CacheGen & CacheBlend: Smarter KV Cache Handling for Faster AI Agents
- Name
- Sebastian Brandt
Updated on
Large Language Models (LLMs) like GPT, Claude, and LLaMA are amazing, but they’re also slow and resource-hungry when handling long contexts. Every time the model processes your prompt or document history, it builds an internal "memory" called the KV cache (key/value tensors). Managing this cache efficiently is critical if you want to build responsive AI apps.
Two recent research papers—CacheGen (2023) and CacheBlend (2024)—propose new ways to speed things up. Here’s a digest of what they found and, more importantly, what you can do with it when building AI agents.
Paper 1: CacheGen – Compressing & Streaming the KV Cache
The challenge:
When LLMs are deployed across servers, KV caches often need to be sent over the network. But the raw cache is huge, and moving it can take longer than recomputing it from scratch. That’s wasted time.
CacheGen’s solution:
- Compress smarter: KV values from nearby tokens look similar (called token-wise locality). CacheGen exploits this, shrinking the cache by 3.5–4.3×.
- Layer-aware compression: Some layers are less sensitive to tiny errors, so CacheGen compresses them more aggressively.
- Adaptive streaming: Like Netflix video quality, it adjusts compression depending on network speed. If things get bad, it falls back to sending raw text for recompute.
Results:
- ~3–4× faster cache transfer.
- Almost no drop in model output quality.
👉 Takeaway for builders:
When designing multi-server AI agents, don’t ship raw caches. Instead:
- Compress KV tensors (delta encoding + quantization).
- Adjust compression in real time based on bandwidth.
- Always have a fallback path (send raw text → recompute).
Paper 2: CacheBlend – Smarter Cache Reuse in RAG
The challenge:
In Retrieval-Augmented Generation (RAG), the model takes multiple chunks of text (retrieved docs). Ideally, you’d reuse caches for each chunk. But if you blindly reuse them, the model may miss cross-attention between chunks, leading to wrong answers.
CacheBlend’s solution:
- Reuse what’s safe: Store and reuse cached tokens where possible.
- Selective recompute: For each layer, detect “important tokens” that matter for cross-attention and recompute only those.
- Overlap with I/O: While new data is fetched, recompute happens in parallel—hiding the latency.
Results:
- 2–3× faster time-to-first-token (TTFT).
- 3–5× higher throughput.
- Accuracy is the same—or slightly better—than full recompute.
👉 Takeaway for builders:
If you’re building RAG pipelines:
- Reuse KV caches between chunks, but don’t trust them blindly.
- Recompute only the most critical tokens (10–20% often suffices).
- Pipeline recompute with I/O to avoid bottlenecks.
Quick Comparison
| Paper | Problem | Core Idea | Benefits |
|---|---|---|---|
| CacheGen | KV transfer over networks is slow | Compress + stream caches adaptively | ~4× faster, near-lossless quality |
| CacheBlend | RAG cache reuse breaks cross-attn | Hybrid reuse + selective recompute | 2–3× faster TTFT, 3–5× throughput |
Practical Checklist for AI Agent Developers
When you’re building apps on top of LLMs:
-
Optimize cache transfers
Compress and stream KV caches instead of sending them raw. -
Design for variable network conditions
Adaptive compression keeps UX smooth even with unstable bandwidth. -
Balance reuse with accuracy
Reuse caches when safe, but recompute critical tokens to keep answers reliable. -
Pipeline tasks
Overlap recomputation with network fetches or I/O to reduce perceived latency. -
Always have a fallback
Graceful degradation (recompute from text) is better than a broken agent.
Final Thoughts
Both CacheGen and CacheBlend show that faster AI isn’t just about bigger GPUs—it’s about smarter cache management. For anyone building AI agents or RAG-powered apps, adopting these strategies can mean the difference between a sluggish prototype and a production-ready product.
As models get bigger and contexts longer, these ideas will only become more important.