World Model · podcast knowledge graph

Speculative Decoding and Efficient LLM Inference with Chris Lott - #717

2025-02-04 · 77 min · episode 717 · 14 entities

episode page audio

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    Speculative Decoding and Efficient LLM Inference with Chris Lott - #717
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  • → hosted by TWIML company
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Entities found in this episode

concepts 6

  • mentioned Science concept
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    Feed category: Science
  • mentioned Tech News concept
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  • discusses Science concept
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    Feed category: Science
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    Feed category: News
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    Feed category: Tech News
  • mentioned LLM concept
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    LLM

companys 4

  • mentioned TWIML company
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  • hosted by TWIML company
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    Feed author/publisher: TWIML

persons 3

  • mentioned Chris Lott person
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    Speculative Decoding and Efficient LLM Inference with Chris Lott - #717
  • mentioned Sam Charrington person
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    Feed author/publisher: Sam Charrington
  • hosted by Sam Charrington person
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    evidence rules-v4
    Feed author/publisher: Sam Charrington

podcasts 1

Episode description as stored
Today, we're joined by Chris Lott, senior director of engineering at Qualcomm AI Research to discuss accelerating large language model inference. We explore the challenges presented by the LLM encoding and decoding (aka generation) and how these interact with various hardware constraints such as FLOPS, memory footprint and memory bandwidth to limit key inference metrics such as time-to-first-token, tokens per second, and tokens per joule. We then dig into a variety of techniques that can be used to accelerate inference such as KV compression, quantization, pruning, speculative decoding, and leveraging small language models (SLMs). We also discuss future directions for enabling on-device agentic experiences such as parallel generation and software tools like Qualcomm AI Orchestrator. The complete show notes for this episode can be found at https://twimlai.com/go/717.