Scaling Agentic Inference Across Heterogeneous Compute with Zain Asgar - #757

2025-12-02 · Source: The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Robotics & Autonomous Systems · Depth: Advanced, extended

Summary

Gimlet Labs, led by co-founder and CEO Zain Asgar, is addressing the unsustainable cost of AI inference, particularly for agentic AI workloads that consume significantly more tokens than traditional LLM applications. Gimlet's approach involves heterogeneous inference, disaggregating workloads across diverse hardware, from high-end H100s to older GPUs and CPUs, to optimize unit economics and performance. Their "three-layer cake" architecture includes workload disaggregation for optimal resource allocation, a compilation layer that maps models to specific hardware targets, and a novel system that uses LLMs to autonomously rewrite and optimize compute kernels. This system aims to improve efficiency and latency by avoiding round trips and orchestrating all components of an agentic system, including CPU compute and database calls, within a single framework. Gimlet Labs currently generates eight figures in revenue through self-hosted data center deployments and plans a Q1 launch for its usage-based cloud product.

Key takeaway

For AI Architects and CTOs managing large-scale AI inference, consider adopting heterogeneous hardware strategies to significantly reduce cost per token. Gimlet Labs' approach demonstrates that mixing high-end GPUs with older, lower-cost hardware, combined with advanced workload disaggregation and kernel optimization, can yield substantial TCO benefits without sacrificing performance. Evaluate solutions that provide fine-grained control over resource allocation and leverage automated optimization to maximize hardware utilization across your diverse infrastructure.

Key insights

Heterogeneous AI inference across diverse hardware optimizes cost and performance for token-intensive agentic workloads.

Principles

• Disaggregate workloads for optimal resource allocation.
• Automate kernel optimization using LLMs.
• Prioritize cost-per-token optimization.

Method

Gimlet's three-layer architecture disaggregates agent graphs, compiles workloads to target hardware, and autonomously optimizes compute kernels using an LLM-driven, hardware-in-the-loop system for efficiency and latency gains.

In practice

• Utilize older GPUs (e.g., Gaudi) with newer ones for TCO benefits.
• Partition models to assign critical pieces to high-end hardware.
• Employ dynamic resource allocation (DRA) for fine-grained GPU utilization.

Topics

• Heterogeneous AI Inference
• Agentic AI Workloads
• LLM-driven Kernel Optimization
• Workload Disaggregation
• Hardware-aware Scheduling

Best for: AI Architect, CTO, VP of Engineering/Data, Machine Learning Engineer, MLOps Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence).