A comprehensive guide to memory-efficient large language model serving using CXL-based intelligent memory endpoints with hardware-accelerated cache management.
High-level overview of the memory wall problem in LLM inference and the proposed intelligent endpoint architecture solution.
The von Neumann bottleneck, why KV-cache matters for LLM serving, and the scope of this technical documentation.
LLM inference fundamentals, the bandwidth-compute gap, and limitations of current approaches like vLLM and tensor parallelism.
Core architecture concepts including memory controller offloading, CXL 3.0, and system topology for single and multi-node deployments.
Two-tier cache model analysis, latency formulas, and the 65× improvement over traditional PCIe paths.
CXL switch topology for linear bandwidth scaling, layer prefetch strategies, and practical bandwidth calculations.
Offloading tokenization, image processing, and format conversion to ARM cores in endpoints for 5-10× latency reduction.
Per-head tracking, EMA-based attention scoring, RoPE-aware prefetch, and the eviction priority function achieving 97% hit rates.
Handling Mixture-of-Experts models with routing histograms, activation tracking, and adaptive caching strategies.
Memory mapping, hint interfaces, driver-to-firmware translation, and fault handling for seamless GPU access.
Analysis of commercial CXL products, software frameworks, recent research, and competitive differentiation.
TTFT improvements, compute vs IO-bound analysis, continuous batching impact, and asymptotic speedup models.
Hardware requirements, software stack, driver modifications, firmware development, and deployment scenarios.
Summary of contributions, key takeaways, and future research directions for next-generation LLM infrastructure.