Optimize models for low-latency, high-throughput inference at production scale.

Model Optimization

Quantization

from transformers import AutoModelForCausalLM
import torch

# INT8 quantization for 4x speedup
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3-8b",
    load_in_8bit=True,  # Automatic INT8 quantization
    device_map="auto"
)

# Custom quantization with GPTQ
from auto_gptq import AutoGPTQForCausalLM

model = AutoGPTQForCausalLM.from_quantized(
    "TheBloke/Llama-3-8B-GPTQ",
    use_safetensors=True,
    device="cuda:0"
)

TensorRT Compilation

import tensorrt as trt
import torch_tensorrt

# Compile PyTorch model to TensorRT
trt_model = torch_tensorrt.compile(
    model,
    inputs=[torch_tensorrt.Input(
        shape=[1, 512],  # batch_size, seq_len
        dtype=torch.long
    )],
    enabled_precisions={torch.float16},
    workspace_size=1 << 30  # 1GB workspace
)

# 2-5x inference speedup
output = trt_model(input_ids)

Batching Strategies

Dynamic Batching

class DynamicBatcher:
    def __init__(self, model, max_batch_size=32, max_wait_ms=10):
        self.model = model
        self.max_batch_size = max_batch_size
        self.max_wait_ms = max_wait_ms
        self.queue = []
    
    async def infer(self, input_data):
        # Add to queue
        future = asyncio.Future()
        self.queue.append((input_data, future))
        
        # Wait for batch or timeout
        await asyncio.sleep(self.max_wait_ms / 1000)
        
        # Process batch
        if len(self.queue) >= self.max_batch_size:
            await self._process_batch()
        
        return await future
    
    async def _process_batch(self):
        batch = self.queue[:self.max_batch_size]
        self.queue = self.queue[self.max_batch_size:]
        
        inputs = [item[0] for item in batch]
        futures = [item[1] for item in batch]
        
        # Batch inference
        outputs = self.model(inputs)
        
        # Resolve futures
        for future, output in zip(futures, outputs):
            future.set_result(output)