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.
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.
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: Static Batching: Wait for N requests, then process as a batch Simple but can introduce latency (waiting for batch to fill) Dynamic Batching: Process requests as they arrive, batch what's available Balances latency and throughput 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
# 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))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
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.
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.
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: Static Batching: Wait for N requests, then process as a batch Simple but can introduce latency (waiting for batch to fill) Dynamic Batching: Process requests as they arrive, batch what's available Balances latency and throughput 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
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