in real-time head-to-head tasks, tracking the strengths of individual components, and combined. This provides deeper insights into specific use-cases and allows users to see which agent performs best for their needs.
In a world of crowd-sourced evaluations, who evaluates the crowd? With Prompt-Hub, users can publish, upvote, and explore prompts used for agent evaluations, creating a collaborative space for the community.
From the Agent Arena team of @NithikYekollu, @arth_bohra, Kai Wen, Sai Kolasani, @infwinston, @ml_angelopoulos, @profjoeyg, Ion Stoica, @shishirpatil_
Come see which agents and models rise to the top! 🚀