Deploy local agents everywhere with LFM2.5-2.6B
huggingface.co·16h ago
System Design
ByteByteGo·15h ago·5 min read
Why An LLM’s Memory Gets Expensive and How to Fix It
TL;DR
Large Language Models (LLMs) can incur high memory costs due to their architecture and data handling. Solutions include optimizing memory usage and implementing efficient data management strategies.
✦ Why It Matters
Evaluate your LLM's memory usage today and consider implementing optimization techniques to reduce costs.
Key Takeaways
- Use grouped-query attention to reduce cache size effectively.
- Apply quantization to lower memory usage without retraining.
- Implement eviction strategies to manage token count efficiently.
- Adopt paged attention for better memory management in serving.
How It Works
LLMs generate tokens in two phases: prefill and decoding. During prefill, the model processes all input tokens simultaneously, creating key and value vectors stored in the KV cache.
In decoding, each new token requires reading from the entire cache, which becomes memory-bound and costly as the cache size increases. Techniques like grouped-query attention reduce the number of key-value heads, while quantization minimizes the bit size of stored values, both leading to significant memory savings.