Neural Magic

Exciting insights into Neural Magic leadership! Mark Kurtz, CTO of Neural Magic, shares his journey to a successful AI career, the importance of hands-on expertise, understanding AI’s limitations, and the latest advancements in neural network efficiency. He also highlights the power of mentorship and open-source projects in democratizing AI education. Catch his key takeaways on creating impactful AI solutions: https://lnkd.in/eBdZyUYe #AI #CareerGrowth #MachineLearning #TechLeadership #AIInnovation

Ever wondered how your LLM deployments perform under real-world conditions? Curious if there’s a better hardware configuration to optimize your model’s performance? Or what the financial impact of different deployment strategies could be, and how to make smarter decisions to reduce costs? We've been there too, and that's why our team at Neural Magic developed GuideLLM – an open-source tool that helps you answer all these questions and more! Feel free to check it out at https://lnkd.in/dNnmwG6r

Excited to be at the #AIConf2024! It’s packed with innovative solutions and talks. Come by and say hello to our team!

?? The recording from last week's vLLM office hours is ready! In this session, Tyler Michael Smith shared how to leverage NVIDIA CUTLASS for high-performance inference in #vLLM. Michael Goin then covered the exciting updates in vLLM v0.6.0 that led to a 2.7x throughput increase and a 5x latency improvement. Watch the recording: https://lnkd.in/eq5RWA6K View the slides: https://lnkd.in/e9kPK4QA Explore and join our future bi-weekly vLLM office hours: https://lnkd.in/euF8m73q

Calling all vLLM users and contributors: There's a new track dedicated to vLLM at Ray Summit. Great talks from companies including Apple, Uber, Handshake, Databricks, Hinge, including vLLM contributor talks on speedups from quantization (Robert Shaw Michael Goin) and multi-modality (Roger Wang). Conference runs from Sep 30-Oct 2. Reach out if you have question :) #RaySummit2024 https://lnkd.in/g8ZhnkyY

Excited to attend the AI Conference at Pier 27 in San Francisco this week, September 10-11. Don’t miss Nir Shavit, MIT Professor and our Co-Founder at Neural Magic, as he presents "The Role of Sparsity in ML" at 9:05AM on Wednesday in the City View room. Nir will explore how sparsity in neural networks can improve efficiency without impacting accuracy. A must-see talk on using parallel algorithms to unlock the full potential of sparsity in machine learning. Looking forward to connecting with fellow AI innovators and practitioners. Let us know you are attending by commenting on this post - we’d love to meet up! #AIConf2024 #MLInnovation https://aiconference.com

Our team open-sourced GuideLLM, a tool for evaluating and enhancing LLM deployments! ?? GuideLLM simulates real-world inference to gauge performance, resource needs, and cost implications of deploying LLMs across different hardware configurations. Key GuideLLM features and benefits include: - Performance Evaluation: Test LLMs under real-world conditions; - Resource Optimization: Find the best hardware setups for effective inference; - Cost Efficiency: Optimize resources and cut expenses; - Scalability Testing: Ensure models handle high user loads. And this is just the beginning! We're actively enhancing GuideLLM with an intuitive UI and new features like deployment cost analysis and accuracy evaluation. Your feedback is crucial as we continue to push AI efficiency. Explore the GitHub repo and share your thoughts! https://lnkd.in/gUWMfhGE

Happening this Thursday! Join our vLLM office hours for the latest vLLM updates, an in-depth session on NVIDIA CUTLASS, and an interactive Q&A/feedback session: https://hubs.li/Q02Nr3W-0

GuideLLM Released by Neural Magic: A Powerful Tool for Evaluating and Optimizing the Deployment of Large Language Models (LLMs) GuideLLM is a comprehensive solution that helps users gauge the performance, resource needs, and cost implications of deploying large language models on various hardware configurations. By simulating real-world inference workloads, GuideLLM enables users to ensure that their LLM deployments are efficient and scalable without compromising service quality. This tool is particularly valuable for organizations looking to deploy LLMs in production environments where performance and cost are critical factors. Key Features of GuideLLM GuideLLM offers several key features that make it an indispensable tool for optimizing LLM deployments: ?? Performance Evaluation:?GuideLLM allows users to analyze the performance of their LLMs under different load scenarios. This feature ensures the deployed models meet the desired service level objectives (SLOs), even under high demand. ?? Resource Optimization:?By evaluating different hardware configurations, GuideLLM helps users determine the most suitable setup for running their models effectively. This leads to optimized resource utilization and potentially significant cost savings. ?? Cost Estimation:?Understanding the financial impact of various deployment strategies is crucial for making informed decisions. GuideLLM gives users insights into the cost implications of different configurations, enabling them to minimize expenses while maintaining high performance. ?? Scalability Testing:?GuideLLM can simulate scaling scenarios to handle large numbers of concurrent users. This feature is essential for ensuring the deployment can scale without performance degradation, which is critical for applications that experience variable traffic loads..... Read our full take on this: https://lnkd.in/gsGBGjpj GitHub: https://lnkd.in/gfQeCA-x Neural Magic Mark Kurtz Sa?a Zelenovi? Alex Matveev Nicole Kim Robert Shaw Brian Stevens #opensource #ai #llms

?? Join the Future of Efficient LLM Deployments with GuideLLM! ?? Exciting news! With Neural Magic, we're thrilled to introduce GuideLLM—an open-source tool that simplifies evaluating and optimizing LLM deployments for everyone. Whether you're developing chatbots, summarization tools, or any other LLM application, GuideLLM offers crucial insights to streamline your development process. Key features and benefits: - Performance Evaluation: Ensure your LLMs perform under real-world conditions. - Cost Efficiency: Optimize deployments to save resources and cut expenses. - Scalability Testing: Get your models ready to handle high user loads. We're actively enhancing GuideLLM with an intuitive UI and more features like accuracy evaluation. Your feedback and contributions are vital as we push the boundaries and ensure its benefits reach every user and company. Explore the GitHub repository, test it out, and join us in this journey of innovation together! Check out the project here: https://lnkd.in/ef8MSJWQ #opensource #efficientai #llms #community #llama31