LLM Inference

Scale LLM inference with distributed, optimized serving.

Overview

Scaling LLM inference requires combining distributed parallelism, optimized kernels, and dynamic resource allocation to meet stringent latency and throughput targets.

State-of-the-Art Methods and Architectures

Data Parallelism (DDP)

Splits batches across GPUs, synchronizing gradients.

Model Parallelism (FSDP)

Shards model parameters across devices to host large models.

Pipeline Parallelism

Chains layer groups across GPUs to maximize utilization.

Market Landscape & Forecasts

40x

Cost Reduction

since 2023

≤200ms

Latency Target

>=100 tokens/s

Throughput

Implementation Guide

Benchmarking

Run throughput and latency tests on sample prompts.

Autoscaling Policies

Define CPU/GPU thresholds and queue backpressure.

Monitoring & APM

Integrate with Datadog or Prometheus for real-time metrics.

Disaster Recovery

Cross-region failover and stateful checkpointing.

Technical Deep Dive

Data Preparation

Collect domain-specific text (e.g., medical records, legal documents). Clean and format data into JSONL.

Adapter Insertion

Insert LoRA/QLoRA adapters into the base model.

Training

Run training with domain data, using a learning rate schedule and early stopping. Monitor loss and validation metrics.

Evaluation

Use ROUGE, accuracy, or custom metrics. Compare outputs to base model.

Sample Code

from transformers import AutoModelForCausalLM, TrainingArguments, Trainer model = AutoModelForCausalLM.from_pretrained('llama-7b') # Insert LoRA adapters... # Prepare data... trainer = Trainer(model=model, args=TrainingArguments(...), train_dataset=...) trainer.train()

Why Fine-Tuning?

Single-Node Inference

- Limited scalability - Higher latency - Not fault-tolerant

Distributed Inference

- Scales to demand - Low latency - Resilient to failures

FAQ

Industry Voices

"40x reduction in cost-to-serve since 2023."

OpenAI Infrastructure Blog

Project Timeline

Model Loading

Load and optimize model.

Scaling

Autoscale based on demand.

Monitoring

Track performance and errors.