Top 10 KV Cache Compression Techniques for LLM Inference

Top 10 KV Cache Compression Techniques for LLM Inference: Reducing Memory Overhead Across Eviction, Quantization, and Low-Rank Methods

The KV cache has become the primary memory bottleneck for large language models, requiring up to 180 GB for a 30B parameter model at a batch size of 128. Modern compression strategies now offer throughput improvements up to 29x without retraining base models.

Why This Matters

While ideal LLM models assume infinite memory, production reality forces a trade-off between context length and hardware constraints. For a 7-billion-parameter model, weights consume only 14 GB while the KV cache can demand 72 GB, leading to severe throughput degradation or out-of-memory errors in high-concurrency environments.

Key Insights

• Token eviction via H2O (NeurIPS 2023) improves throughput by 29x on OPT-30B by retaining only ‘Heavy Hitter’ tokens that contribute most to attention scores.
• StreamingLLM preserves initial ‘attention sink’ tokens to stabilize infinite sequence generation for streaming dialogue applications.
• KIVI (ICML 2024) utilizes 2-bit asymmetric quantization to reduce combined peak memory by 2.6x across Llama-2 and Mistral models.
• DeepSeek’s Multi-Head Latent Attention (MLA) reduces KV cache requirements by 93.3% compared to traditional dense models like DeepSeek-67B.
• TurboQuant (ICLR 2026) employs random orthogonal rotations and 1-bit QJL correction to achieve 6x memory reduction at 3-bit precision.

Practical Applications

• Use Case: DeepSeek-V2 and DeepSeek-R1 use Multi-Head Latent Attention (MLA) to serve long-context queries with 93.3% less memory overhead. Pitfall: Implementing MLA requires training from scratch, making it unsuitable for post-training optimization of existing models.
• Use Case: Production systems using Llama-3 or Mistral utilize Grouped-Query Attention (GQA) as a baseline to optimize the KV cache. Pitfall: Dropping initial tokens in streaming scenarios without retention mechanisms like StreamingLLM leads to catastrophic accuracy loss.

References:

• https://www.marktechpost.com/2026/04/29/top-10-kv-cache-compression-techniques-for-llm-inference-reducing-memory-overhead-across-eviction-quantization-and-low-rank-methods/