Batch Processing

The technique of grouping multiple inference requests together and processing them simultaneously on hardware accelerators like GPUs, maximizing computational efficiency and throughput at the cost of increased latency for individual requests.

The Simple Version

Imagine a laundromat with 8 washing machines. You could run one load at a time (slow, inefficient), or you could wait until you have 8 loads and run them all together (fast, efficient).

Batch processing does the same for AI inference. Instead of processing one request at a time, you group multiple requests and process them together on the GPU. This maximizes GPU utilization and dramatically increases throughput.

Detailed Explanation

GPUs are designed for parallel computation. Processing a single request underutilizes the GPU’s capabilities. Batching groups multiple requests to fully utilize the hardware.

Batching Strategies:

1. Static Batching:

Wait for N requests, then process as a batch
Simple but can introduce latency (waiting for batch to fill)

2. Dynamic Batching:

Process requests as they arrive, batch what’s available
Balances latency and throughput

3. Continuous Batching (vLLM):

Dynamically add/remove requests from batch during processing
Handles variable-length sequences efficiently
State-of-the-art for LLM serving

Trade-offs:

Larger batches: Higher throughput, higher latency per request
Smaller batches: Lower latency, lower throughput
Optimal batch size: Depends on model, hardware, and latency requirements

Memory Considerations:

Larger batches require more GPU memory
Must balance batch size with available VRAM
KV cache memory grows with batch size and sequence length

Key Characteristics

Throughput Optimization: Maximizes GPU utilization
Latency Trade-off: Increases per-request latency
Hardware-Dependent: Benefits scale with GPU parallelism
Configurable: Batch size is a tunable hyperparameter

Business Context

Batch processing is essential for cost-effective AI deployment at scale:

When to Use:

High-volume APIs: Serving thousands of requests per second
Offline processing: Document analysis, data transformation
Cost-sensitive applications: Maximizing ROI on GPU infrastructure

When to Avoid:

Real-time chat: Users expect immediate responses
Interactive applications: Latency-sensitive use cases
Low-traffic systems: Batching overhead not justified

Cost Impact:

Without batching: 1 GPU serves 10 RPS = $0.10/request
With batching (size=32): 1 GPU serves 100 RPS = $0.01/request
Savings: 10x cost reduction through efficient hardware utilization

Real-World Analogy

A bus vs. a taxi. A taxi (no batching) takes one passenger directly to their destination (low latency, high cost per passenger). A bus (batching) takes 50 passengers together, making multiple stops (higher latency per passenger, much lower cost per passenger). The choice depends on urgency and budget.

Code Example

# Batch inference with Hugging Face
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# Single request (no batching)
single_prompt = "Hello, my name is"
inputs = tokenizer(single_prompt, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=10)

# Batch of requests (batching)
batch_prompts = [
    "Hello, my name is",
    "The capital of France is",
    "Machine learning is",
    "The future of AI is"
]

# Tokenize as a batch
inputs = tokenizer(batch_prompts, return_tensors="pt", padding=True)

# Process all 4 prompts in one GPU pass
outputs = model.generate(**inputs, max_new_tokens=10)

# Decode all results
for i, output in enumerate(outputs):
    print(f"Prompt {i+1}:", tokenizer.decode(output))

Common Misconceptions

Myth: Batching always improves performance.
Reality: Batching improves throughput but increases latency. For latency-sensitive applications, smaller batches or no batching may be better.
Myth: Larger batches are always better.
Reality: Very large batches can exceed GPU memory or introduce unacceptable latency. Optimal batch size depends on the specific use case.
Myth: Batching is only for training.
Reality: Batching is critical for inference too, especially for high-throughput serving scenarios.