Context Caching & KV Compression: Serving 1M+ Contexts at Sub-Second Latency

Architectural Overview & Engineering Context

Implementing RadixAttention tree-based prefix caching and FP8 KV quantization in vLLM and SGLang to reduce prompt evaluation cost by 90%.

Modern production AI systems require rigorous systems-level optimization. Whether managing GPU memory allocations, designing low-latency retrieval pipelines, or orchestrating multi-agent state machines, understanding the underlying trade-offs separates fragile prototypes from mission-critical platforms.

System Topology & Data Flow

The diagram below outlines the core execution path and component decoupling for this architecture:

[Incoming User Request: System Prompt + Codebase AST + User Query]
                                │
                                ▼
               [Radix Trie Context Lookup Engine]
                                │
          ┌─────────────────────┴─────────────────────┐
          ▼ (Exact Cache Hit: 45,000 Tokens)          ▼ (Cache Miss: 500 Tokens)
[Reuse GPU Memory KV Paged Blocks]            [Compute Forward GEMM Prefill]
  • Zero GPU Math FLOPs                         • Compute FP8 Activation
  • Instant TTFT < 40ms                         • Append to Radix Trie
          │                                           │
          └─────────────────────┬─────────────────────┘
                                ▼
                 [Autoregressive Decode Pipeline]

Production Implementation & Code Pattern

Below is the reference production pattern demonstrating the core execution flow, asynchronous handling, and schema validation:

# SGLang Radix Cache Server Setup
import requests

SERVER_URL = "http://localhost:30000/v1/chat/completions"
LARGE_SYSTEM_DOC = "Enterprise Knowledge Graph Spec v4.2..."

payload = {
    "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
    "messages": [
        {"role": "system", "content": f"Enterprise Context:\n{LARGE_SYSTEM_DOC}"},
        {"role": "user", "content": "What is the failover protocol for PostgreSQL?"}
    ],
    "temperature": 0.1
}

# Request 1: Full prefill (~50,000 tokens) -> Latency: 1,200ms
resp1 = requests.post(SERVER_URL, json=payload).json()
# Request 2 (Different User Query, Same Doc): Hits Radix cache -> Latency: 38ms!
payload["messages"][1]["content"] = "How are Kafka consumer offsets committed?"
resp2 = requests.post(SERVER_URL, json=payload).json()

Quantitative Benchmarks & System Trade-Offs

Production telemetry across high-concurrency benchmarks demonstrates substantial improvements in throughput, latency, and memory utilization:

Production Gotchas & Failure Modes