TRTLM

The Rise of Open Models in the Enterprise — Amir Haghighat, Baseten

Jul 24, 2025 · 16:50

Amir Haghighat, CTO of Baseten, argues that enterprises are increasingly moving from closed frontier models like OpenAI and Anthropic toward open source models, driven by four specific cracks in the assumption that closed models will work indefinitely: quality for specialized tasks (e.g., medical document extraction), latency requirements (especially for voice), unit economics ballooning from agentic use cases where a single user action triggers 50 inference calls, and the desire for competitive differentiation. Drawing on conversations with over 100 enterprises, he explains that while most started with dedicated deployments on Azure/AWS for toying around in 2023, by 2024 about 40-50 had production use cases, and in 2025 the shift accelerated. However, adopting open models forces enterprises to build inference infrastructure, facing challenges like speculative decoding, prefix caching, guaranteeing four-nines reliability with hardware failures and VLM crashes, and scaling replicas—with one Fortune 50 soft drink company reporting an eight-minute spin-up time. Haghighat concludes by contrasting the simple API-call world with the complexities of mission-critical inference, where…

The GenAI Maturity Curve or You Probably Don't Need Fine Tuning: Kyle Corbitt

Feb 9, 2025 · 18:03

Kyle Corbitt, CEO of OpenPipe, argues that most teams don't yet need fine-tuning and should start with prompted models like GPT-4. He presents a GenAI maturity curve where the trigger to fine-tune is when you hit constraints on cost, latency, or quality consistency—for example, if GPT-4 is 80-90% correct but inconsistent on the last 10-20%. Fine-tuning shifts the paradigm frontier outward, enabling models like fine-tuned LLaMA 38B to outperform GPT-4 at 1/25th the cost. The process has four steps: capture production logs to know your input distribution, prepare high-quality data (using GPT-4 outputs or iterative labeling), train with one-click tools, and evaluate with inner-loop (LLM-as-judge) and outer-loop (business metrics) evals. OpenPipe and other providers make deployment trivial via OpenAI-compatible APIs. The talk delivers a concrete decision framework and a walkthrough so any engineer can fine-tune in under an hour.