Mastering Long Contexts in LLMs with KVPress

TL;DR: KVPress packs the latest KV cache compression techniques, enabling memory-efficient long-context LLMs. 🚀

One of the key features of Large Language Models (LLMs) is their context window—the maximum number of tokens they can process in a single request. As LLMs evolve, their context windows are becoming increasingly larger.

Larger context windows unlock incredible possibilities:

• In-context retrieval: Seamlessly referencing large amounts of text within a single query.
• In-context learning: Adapting behavior to specific examples within the same session.
• Extended reasoning: Handling very long chains of thought without breaking context.

However, these extended windows come at a cost—memory taken by the long context in the KV Cache becomes hard to manage. For instance, handling 1M tokens with Llama 3-70B in float16 demands 330GB for KV Cache, rendering it infeasible for many applications.

In this blog post, we'll address one solution for this problem: compressing the KV Cache for more efficient generation. To achieve this, we'll explore:

• What the KV Cache is and why it matters.
• KVPress, a powerful toolkit from NVIDIA designed to compress KV Cache effectively.
• The inner workings of KVPress and how it achieves compression.

Before getting started, explore KVPress in this Space (you'll find examples at the end if needed):

What is KV Cache and Why does it matter?

Figure 1: Key Value cache inside the attention module (Source: NVIDIA)

In autoregressive models, text generation happens token by token, with each prediction relying on all preceding tokens for context. For example:

• To generate token 1000, the model must consider the representations of tokens 1 to 999.
• To generate token 1001, the same information (tokens 1 to 999) must be processed again, along with token 1000.

The Problem: KV Cache and its Linearly Scaling Burden

The Size of the KV Cache

Size(KV)=2×2×80×8×128×1M=327.6GB \text{Size}(\text{KV}) = 2 \times 2 \times 80 \times 8 \times 128 \times 1M = 327.6 \text{GB}