Micron Shared Memory Architecture Accelerates AI Inference

The new architecture utilizes CXL 2.0 to pool GPU cache memory, achieving 2.8 times higher throughput in inference benchmarks.

Updated on Oct. 26, 2026 in Semiconductors

Micron Shared Memory Architecture Accelerates AI Inference

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Micron has announced the Great Abaco system architecture, which uses CXL 2.0 to create a shared memory pool for GPU clusters. This research-stage development targets AI inference bottlenecks by allowing GPUs to access shared KV cache memory, which has been demonstrated to improve throughput by 2.8 times in testing.

Why it matters

Scaling Large Language Model inference is limited by memory bottlenecks on individual GPUs, forcing redundant computations. A shared memory architecture addresses this by allowing clusters to reuse previously computed KV cache entries across devices, significantly accelerating time-to-token metrics.

In testing at Pacific Northwest National Laboratory, the architecture achieved CXL memory performance within 99 percent of host DRAM, supporting up to 160TB of DDR5 memory per rack. These benchmarks demonstrate 2.9 times faster time to first token using Llama3-70B with 90 percent shared prefixes.

The players

Micron

A global leader in semiconductor memory and storage solutions known for its work in HBM, DDR5, and CXL-based memory expansion technologies.

Pacific Northwest National Laboratory

A U.S. Department of Energy laboratory that conducts research in high-performance computing, data science, and energy efficiency.

Liqid

A developer of composable disaggregated infrastructure that enables the pooling and dynamic allocation of GPU and memory resources.

The details

The Great Abaco architecture functions by offloading Key-Value (KV) cache entries into a shared CXL 2.0 pool rather than local GPU memory. To manage this, the system employs a sequence-hash-based cache discovery process that tracks cache entries across different GPUs. Data is moved between the CXL pool and GPU memory via an asynchronous transfer engine, which prevents the system scheduler from stalling while waiting for data retrieval.

October 26, 2026: Micron details the Great Abaco system architecture.

The Tech Race

This development represents a shift toward composable infrastructure designed to mitigate the memory wall currently limiting LLM inference performance. It builds upon the CXL 2.0 specification to compete with traditional static memory allocations in massive HPC clusters.

This architecture is currently in the research phase and is not yet available for commercial deployment in private data centers. Once integrated into production HPC environments, it will primarily benefit large-scale AI service providers looking to reduce inference latency and hardware costs.

The takeaway

The Great Abaco system signals a transition toward disaggregated memory pools as a primary solution for AI scaling bottlenecks. Industry observers should watch for forthcoming benchmarks from commercial CXL-enabled hardware deployments as providers attempt to replicate these performance gains outside of laboratory settings.