
Expanding AI chip capabilities beyond Nvidia with Modular CEO Chris Lattner | E1808
This Week in StartupsWed Sep 13 2023
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Background of LLVM technology:
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LLVM is a compiler technology used in devices like phones, laptops, and consoles.
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It unified compute around CPUs by supporting multiple programming languages.
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The need for a unified AI stack:
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Machine learning lacks a solution like LLVM for hardware integration.
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Modular aims to build an AI stack that can be plugged into any hardware, simplifying deployment and distribution of AI models.
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Importance of software development in the AI space:
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Building AI models involves training, deploying, and distributing them into products.
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Existing ML systems focus on research and training, making the transition to production challenging.
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Challenges in deploying ML models:
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Deploying ML models requires addressing different problems than training.
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Fragmentation and complexity in existing systems make it difficult to integrate models into various platforms.
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Future of hardware and optimization:
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Advancements in wearables, personal computing, AR/VR technologies are leading to more specialized and customized hardware.
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Customized chips will play a significant role in future developments.
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Open-source community and RISC-V:
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RISC-V is an open-source instruction set architecture allowing anyone to create their own member of the RISC-V family.
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This innovation has led to new hardware designs but highlights the need for scalable software solutions.
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NVIDIA's dominance and partnership with Modular:
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NVIDIA succeeded through programmability and support for general compute on GPUs.
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Modular sees NVIDIA as an important partner due to complementary missions aimed at expanding the developer ecosystem.
AI wrapper debate:
- Vertical AI apps vs. relying on a single language model like GPT-3.
- Verticalized apps provide more specialized solutions compared to general-purpose models.
- Different applications require different approaches, both have value depending on the use case.
Transition from nonprofit to for-profit by OpenAI:
- Perceived as a money grab by some, while others understand the need for financial sustainability.
- Expecting companies to invest billions of dollars into free products is unrealistic.
How much faster are developers getting, in your estimation?:
- Developers have become significantly faster over time.
- Improved tools and technologies contribute to increased development speed.
Comparison with hardware transitions:
- Hardware transitions took a long time (e.g., two decades for PCs).
- Software-based platforms like Google and Uber took around 10 years to deploy widely.
- AI advancements may happen faster than hardware transitions due to lower manufacturing delays.
Challenges faced by startups:
- Startups often believe they can quickly build successful products based on existing technology.
- Intense competition arises if one startup can build something in a month, leading to the need for incremental progress and delivering value.
Modular's approach to tackling complexity:
- Modular aims to simplify the complex AI infrastructure landscape.
- They provide a unified solution that addresses fragmentation and infighting among different companies and groups.
- Mojo, their programming language, allows Python developers to scale code effectively.
Mojo's role in addressing Python limitations:
- Mojo enhances Python capabilities for high-performance tasks like AI and machine learning.
- It enables customization while maintaining compatibility with the existing Python ecosystem.
AI Engine as a drop-in replacement for TensorFlow and PyTorch:
- Modular offers the AI Engine, seamlessly replacing popular frameworks without code rewriting.
- This consolidates various point solutions used by enterprises.
Compatibility with different hardware platforms:
- Making Mojo compatible with each platform is challenging but crucial for scalability.
- Modular's expertise allows them to bring up new architectures quickly, achieving performance uplifts on Intel CPUs, AMD CPUs, and ARM-based cloud servers like Graviton.
Apple's involvement in AI:
- Apple focuses on client-side running of AI models on devices like laptops and phones.
- They optimize their hardware and software for this purpose, providing a seamless user experience.
Modular's hiring and company culture:
- Modular tackles a challenging technology problem in the AI infrastructure space.
- They emphasize building high-quality production-ready solutions rather than rushing from demo to demo.
- The company offers opportunities to impact millions of developers by enabling them to participate in AI development.