Phase 7: Production~8 minadvanced

🚀Deployment & Serving

Launching to the World

vLLM, TensorRT-LLM, API design, load balancing, and production infrastructure.

vLLMTensorRT-LLMKV CachingSpeculative Decoding

Launching to the World

You've trained and optimized your model. Now it needs to serve millions of users. Inference serving is about making the model fast, reliable, and cost-effective at scale.

Opening a Restaurant

Training is like perfecting recipes. Deployment is opening the restaurant — handling rush hour (high concurrency), keeping dishes warm (KV caching), and not making customers wait too long (latency optimization).
<100ms
P50 Latency Goal
1000+
Requests/Second
99.9%
Uptime SLA
$0.01
Per 1K Tokens

Token-by-Token Generation

👆 Try it yourself!
Temperature: 0.7Balanced
Q: What is the moon?
A:

Serving Frameworks

vLLM

PagedAttention, continuous batching

Speed: HighEase: Medium
TensorRT-LLM

NVIDIA optimized, best for H100

Speed: HighestEase: Low
Text Generation Inference

HuggingFace, production-ready

Speed: HighEase: High
llama.cpp

CPU inference, quantization

Speed: MediumEase: High

✅
Key Takeaways
  • vLLM and TensorRT-LLM are leading serving frameworks
  • KV caching is essential for fast inference
  • Continuous batching maximizes GPU utilization
  • Target <100ms latency for good user experience