RINA-1bit-KV: A New 1-bit KV Cache Compression Scheme for Long-Context LLM Inference

The RINA project proposes a recursive integrated noise feedback approximation method to achieve 1-bit KV cache compression. It significantly improves long-context LLM inference efficiency through dynamic error tracking technology, breaks through the upper limit of compression ratio of traditional schemes, and still maintains usable inference quality under the extreme condition of 1-bit.

When large language models process long texts, the memory usage of KV cache grows linearly with the context length, becoming a key bottleneck. Existing KV cache compression schemes (quantization, pruning, dynamic eviction) suffer from severe accuracy loss under the extreme compression ratio of 1-bit.

RINA adopts a recursive integrated architecture (capturing global semantics and local details hierarchically), a noise feedback mechanism (using quantization error as feedback to guide compression strategies), and dynamic error compensation (continuously monitoring and compensating accumulated errors) to achieve 1-bit KV cache compression.

• Extreme compression ratio: 1-bit representation achieves 16x space compression, extending context length;
• Dynamic adaptability: Allocate representation precision based on token importance;
• Controllable error: Inference quality is close to 4-bit quantization;
• Low computational overhead: Compression and decompression complexity is low, and memory savings far exceed the increase in overhead.

• Hierarchical encoder: Decompose KV vectors into subspaces and encode each independently with 1-bit;
• Noise estimation network: Real-time estimation of quantization noise distribution to guide compensation strategies;
• Adaptive threshold: Dynamically adjust quantization thresholds to retain effective information;
• Accumulated error tracking: Maintain error state vectors to compensate for historical errors.

Suitable for long document processing, multi-turn dialogue systems, code understanding and generation, and retrieval-augmented generation (RAG) scenarios. It allows consumer GPUs to handle million-token contexts, reducing service costs and improving accuracy.

Scheme Type	Compression Ratio	Accuracy Retention	Computational Overhead	Application Scenarios
Static Quantization (INT8)	2x	High	Low	General Scenarios
Static Quantization (INT4)	4x	Medium	Low	Resource-Constrained
Dynamic Pruning	2-8x	Medium	Medium	Long Context
H2O/Streaming	2-10x	Medium-High	Low	Streaming Processing
RINA (1-bit)	16x	Medium	Medium-Low	Extreme Compression
RINA breaks through the upper limit of compression ratio and maintains usable inference quality under 1-bit conditions.

Insights: The potential of recursive structures in the compression field, the value of feedback mechanisms, and the application of hierarchical representation learning; Future directions: Collaborative design of compression technology and model architecture (natively supporting low-precision representations).