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Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 10:44

Enabling Disaggregated Multi-Stage MLLM Inference via GPU-Internal Scheduling and Resource Sharing

Published:Dec 19, 2025 13:40
1 min read
ArXiv

Analysis

This research paper from ArXiv focuses on improving the efficiency of Multi-Stage Large Language Model (MLLM) inference. It explores methods for disaggregating the inference process and optimizing resource utilization within GPUs. The core of the work likely revolves around scheduling and resource sharing techniques to enhance performance.
Reference

“The paper likely presents novel scheduling algorithms or resource allocation strategies tailored for MLLM inference.”

PermalinkArXiv
Research#Key-Value🔬 ResearchAnalyzed: Jan 10, 2026 10:11

FlexKV: Optimizing Key-Value Store Performance with Flexible Index Offloading

Published:Dec 18, 2025 04:03
1 min read
ArXiv

Analysis

This ArXiv paper likely presents a novel approach to improve the performance of memory-disaggregated key-value stores. It focuses on FlexKV, a technique employing flexible index offloading strategies, which could significantly benefit large-scale data management.
Reference

“The paper focuses on FlexKV, a flexible index offloading strategy.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 09:12

CXL-SpecKV: A Disaggregated FPGA Speculative KV-Cache for Datacenter LLM Serving

Published:Dec 11, 2025 15:40
1 min read
ArXiv

Analysis

This article introduces CXL-SpecKV, a system designed to improve the performance of Large Language Model (LLM) serving in datacenters. It leverages Field Programmable Gate Arrays (FPGAs) and a speculative KV-cache, likely aiming to reduce latency and improve throughput. The use of CXL (Compute Express Link) suggests an attempt to efficiently connect and share resources across different components. The focus on disaggregation implies a distributed architecture, potentially offering scalability and resource utilization benefits. The research is likely focused on optimizing the memory access patterns and caching strategies specific to LLM workloads.

Key Takeaways

    Reference

    “The article likely details the architecture, implementation, and performance evaluation of CXL-SpecKV, potentially comparing it to other KV-cache designs or serving frameworks.”

    PermalinkArXiv
    Research#llm📝 BlogAnalyzed: Dec 28, 2025 21:57

    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.
    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.”

    PermalinkPractical AI