Episode Details

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We try to stay close to the inference providers as part of our coverage, as our podcasts with Together AI and Replicate will attest:

However one of the most notable pull quotes from our very well received Braintrust episode was his opinion that open source model adoption has NOT gone very well and is actually declining in relative market share terms (it is of course increasing in absolute terms):

Today’s guest, Lin Qiao, would wholly disagree. Her team of Pytorch/GPU experts are wholly dedicated toward helping you serve and finetune the full stack of open source models from Meta and others, across all modalities (Text, Audio, Image, Embedding, Vision-understanding), helping customers like Cursor and Hubspot scale up open source model inference both rapidly and affordably.

Fireworks has emerged after its successive funding rounds with top tier VCs as one of the leaders of the Compound AI movement, a term first coined by the Databricks/Mosaic gang at Berkeley AI and adapted as “Composite AI” by Gartner:

Replicating o1

We are the first podcast to discuss Fireworks’ f1, their proprietary replication of OpenAI’s o1. This has become a surprisingly hot area of competition in the past week as both Nous Forge and Deepseek r1 have launched competitive models.

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Timestamps

* 00:00:00 Introductions

* 00:02:08 Pre-history of Fireworks and PyTorch at Meta

* 00:09:49 Product Strategy: From Framework to Model Library

* 00:13:01 Compound AI Concept and Industry Dynamics

* 00:20:07 Fireworks' Distributed Inference Engine

* 00:22:58 OSS Model Support and Competitive Strategy

* 00:29:46 Declarative System Approach in AI

* 00:31:00 Can OSS replicate o1?

* 00:36:51 Fireworks f1

* 00:41:03 Collaboration with Cursor and Speculative Decoding

* 00:46:44 Fireworks quantization (and drama aro