KForge uses dual LLM agents for cross-platform kernel generation, reporting 2.12% throughput gain on NVIDIA B200 vs TensorRT-LLM and 5.13x geometric mean speedup on Intel Arc B580 vs PyTorch on 37 workloads.
Efficient and scalable agentic ai with heteroge- neous systems
7 Pith papers cite this work. Polarity classification is still indexing.
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KAIROS reduces power by 27% on average (up to 39.8%) for agentic AI inference by using long-lived context to jointly manage GPU frequency, concurrency, and request routing across instances.
Outlines a vision and key research challenges for scalable networks of autonomous AI agents drawing on multi-agent systems, networks, and security.
Agentic workloads with context caching become decode-dominated with high KV-cache reuse and show tool use shifting from early read/explore to later execute/write phases.
The paper analyzes CPU bottlenecks in agentic AI serving, selects representative workloads, and demonstrates that CPU-aware scheduling optimizations COMB and MAS can reduce P50 latency by up to 1.7x and total latency by up to 2.49x on two hardware systems.
A two-stage framework for domain-adapting multi-agent LLMs and optimizing their inference claims a 4.48x throughput gain with maintained performance on enterprise tasks.
GoodServe proposes a predict-and-rectify routing system for agentic LLM inferences on heterogeneous GPUs that improves goodput by up to 27.4%.
citing papers explorer
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KForge: LLM-Driven Cross-Platform Kernel Generation for AI Accelerators
KForge uses dual LLM agents for cross-platform kernel generation, reporting 2.12% throughput gain on NVIDIA B200 vs TensorRT-LLM and 5.13x geometric mean speedup on Intel Arc B580 vs PyTorch on 37 workloads.
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KAIROS: Stateful, Context-Aware Power-Efficient Agentic Inference Serving
KAIROS reduces power by 27% on average (up to 39.8%) for agentic AI inference by using long-lived context to jointly manage GPU frequency, concurrency, and request routing across instances.
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The Internet of Agentic AI: Communication, Coordination, and Collective Intelligence at Scale
Outlines a vision and key research challenges for scalable networks of autonomous AI agents drawing on multi-agent systems, networks, and security.
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Agentic AI Workload Characteristics
Agentic workloads with context caching become decode-dominated with high KV-cache reuse and show tool use shifting from early read/explore to later execute/write phases.
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Towards Understanding, Analyzing, and Optimizing Agentic AI Execution: A CPU-Centric Perspective
The paper analyzes CPU bottlenecks in agentic AI serving, selects representative workloads, and demonstrates that CPU-aware scheduling optimizations COMB and MAS can reduce P50 latency by up to 1.7x and total latency by up to 2.49x on two hardware systems.
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Towards Scalable Customization and Deployment of Multi-Agent Systems for Enterprise Applications
A two-stage framework for domain-adapting multi-agent LLMs and optimizing their inference claims a 4.48x throughput gain with maintained performance on enterprise tasks.
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GoodServe: Towards High-Goodput Serving of Agentic LLM Inferences over Heterogeneous Resources
GoodServe proposes a predict-and-rectify routing system for agentic LLM inferences on heterogeneous GPUs that improves goodput by up to 27.4%.