Scaling Agentic Inference Across Heterogeneous Compute with Zain Asgar - #757
Published:Dec 2, 2025 22:29
•1 min read
•Practical AI
Analysis
This article from Practical AI discusses Gimlet Labs' approach to optimizing AI inference for agentic applications. The core issue is the unsustainability of relying solely on high-end GPUs due to the increased token consumption of agents compared to traditional LLM applications. Gimlet's solution involves a heterogeneous approach, distributing workloads across various hardware types (H100s, older GPUs, and CPUs). The article highlights their three-layer architecture: workload disaggregation, a compilation layer, and a system using LLMs to optimize compute kernels. It also touches on networking complexities, precision trade-offs, and hardware-aware scheduling, indicating a focus on efficiency and cost-effectiveness in AI infrastructure.
Key Takeaways
- •Gimlet Labs is developing a heterogeneous AI inference solution to address the high token consumption of agentic applications.
- •Their approach involves disaggregating workloads across various hardware, including CPUs and older GPUs, to optimize unit economics.
- •The architecture includes a compilation layer and a system using LLMs to optimize compute kernels, demonstrating a focus on efficiency.
Reference
“Zain argues that the current industry standard of running all AI workloads on high-end GPUs is unsustainable for agents, which consume significantly more tokens than traditional LLM applications.”