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MiniTriton: Building a Dynamic Batcher in 100 Lines

If you’ve used any AI API, you’ve noticed the latency varies. Sometimes responses are instant. Sometimes there’s a pause. This is usually because of dynamic batching.

Dynamic batching is a scheduling technique where the server groups multiple inference requests together before running them through the model. The goal is simple: maximize GPU utilization without making users wait too long.

We’re going to build a minimal version of this system. By the end, you’ll understand why batching exists and what tradeoffs it forces you to make.

What is Dynamic Batching?

Modern GPUs are designed for parallel computation. Running a neural network on a single input wastes most of the GPU’s capacity. Running the same network on 32 inputs together is only marginally slower than running it on one input. 1

This creates an opportunity. If we can collect multiple requests and process them as a batch, we can serve more requests per second with the same hardware.

The problem is you need to wait for requests to arrive. Wait too long and users get frustrated. Don’t wait enough and you’re back to processing one request at a time.

Dynamic batching solves this with two parameters:

MAX_BATCH_SIZE = 32  # Maximum items in one batch
MAX_WAIT_MS = 10     # Maximum time to wait for more requests

These parameters define the batching policy. The batcher will flush whenever it hits either limit. This is the core idea behind systems like Triton Inference Server2 and TensorFlow Serving.3

Key Terminology

Before we write code, let’s define some terms.

Latency is the time from when a request arrives to when it gets a response. We care about percentiles. P95 latency means 95% of requests finish faster than this time. The remaining 5% are the “tail.”

Throughput is how many requests we can handle per second. Higher is better.

Queueing delay is time spent waiting in the queue before processing starts. This is where batching adds latency.

Compute time is the actual model inference time. For a batch, this grows sublinearly with batch size due to GPU parallelism.

The batching tradeoff is this: higher throughput comes at the cost of higher tail latency.

Building the Queue

We’ll use Python’s asyncio library. The basic structure is a queue where requests wait.4

import asyncio
from collections import namedtuple

Request = namedtuple('Request', ['data', 'future'])
pending_queue = asyncio.Queue()

When a client submits a request, we create a Future object. This is a placeholder for the result. The client waits on this future until we process their request.

async def submit_request(data):
    loop = asyncio.get_event_loop()
    result_future = loop.create_future()
    
    # Put request in queue and return a future that will hold the result
    await pending_queue.put(Request(data, result_future))
    return await result_future  # Block until result is ready

The request sits in the queue until the batcher is ready to process it.

The Flusher Loop

The flusher is a background coroutine that implements the batching policy. It runs continuously and decides when to process batches.

async def batch_flusher(model_fn):
    while True:
        batch = []
        # Calculate when we should stop waiting for more requests
        deadline = asyncio.get_event_loop().time() + (MAX_WAIT_MS / 1000)
        
        # Collect requests until batch is full or deadline hits
        while len(batch) < MAX_BATCH_SIZE:
            timeout = max(0, deadline - asyncio.get_event_loop().time())
            try:
                req = await asyncio.wait_for(
                    pending_queue.get(), 
                    timeout=timeout
                )
                batch.append(req)
            except asyncio.TimeoutError:
                break  # Deadline reached, process what we have

The logic here is straightforward. We collect requests until we hit MAX_BATCH_SIZE or until MAX_WAIT_MS elapses. Whichever happens first triggers a flush.

Once we have a batch, we process it:

        if batch:
            # Extract input data from all requests
            inputs = [r.data for r in batch]
            # Process entire batch in one model call
            results = await model_fn(inputs)
            
            # Wake up all waiting clients with their results
            for req, result in zip(batch, results):
                req.future.set_result(result)

Setting the result on the future wakes up the waiting client. They get their response and move on.

Simulating a Model

Real inference servers call actual neural networks. We’ll simulate this with sleep. The key property we need to model is sublinear scaling.

A batch of size N should take more time than a batch of size 1, but not N times as much.1 We’ll use a simple linear model for this simulation.

async def fake_model(batch):
    batch_size = len(batch)
    # 5ms base + 3ms per additional item (sublinear scaling)
    compute_time = 0.005 + (0.003 * (batch_size - 1))
    
    await asyncio.sleep(compute_time)
    return [f"processed_{x}" for x in batch]

This is obviously simplified. Real GPU kernels have more complex performance characteristics.5 But it’s good enough to demonstrate the tradeoff.

