Understanding and Implementing KV Cache for Efficient LLM Inference

LLM-Optimization - Jul 29, 2025

Key-Value (KV) cache is a crucial optimization technique used in Large Language Models (LLMs) to speed up autoregressive inference by caching previously computed key and value tensors during transformer decoding. This article explores the underlying concepts, implementation strategies, and benefits of KV caching for efficient LLM inference.

1. What is KV Cache in Transformer Models?

During autoregressive generation, transformer models compute attention scores using queries (Q), keys (K), and values (V). At each decoding step, the model attends over all previous tokens by recomputing keys and values, which is computationally expensive. KV cache stores the keys and values from previous steps to avoid redundant computation.

Formally, for each layer at timestep $t$, the model computes: 

Q_t, K_t, V_t = TransformerLayer(x_t)

KV cache maintains: 

K_{1:t} = [K_1, K_2, ..., K_t],  V_{1:t} = [V_1, V_2, ..., V_t]

This allows efficient attention computation without recomputing keys and values for all previous tokens.

2. Benefits of Using KV Cache

  • Reduced Computation: Avoids recomputation of keys and values for past tokens at each step.
  • Lower Latency: Speeds up autoregressive decoding, enabling real-time applications.
  • Memory Efficiency: Stores compressed representations rather than raw activations.
  • Scalability: Enables inference on longer sequences without quadratic time complexity.

3. Implementation Details

KV cache typically involves maintaining two tensors per transformer layer: K_cache and V_cache. At each decoding step:

  • Compute current step's Q_t, K_t, V_t.
  • Append K_t and V_t to K_cache and V_cache respectively.
  • Compute attention using Q_t and cached K_cache, V_cache.

This can be expressed as: 

Attention(Q_t, K_{1:t}, V_{1:t}) = softmax\left(\frac{Q_t K_{1:t}^T}{\sqrt{d_k}}\right) V_{1:t}

where $d_k$ is the dimensionality of the key vectors.

4. Challenges and Considerations

  • Memory Growth: KV cache size grows linearly with sequence length, requiring efficient memory management.
  • Batching: Handling variable-length sequences and batch processing can complicate cache management.
  • Hardware Constraints: Efficient implementation depends on hardware capabilities such as GPU memory and bandwidth.
  • Cache Invalidation: For models with dynamic architectures or pruning, cache consistency must be maintained.

5. Practical Usage and Tools

Modern LLM frameworks like Hugging Face Transformers and OpenAI's API internally use KV caching to optimize inference speed. Developers can leverage built-in APIs to enable KV cache during generation, significantly improving performance on long sequences.

Example in Hugging Face Transformers: 

outputs = model.generate(input_ids, use_cache=True)

This flag enables KV caching automatically.