From data to decisions: Why SSD write cache matters to AI applications

Imagine you’re a busy office worker: Every time someone emails you — even for minor tasks — you must stop what you’re doing and respond immediately. These interruptions not only break your focus, but they also slow down your productivity and create chaos in your schedule. This constant disruption is what computers would experience without write caching.

SSD write caching provides the speed boost AI needs

Write caching has been a key feature in storage systems since the 1980s,¹ starting with hard disk drives (HDDs) and using methods like write-back and write-through caching in RAID configurations and enterprise systems. Write-back caching temporarily stores data in high-speed memory before writing it to the disk, a process that improves performance by reducing the time it takes to complete write operations.

With the advent of solid-state drives (SSDs) and modern NAND architectures, write caching has evolved significantly. It now plays a crucial role in reducing write amplification (when the drive ends up writing more data than the user originally requested, due to internal processes like data reorganization and garbage collection), maintaining high throughput under heavy input/output (I/O) loads, and supporting real-time data processing.

SSD dynamic write caching temporarily stores write operations in high-speed memory (typically DRAM or SLC cache) before committing them to slower NAND flash. This process allows for faster write acknowledgment, reduced latency and improved system responsiveness — key advantages in data-intensive environments like artificial intelligence (AI). Unlike static or fixed caching, dynamic write caching adjusts cache use in real time based on workload patterns, optimizing performance and endurance.

AI runs faster (and smarter) with write caching

Write caching optimizes AI for a number of use cases and benefits, including AI training, inference latency, large language model efficiency, and edge and distributed environments.

AI model training: AI training workloads require sustained throughput and minimal latency, especially for large-scale datasets and complex neural network architectures. Write caching enhances these workloads by speeding up data access and reducing latency, which minimizes I/O bottlenecks. This optimization is crucial for maintaining performance and scalability across different storage levels.

The Micron 9550 SSD, in tandem with NVIDIA® H100 GPUs, accelerated graph neural network training by 33%, thanks to a 60% increase in throughput. This increase also led to a 43% reduction in SSD energy consumption and 29% lower total system energy use.²

In Unet3D medical segmentation workloads (from MLPerf Storage benchmarks), the same SSD achieved a 5% performance improvement while consuming 32% less average power, equating to 35% lower SSD energy use.²

Inference latency optimization: Imagine asking a voice assistant a question and having to wait minutes for a response. That delay is often caused by slow data access. Reducing inference latency is crucial for the success of AI applications that require real-time responses. Whether it's conversational AI, fraud detection or autonomous decision-making, minimizing latency ensures timely and accurate outputs and enhances user experience and system reliability. SSD write cache plays a vital role in this process by accelerating data access, managing writes efficiently and optimizing system performance.

The GATI prediction-serving system integrated a learned caching layer, achieving up to a 7.69 times reduction in end-to-end inference latency for realistic AI workloads.³

Large language model efficiency: LLMs like GPT and LLaMA require high memory bandwidth to process vast amounts of data quickly and efficiently. However, running these models on commodity or memory-constrained hardware can be challenging — without fast storage, they can stumble. SSD write caching helps by temporarily storing frequently accessed data in high-speed memory, which reduces latency and makes inference feasible even on less powerful systems.

M2Cache, a mixed-precision, multilevel cache framework, leverages both DRAM and SSDs to manage massive model parameters, enabling scalable LLM inference with minimal performance degradation.⁴ 

Edge AI and distributed environments: In edge computing, write caching becomes even more essential due to hardware constraints and the need for localized inference. Edge devices — like smart cameras, thin and light laptops and onboard vehicle units — often have limited memory and processing power, making it challenging to handle large datasets and complex computations. Quick access to local data to make decisions on the spot is critical. Caching helps by temporarily storing frequently accessed data closer to the edge, reducing latency and improving the efficiency of real-time data processing and inference tasks.

Using Redis as a distributed cache with NVIDIA Triton Inference Server, inference throughput increased from 80 to 329 inferences per second, and latency decreased from 12,680 to 3,030 microseconds — a four times increase in throughput and four times decrease in latency.⁵

SSD write caching enables AI

From your smartphone to self-driving cars, you’ll find AI is everywhere — and it’s only as fast as the data it can access.

Write caching is pivotal for advancing AI; it ensures that models can scale efficiently and operate seamlessly. SSD write caching is essential for maintaining high I/O throughput, enabling real-time edge intelligence, enhancing energy efficiency and optimizing multi-agent system performance. By reducing bottlenecks, overcoming hardware limitations, lowering power consumption and providing rapid local cache access, SSD write caching is a key enabler for the next generation of responsive, efficient and scalable AI systems.

Learn more about Micron SSDs