Deployment and Serving
Deployment Solutions
vLLM
from vllm import LLM, SamplingParams
# Initialize vLLM
llm = LLM(
model="meta-llama/Llama-2-7b-chat-hf",
tensor_parallel_size=2,
gpu_memory_utilization=0.9
)
# Generate
sampling_params = SamplingParams(
temperature=0.7,
max_tokens=512,
top_p=0.9
)
outputs = llm.generate(["Hello, world!"], sampling_params)
print(outputs[0].outputs[0].text)
TGI (Text Generation Inference)
from text_generation import Client
client = Client("http://localhost:8080")
response = client.generate(
"What is machine learning?",
max_new_tokens=200,
temperature=0.7,
do_sample=True
)
print(response.generated_text)
TensorRT-LLM
import tensorrt_llm
# Build engine
builder = tensorrt_llm.Builder()
network = builder.create_network()
# ... build network ...
# Optimize for inference
config = tensorrt_llm.BuildConfig()
engine = builder.build_serialized_network(network, config)
Serving Comparison
| Solution | Throughput | Latency | Ease of Use |
|---|---|---|---|
| vLLM | Very High | Low | Easy |
| TGI | High | Low | Easy |
| TensorRT-LLM | Highest | Lowest | Complex |
| llama.cpp | Medium | Medium | Easy |
Production Checklist
- Scaling: Auto-scaling based on load
- Monitoring: Latency, throughput, errors
- Caching: Response caching for repeated queries
- Security: Authentication, rate limiting
- Reliability: Health checks, failover
Summary
Choosing the right deployment solution depends on your scale, latency requirements, and infrastructure expertise. vLLM and TGI offer excellent out-of-the-box performance.
Next: We'll explore practical fine-tuning workflows.