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Lesson #62 of 70
Agentic AI & RAG9 min readAdvanced
High-Throughput LLM Serving: vLLM, PagedAttention & KV Caching
Optimize self-hosted LLM inference throughput: PagedAttention virtual memory, continuous batching, KV cache quantization, and multi-GPU tensor parallelism.
Works with:vLLMTensorRT-LLMTriton Inference ServerNVIDIA H100 / A100
Key Takeaways
- Naive LLM serving wastes up to 60-80% of GPU VRAM due to memory fragmentation in dynamic Key-Value (KV) caching
- PagedAttention treats KV cache memory like virtual memory pages in operating systems, eliminating fragmentation and enabling 2x–4x higher concurrency
- Continuous iteration-level batching dynamically injects new requests as soon as earlier requests finish generation
- Model quantization (AWQ, GPTQ, FP8) reduces weight memory footprint while preserving perplexity scores
The Diagnostic Context
Deploying open-weight models (Llama 3, Mistral, Qwen) in production requires maximizing tokens per second per dollar. vLLM with PagedAttention is the gold standard for high-throughput enterprise serving.
The Core Technique
Deploying High-Concurrency vLLM Server
BASH
# Launch OpenAI-compatible vLLM server with PagedAttention & FP8 quantization
vllm serve meta-llama/Meta-Llama-3-70B-Instruct \
--tensor-parallel-size 4 \
--gpu-memory-utilization 0.92 \
--max-model-len 8192 \
--quantization fp8 \
--port 8000
PYTHON
# Client-side streaming consumption via OpenAI SDK:
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
response = client.chat.completions.create(
model="meta-llama/Meta-Llama-3-70B-Instruct",
messages=[{"role": "user", "content": "Analyze distributed lock mechanisms in Redis."}],
temperature=0.2,
stream=True
)
for chunk in response:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="", flush=True)
Key Serving Performance Metrics
- TTFT (Time to First Token): Latency required to process prompt input tokens (prefill stage).
- ITL (Inter-Token Latency): Time required to decode each subsequent output token.
- Tokens/Sec/GPU: Total system throughput under heavy concurrent load.
5-Minute Activation Challenge
Try This Right Now
Calculate the VRAM required to hold the KV Cache for 100 concurrent users at 4,000 tokens context length on a 70B model with 16-bit precision vs FP8 quantized KV cache!
Tip: Knowledge only becomes capability once you run the prompt yourself.
Comprehension Check
Test Your Instincts (1 Questions)
1