Breaking the Memory Wall in the Age of Inference

2026-02-12 · Source: The Data Exchange · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Emerging Technologies & Innovation · Depth: Advanced, long

Summary

Sid Sheth, founder and CEO of d-matrix, discusses the company's strategy for AI inference hardware, specifically addressing the memory bottleneck. d-matrix develops SRAM-based inference accelerators utilizing Digital In-Memory Compute (DIMC) technology, optimized for low-latency data center applications rather than high-throughput workloads. Sheth highlights the limitations of High Bandwidth Memory (HBM) for mainstream inference due to its cost, energy consumption, and insufficient speed for modern AI. The discussion details the pre-fill (compute-intensive) and decode (memory-intensive) phases of LLM inference, explaining how DIMC minimizes data movement by integrating compute directly into SRAM cells, thereby enhancing efficiency and speed. d-matrix's hardware supports up to 256GB on a single card and 10 terabytes in a rack, capable of running 100-billion-parameter models entirely from SRAM in one rack, targeting hyperscalers and neo-clouds.

Key takeaway

For MLOps Engineers deploying large language models, understanding the memory bottleneck and the distinction between pre-fill and decode phases is critical. Your choice of inference hardware should prioritize memory-centric architectures like d-matrix's DIMC to achieve the low latency required for interactive and agentic AI applications, especially as models scale to 100 billion parameters and beyond. Evaluate specialized accelerators against general-purpose GPUs based on your specific latency and throughput requirements.

Key insights

Integrating compute directly into SRAM cells via Digital In-Memory Compute (DIMC) effectively addresses AI inference memory bottlenecks.

Principles

• Inference efficiency prioritizes money, time, and energy.
• Memory-centric design is crucial for low-latency AI inference.
• Model growth necessitates external memory tiers.

Method

d-matrix augments SRAM cells with additional transistors to enable simultaneous data storage and multiplication, creating a compute+store fabric that minimizes data movement for faster, lower-energy inference.

In practice

• Use SRAM-based accelerators for low-latency inference.
• Consider LPDDR for external memory tiers in LLMs.
• Optimize for decode phase in generative AI for speed.

Topics

• Digital In-Memory Compute
• AI Inference Hardware
• Memory Bottlenecks
• LLM Inference
• SRAM Accelerators

Best for: MLOps Engineer, NLP Engineer, CTO, AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Data Exchange.