Measuring Latency Distribution

We need to measure how latency changes with different batching parameters. The standard approach is to send many concurrent requests and record their latencies.

import time
import statistics

async def benchmark(num_requests=500):
    latencies = []
    
    async def send_one(i):
        start = time.time()
        await submit_request(f"req_{i}")
        duration = (time.time() - start) * 1000  # Convert to milliseconds
        latencies.append(duration)
    
    # Launch all requests simultaneously to simulate real load
    await asyncio.gather(*[send_one(i) for i in range(num_requests)])
    
    latencies.sort()
    return {
        'p50': statistics.median(latencies),
        'p95': latencies[int(0.95 * len(latencies))],
        'p99': latencies[int(0.99 * len(latencies))]
    }

We launch all requests concurrently using asyncio.gather(). This simulates realistic load where requests arrive from many clients.

The Experiment

I ran this with three configurations on my laptop. Each test sent 500 concurrent requests.

  1. Configuration 1: (batch=8, wait=5ms)
  • P50: 11ms
  • P95: 24ms
  • P99: 31ms
  • Throughput: ~640 req/s
  1. Configuration 2: (batch=16, wait=10ms)
  • P50: 17ms
  • P95: 43ms
  • P99: 48ms
  • Throughput: ~880 req/s
  1. Configuration 3: (batch=32, wait=20ms)
  • P50: 33ms
  • P95: 79ms
  • P99: 91ms
  • Throughput: ~1050 req/s

The pattern is clear. Larger batches increase throughput but hurt tail latency. The P95 latency nearly triples going from config 1 to config 3. Throughput only increases by 64%.

This is the fundamental tradeoff. You need to decide how much latency degradation is acceptable for the throughput gain.6

Why This Happens

Let’s trace what happens to an unlucky request.

The request arrives when the queue is empty. The batcher starts its timer. It waits for MAX_WAIT_MS hoping more requests arrive. If the system is lightly loaded, no other requests show up. The lone request sits there for the full wait period. Then it gets processed.

This is why P95 and P99 latencies are so sensitive to MAX_WAIT_MS. The worst-case latency is roughly MAX_WAIT_MS + compute_time.

Under heavy load, batches fill up quickly. Requests spend less time waiting. But they’re in larger batches, so compute time increases. This is why even P50 latency grows with batch size.

Production Systems

Real inference servers use more sophisticated policies. Triton Inference Server supports dynamic batching with additional features like preferred batch sizes and queue priority.2

TensorFlow Serving has a batch scheduler that can adapt its parameters based on observed latency.3 It tries to learn the optimal policy for your workload.

NVIDIA’s FasterTransformer and vLLM implement continuous batching for transformer models.7 This is more complex because requests can have variable output lengths. The system needs to batch and unbatch dynamically as requests complete.

The core idea remains the same though. Collect requests, process them together, manage the latency vs throughput tradeoff.

What You Should Take Away

Dynamic batching is everywhere in production ML systems. It’s not optional if you want efficient GPU utilization.

The implementation is simple. About 100 lines of Python gives you a working batcher. The hard part is choosing good parameters for your workload.

You need to measure your latency requirements. What P95 latency can your users tolerate? Then you can sweep batch sizes and find the configuration that maximizes throughput while staying under your latency budget.

Building this yourself, even as a toy, gives you intuition that’s hard to get from reading documentation. You understand why cold starts happen. You understand why latency is variable. You understand the operational constraints inference engineers deal with.

The code for this post is on my GitHub. Try running it. Change the parameters. Add a bursty load pattern. See what breaks.

That’s how you actually learn this stuff.

  1. NVIDIA. “Inference Performance Optimization.” NVIDIA Deep Learning Performance Documentation. https://docs.nvidia.com/deeplearning/performance/ ↩ ↩2

  2. NVIDIA Triton Inference Server. “Model Configuration: Dynamic Batching.” https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#dynamic-batcher ↩ ↩2

  3. TensorFlow. “TensorFlow Serving Batching Guide.” https://github.com/tensorflow/serving/blob/master/tensorflow_serving/batching/README.md ↩ ↩2

  4. Python Software Foundation. “asyncio — Asynchronous I/O.” Python 3 Documentation. https://docs.python.org/3/library/asyncio.html ↩

  5. Jia, Zhihao, et al. “Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking.” arXiv:1804.06826 (2018). ↩

  6. Dean, Jeffrey, and Luiz André Barroso. “The tail at scale.” Communications of the ACM 56.2 (2013): 74-80. ↩

  7. vLLM Team. “Continuous Batching in vLLM.” https://blog.vllm.ai/2023/06/20/vllm.html ↩