Batch inference for a large dataset usually means: run your trained model on many records efficiently, in chunks, rather than one row at a time.

Here’s the practical approach.

1) Load the model once

Don’t reload the model for every batch. Load it a single time at the start.

2) Process data in batches

Split the dataset into manageable chunks so you don’t run out of memory.

Typical batch sizes:

• CPU: 32–512
• GPU: 64–4096, depending on model and memory

3) Use a data pipeline

Read data lazily instead of loading the full dataset into RAM if it’s large.

Common tools:

• Python: pandas chunking, pyarrow, polars
• PyTorch: Dataset + DataLoader
• TensorFlow: tf.data
• Spark: mapInPandas, UDFs, or foreachPartition

4) Predict batch-by-batch

For each batch:

• preprocess
• run inference
• save predictions immediately

This avoids holding all outputs in memory.

5) Save outputs incrementally

Write results to:

• CSV/Parquet
• database
• object storage
• message queue

Prefer Parquet for large outputs if possible.

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Example: simple Python batch inference with pandas

import pandas as pd
import joblib

model = joblib.load("model.pkl")

chunk_size = 10000
results = []

for chunk in pd.read_csv("input.csv", chunksize=chunk_size):
    X = chunk[["feature1", "feature2", "feature3"]]
    preds = model.predict(X)
    chunk["prediction"] = preds
    results.append(chunk)

output = pd.concat(results, ignore_index=True)
output.to_csv("predictions.csv", index=False)

If the output is very large, write each chunk directly instead of collecting all results.

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Better pattern: stream results to disk

import pandas as pd
import joblib

model = joblib.load("model.pkl")

first = True
for chunk in pd.read_csv("input.csv", chunksize=10000):
    X = chunk[["feature1", "feature2", "feature3"]]
    chunk["prediction"] = model.predict(X)
    chunk.to_csv("predictions.csv", mode="a", header=first, index=False)
    first = False

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If using PyTorch

from torch.utils.data import DataLoader

model.eval()
with torch.no_grad():
    for batch in dataloader:
        outputs = model(batch)
        # save outputs

Use:

• model.eval()
• torch.no_grad()
• a DataLoader with batch_size > 1

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Performance tips

• Vectorize preprocessing where possible
• Avoid Python loops over individual rows
• Use mixed precision on supported GPUs
• Increase batch size until memory becomes a problem
• Use parallelism if preprocessing is the bottleneck
• Keep input/output formats efficient (Parquet > CSV for large workloads)

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If the dataset is too large for one machine

Use distributed batch inference:

• Spark
• Ray
• Dask
• Kubernetes jobs
• cloud batch services like AWS Batch / Vertex AI batch prediction / SageMaker Batch Transform

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If you tell me what framework you’re using (scikit-learn, PyTorch, TensorFlow, Hugging Face, Spark, etc.) and what format your data is in, I can show a concrete batch inference example.