New performance demonstrations show how better memory management can dramatically reduce AI response delays and help expensive computing hardware handle more work.

WHAT’S HAPPENING

The race to build faster and more affordable artificial intelligence may depend on more than purchasing powerful computer chips.

A technical presentation published by InfoQ on October 27, 2026, highlights how improved memory management can help AI systems deliver faster responses while making better use of existing computing equipment.

The presentation, Serving LLMs at Scale: The Hidden KV Cache Advantage, was delivered by Khawaja Shams, co-founder and CEO of Momento, at QCon AI Boston.

It focuses on a technology called key-value caching, commonly known as KV caching.

When an AI model processes a question or conversation, it performs calculations to understand the information provided.

KV caching allows certain intermediate calculations to be stored and reused rather than repeatedly generated when processing the same conversation or a shared prompt prefix.

Managing that stored information efficiently can reduce unnecessary computing work.

In one demonstration involving a 4-billion-parameter AI model running on an Amazon Web Services instance, adding 16 gigabytes of RAM reduced the reported 99th-percentile response latency from approximately 38.9 seconds to 3.5 seconds at the same workload.

That represents roughly a 91% reduction in the measured delay for the slowest one percent of requests.

The presentation also demonstrated how additional memory could support more simultaneous AI sessions.

These results were obtained under specific testing conditions and should not be interpreted as guaranteed improvements for every AI application.

The important finding is that better management of information already processed by an AI model can significantly improve performance without necessarily requiring more expensive graphics processors.

WHY IT MATTERS

Artificial intelligence has become an expensive computing business.

Companies operating large AI systems must pay for specialized processors, data centers, electricity, networking, and cloud infrastructure.

As more businesses introduce AI assistants and autonomous agents, the number of requests these systems must handle continues to grow.

This creates pressure to improve performance while controlling costs.

Much of the industry’s attention has focused on developing more powerful chips and constructing larger data centers.

But purchasing additional hardware is not the only way to increase computing capacity.

A system that uses existing equipment more efficiently can potentially serve more customers at a lower cost per request.

KV caching is particularly relevant because AI conversations frequently contain information that has already been processed.

For example, a business AI assistant may repeatedly receive the same operating instructions, company information, and conversation history.

Reusing compatible cached calculations can reduce the need to process those portions again.

The benefits depend on whether the requests share reusable information and whether enough memory is available to store it.

Memory also creates costs of its own.

Additional RAM, storage, and network transfers must be weighed against the computing resources they save.

The commercial objective is therefore not simply to create larger caches.

It is to determine which information should be retained, where it should be stored, and when reusing it delivers measurable savings.

WHO BENEFITS

• AI infrastructure providers: Can potentially increase computing efficiency and serve more requests using existing equipment.

• Cloud computing companies: May develop additional services around AI memory management, workload routing, and caching.

• Businesses operating AI assistants: Could benefit from faster responses and lower underlying computing costs.

• AI software developers: Gain opportunities to optimize applications without relying exclusively on newer hardware.

• Companies providing AI inference technology: May see increasing demand for systems that improve performance and reduce processing expenses.

WHO LOSES

• AI operators with inefficient infrastructure: Could face higher computing costs than competitors using better memory and workload management.

• Companies relying exclusively on hardware expansion: May spend more than necessary if software optimization could address some performance limitations.

• Technology providers unable to improve operating efficiency: Could experience pricing pressure as competitors lower their cost per AI request.

• Businesses overlooking AI usage costs: May find that growing demand reduces profitability when infrastructure expenses are not carefully managed.

WHAT HAPPENS NEXT

The next stage of AI infrastructure development will increasingly focus on how efficiently computing resources are used.

Several approaches are attracting attention.

Smarter caching: Retaining frequently reused calculations to avoid unnecessary processing.

Improved memory allocation: Making better decisions about which information remains in fast, expensive memory and which can be stored elsewhere.

Intelligent request routing: Directing AI workloads toward servers best positioned to process them efficiently.

Specialized processing systems: Separating different stages of AI inference to improve resource utilization.

Lower operating expenses: Measuring whether performance improvements actually reduce the cost of delivering reliable AI services.

The business implications could extend throughout the AI industry.

Lower inference costs may allow providers to offer more competitive pricing, support greater customer demand, or improve operating margins.

Companies deploying large numbers of autonomous agents could also benefit because recurring AI activities can generate substantial processing expenses.

However, not every workload will achieve the dramatic improvements demonstrated in the presentation.

Actual results will depend on model size, conversation structure, memory availability, hardware configuration, and system design.

The strongest competitive advantages will come from improvements that can be demonstrated consistently across real operating environments.

The larger shift is clear: The economics of artificial intelligence will not be determined by computing power alone. As AI adoption expands, companies that use memory, software, and existing hardware more efficiently may gain significant cost and performance advantages. The next major improvement in AI affordability could come not from buying more chips, but from making better use of the ones already installed.