Optimising Deployment Inference

In this chapter, we’ll explore advanced techniques and best practices for optimizing LLM deployments in production environments. We’ll cover strategies for improving performance, reducing costs, and ensuring reliability at scale.

Performance Optimization Techniques

1. Model Optimization

Quantization

from transformers import AutoModelForCausalLM, BitsAndBytesConfig

# Configure quantization
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype="float16",
    bnb_4bit_quant_type="nf4"
)

# Load quantized model
model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceTB/SmolLM2-1.7B-Instruct",
    quantization_config=quantization_config,
    device_map="auto"
)

Model Pruning

2. Hardware Optimization

GPU Memory Management

import torch
torch.cuda.empty_cache()  # Clear GPU cache
torch.cuda.memory_summary()  # Monitor memory usage

Multi-GPU Deployment

3. Batching Strategies

Dynamic Batching

class DynamicBatcher:
    def __init__(self, max_batch_size=32, max_wait_time=0.1):
        self.queue = []
        self.max_batch_size = max_batch_size
        self.max_wait_time = max_wait_time
    
    async def add_request(self, request):
        self.queue.append(request)
        if len(self.queue) >= self.max_batch_size:
            return await self.process_batch()
        return await self.wait_for_batch()

Request Queuing

Deployment Architecture

1. Load Balancing

Request Distribution

from fastapi import FastAPI
from load_balancer import LoadBalancer

app = FastAPI()
lb = LoadBalancer(["server1", "server2", "server3"])

@app.post("/generate")
async def generate(request: Request):
    server = lb.get_next_server()
    return await server.process(request)

Health Checking

2. Caching Strategies

Response Caching

from functools import lru_cache
import redis

redis_client = redis.Redis(host='localhost', port=6379)

@lru_cache(maxsize=1000)
def get_cached_response(prompt: str) -> str:
    # Check local cache first
    if cached := redis_client.get(prompt):
        return cached
    
    # Generate and cache response
    response = generate_response(prompt)
    redis_client.setex(prompt, 3600, response)  # Cache for 1 hour
    return response

KV Cache Management

3. Monitoring and Observability

Performance Metrics

from prometheus_client import Counter, Histogram

requests_total = Counter('requests_total', 'Total requests processed')
latency_seconds = Histogram('latency_seconds', 'Request latency')

@app.post("/generate")
async def generate(request: Request):
    requests_total.inc()
    with latency_seconds.time():
        return await process_request(request)

Resource Utilization

Cost Optimization

1. Resource Scaling

Auto-scaling

class AutoScaler:
    def __init__(self, min_instances=1, max_instances=10):
        self.instances = min_instances
        self.min_instances = min_instances
        self.max_instances = max_instances
    
    def scale_based_on_load(self, current_load):
        target_instances = max(
            self.min_instances,
            min(self.max_instances, current_load // 100)
        )
        self.adjust_instances(target_instances)

Cost Monitoring

2. Request Optimization

Token Management

class TokenBudget:
    def __init__(self, max_daily_tokens=1000000):
        self.max_daily_tokens = max_daily_tokens
        self.used_tokens = 0
        self.reset_time = datetime.now() + timedelta(days=1)
    
    def can_process_request(self, token_count):
        if datetime.now() >= self.reset_time:
            self.used_tokens = 0
            self.reset_time = datetime.now() + timedelta(days=1)
        
        if self.used_tokens + token_count <= self.max_daily_tokens:
            self.used_tokens += token_count
            return True
        return False

Best Practices

  1. Gradual Rollout

  2. Error Handling

  3. Security

Resources

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