Dataflow Computing for AI Inference with Kunle Olukotun - #751
In this episode, we're joined by Kunle Olukotun, professor of electrical engineering and computer science at Stanford University and co-founder and chief technologist at Sambanova Systems, to discuss reconfigurable dataflow architectures for AI inference. Kunle explains the core idea of building computers that are dynamically configured to match the dataflow graph of an AI model, moving beyond the traditional instruction-fetch paradigm of CPUs and GPUs. We explore how this architecture is well-suited for LLM inference, reducing memory bandwidth bottlenecks and improving performance. Kunle reviews how this system also enables efficient multi-model serving and agentic workflows through its large, tiered memory and fast model-switching capabilities. Finally, we discuss his research into future dynamic reconfigurable architectures, and the use of AI agents to build compilers for new hardware.
The complete show notes for this episode can be found at https://twimlai.com/go/751.
Improving on a 30-year-old hardware architecture
SambaNova makes a full-stack AI platform and an “intelligent chip” capable of running models of up to five trillion parameters, allowing developers to run state-of-the-art open source models without the time-consuming work of tuning and modeling. See what developers are building with the tech.
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#228 Rodrigo Liang: How SambaNova Systems Is Disrupting AI Inference
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In this episode of the Eye on AI podcast, we explore the world of AI inference technology with Rodrigo Liang, co-founder and CEO of SambaNova Systems.
Rodrigo shares his journey from high-performance chip design to building SambaNova, a company revolutionizing how enterprises leverage AI through scalable, power-efficient solutions. We dive into SambaNova's groundbreaking achievements, including their record-breaking inference models, the Lama 405B and 70B, which deliver unparalleled speed and accuracy—all on a single rack consuming less than 10 kilowatts of power.
Throughout the conversation, Rodrigo highlights the seismic shift from AI training to inference, explaining why production AI is now about speed, efficiency, and real-time applications. He details SambaNova's approach to open-source models, modular deployment, and multi-tenancy, enabling enterprises to scale AI without costly infrastructure overhauls.
We also discuss the competitive landscape of AI hardware, the challenges of NVIDIA's dominance, and how SambaNova is paving the way for a new era of AI innovation. Rodrigo explains the critical importance of power efficiency and how SambaNova's technology is unlocking opportunities for enterprises to deploy private, secure AI systems on-premises and in the cloud.
Discover how SambaNova is redefining AI for enterprise adoption, enabling real-time AI, and setting new standards in efficiency and scalability.
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Redefining AI Hardware for Enterprise with SambaNova’s Rodrigo Liang
🚀 Discover the cutting-edge AI hardware development for enterprises in this episode of Gradient Dissent, featuring Rodrigo Liang, CEO of SambaNova Systems.
Rodrigo Liang’s journey from Oracle to founding SambaNova is a tale of innovation and determination. In this episode, Rodrigo discusses the importance of specialized hardware in unlocking AI's potential for Enterprise businesses and SambaNova's mission to deliver comprehensive AI solutions from chips to models.
Explore the critical insights on navigating the challenges of introducing AI to executives and the evolution of AI applications within large enterprises, and get a glimpse into the future of AI in the business world.
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Scaling BERT and GPT for Financial Services with Jennifer Glore - #561
Today we’re joined by Jennifer Glore, VP of customer engineering at SambaNova Systems. In our conversation with Jennifer, we discuss how, and why, Sambanova, who is primarily focused on building hardware to support machine learning applications, has built a GPT language model for the financial services industry. Jennifer shares her thoughts on the progress of industries like banking and finance, as well as other traditional organizations, in their attempts at using transformers and other models, and where they’ve begun to see success, as well as some of the hidden challenges that orgs run into that impede their progress. Finally, we explore their experience replicating the GPT-3 paper from a R&D perspective, how they’re addressing issues of predictability, controllability, governance, etc, and much more.
The complete show notes for this episode can be found at twimlai.com/go/561
Designing Computer Systems for Software with Kunle Olukotun - TWiML Talk #211
Today we’re joined by Kunle Olukotun, Professor in the department of EE and CS at Stanford University, and Chief Technologist at Sambanova Systems. Kunle was an invited speaker at NeurIPS this year, presenting on “Designing Computer Systems for Software 2.0.” In our conversation, we discuss various aspects of designing hardware systems for machine and deep learning, touching on multicore processor design, domain specific languages, and graph-based hardware. This was a fun one!