Why is AI so memory hungry?
Understanding KV Cache: The secret memory vault that makes LLMs fast.
Understanding Self-Attention
Transformer models use self-attention to understand relationships between words. Each word looks at all previous words to understand context.
Self-Attention Mechanism
How KV Cache Helps
For each new word, recalculate K and V for ALL previous words. This is O(NĀ²) complexity.
Store previously computed K and V values. Only compute for the new word. This is O(N) complexity.
Since previous tokens don't change, their K and V values remain constant. We can cache them and reuse them for every new token!
Self-Attention Formula
By caching K and V, we avoid recomputing them for every new token.
How LLMs Write
Imagine you are writing a story. To write the next word, you need to remember everything that happened before.
Large Language Models (LLMs) work the same way. They predict one word (token) at a time.
Input: "The quick brown fox"
Prediction: "jumps"
The model looks at all previous words to guess the next one.
The Problem: No Cache
Without a cache, the model must re-compute the entire sentence from the beginning to generate just one new word.
The Solution: KV Cache
With KV Cache, we save the computational need from previous words since we only need to calculate the new word. Thus, saving energy, cost and help protect the environment.
The Speed Advantage
Without KV Cache, the model has to re-compute everything for every new word.
With KV Cache, it only computes the new word.
Why it matters
Without KV Cache, the computational cost grows linearly with every new token. For long contexts (e.g., 128k tokens), this makes generation prohibitively slow without caching.
The Cost: Memory Usage
KV Cache makes generation fast, but it eats up RAM. Newer models use tricks like GQA and MLA to reduce this cost.
Optimization Impact
Comparing GQA optimization vs unoptimized (Standard) architecture
Why this matters: Grouped Query Attention (GQA) shares key-value pairs across multiple query heads, reducing memory usage without sacrificing performance.
DynamoEnterprise-Scale LLM Inference
NVIDIA Dynamo optimizes LLM serving across distributed systems with intelligent KV cache management
Prefill-Decode Disaggregation
Different phases have different resource needs. Separating them maximizes GPU utilization and throughput.
KV Block Manager (KVBM)
KV cache overflows to slower tiers when GPU memory is full, enabling larger context windows.
NIXL: Fast Data Transfer
Seamless KV cache sharing between prefill and decode engines across different nodes.
KV Cache-Aware Routing
Routes requests to workers with highest KV cache hit rate, reducing redundant computation.
How It All Works Together
Separate prefill (compute) and decode (memory) phases for optimal resource use
KVBM stores KV cache across GPU, CPU, SSD, and network tiers
NIXL moves KV cache between nodes with ultra-low latency
Direct requests to workers with best cache hit rates
Higher throughput, lower latency, and efficient resource utilization for serving LLMs at scale
KV Cache for Agent Swarms
In Agentic AI, context is a continuous stream from sensors and cameras, not just a 2000-token prompt
Drone Swarm: Agent-to-Agent Handoff
Drone A tracks vehicle ā Low battery ā Sends basic info to Drone B ā Drone B restarts, wasting time to re-acquire target. Drone A's "memory" is lost.
Zero Context Loss
Agents instantly inherit full context from previous agents, maintaining continuous awareness
Persistent Memory
KV cache becomes long-term memory for the entire agent swarm, stored on VAST
Instant Handoff
No time wasted re-acquiring targets or rebuilding context from scratch
Collective Intelligence Through Shared Memory
By transforming KV cache into persistent, shareable "long-term memory," agent swarms achieve true collective intelligence. Each agent builds upon the knowledge of all previous agents.