arXiv:2602.23200v2 Announce Type: replace Abstract: When transformer-based language models are deployed for text generation, most of the inference time is spent in the decoding stage, where output tokens are generated sequentially. Reducing the hardware cost of each decoding step is therefore critical for efficient long-context generation. A major bottleneck is the key-value (KV) cache, whose size grows with sequence length and often dominates the model's memory footprint. Prior work has proposed quantization methods to compress the KV cache while minimizing its loss of precision. We present I
The rapid growth of Large Language Models (LLMs) and their deployment necessitates continuous innovation in hardware optimization to meet increasing computational demands, making KV cache quantization a critical and timely area of research.
This research addresses a major bottleneck in LLM inference, directly impacting the cost and efficiency of AI deployments, and enabling more sophisticated and longer-context applications.
Hardware-aware, tuning-free quantization methods for KV cache can significantly reduce memory footprint and computational requirements for LLMs, making their deployment more economical and scalable.
- · AI compute providers
- · Cloud infrastructure companies
- · Companies deploying LLMs
- · LLM researchers
- · Companies reliant on inefficient LLM deployments
Reduced operational costs for large language model inference.
Enables the deployment of larger and more complex LLMs in resource-constrained environments.
Accelerates the development and adoption of AI-powered applications that require long context windows, potentially expanding the market for agentic AI systems.
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Read at arXiv cs.LG