NVIDIA introduced KVTC: an LLM cache compression technology that will make neural networks run 20x faster

# NVIDIA Unveiled KVTC: LLM Cache Compression Technology Will Accelerate Neural Networks by 20 Times

The artificial intelligence industry faces a paradox: the more powerful language models become, the slower they work. NVIDIA researchers discovered the source of this slowdown and proposed a radical solution. The new KVTC architecture allows compressing the auxiliary data cache twenty times over, eliminating the main bottleneck that slows down request processing for modern neural networks. This development could transform the economics of cloud AI, enabling companies to serve several times more users on a single server.

The problem lay hidden in the architecture of transformers themselves — on which ChatGPT, Claude, Gemini, and other LLMs are built. When a model processes text, it creates a special KV-cache: it stores keys and values for each token needed for calculating attention in subsequent generation stages. This sounds technical, but the essence is simple — these are intermediate data without which the model cannot continue the conversation.

As the model grows and the context expands (the number of words it remembers), this cache grows exponentially. For advanced LLMs with tens of billions of parameters, the KV-cache can occupy tens of gigabytes of GPU video memory. When working with long documents or in scenarios where a server must simultaneously serve hundreds of users, memory becomes completely saturated, and the system begins to freeze.

The NVIDIA team proposed using transformational encoding to compress this cache without losing answer quality. KVTC works like an intelligent compressor: the system analyzes which parts of the KV-cache are truly critical for accuracy and which can be safely discarded or quantized. In practical tests, the method achieves 20-fold compression with minimal performance degradation of the model. This is not just memory reduction — it is a fundamental rethinking of how auxiliary transformer data is stored.

The significance of this achievement is difficult to overstate. According to research, serving LLMs in the cloud accounts for up to 60% of data center costs for memory and computation. If KVTC allows a company to fit four times as many simultaneous requests on the same equipment, this means a four-fold reduction in cost per token. For a service like ChatGPT or Claude serving millions of requests daily, this means hundreds of millions of dollars in saved expenses. At the same time, users will receive faster text generation — a cache placed in faster memory is processed noticeably quicker.

Implementing KVTC will also expand AI accessibility. Companies that cannot afford massive clusters with expensive GPUs will be able to run powerful models on more modest hardware. This is particularly important for startups and companies outside tech hubs. NVIDIA researchers have already shared detailed documentation of the method, allowing the community to quickly integrate KVTC into popular frameworks like vLLM and TensorRT-LLM.

Although KVTC solves a specific technical problem, it points to a broader trend in the AI industry: the future belongs to engineers who know how to make models not bigger and more complex, but more efficient. When model sizes are already reaching physical and economic boundaries, optimization becomes a competitive advantage. NVIDIA demonstrates that at the frontier of AI there remain truly valuable innovations — not in model architecture, but in how to practically run them in the real world.