Batch Size, Explained for People Who Just Want Faster Local Inference

Aug 4

# Batch Size, Explained for People Who Just Want Faster Local Inference

If you've spent any time tweaking a local inference setup, you've run into "batch size" as a configuration option and probably left it alone because the documentation assumes you already know what it does. Here's what it actually controls, and why getting it right matters more than most people realize.

**What batch size actually means.** When a model processes requests, batch size determines how many separate input sequences get processed together, as a group, in a single pass through the model. A batch size of 1 means the model handles one request at a time, start to finish, before touching the next. A batch size of 8 means it processes eight requests' worth of computation simultaneously, sharing the same pass through the model's layers.

**Why batching makes things faster overall, even though it sounds slower.** The GPU computation involved in running a model through a single forward pass is largely the same regardless of whether it's processing one sequence or several at once — the model's weights have to be read from memory either way, and that memory access, not the actual math, is usually the real bottleneck on modern hardware. Batching multiple requests together means that expensive memory read gets amortized across more work, dramatically increasing total throughput — how many tokens get generated per second across all users combined — even though it can very slightly increase the delay for any single individual request.

**The throughput-versus-latency tradeoff.** This is the actual decision batch size represents. A larger batch size increases total throughput, which matters enormously if you're serving many simultaneous users or running large offline processing jobs. But it can also increase latency — the time before any single response starts appearing — because the GPU is juggling more concurrent work per pass. If you're the only user, running a single chat session locally, a small batch size (often effectively 1) usually gives you the fastest individual response, since there's no other work competing for the same pass.

**Where this actually matters for local hosting.** If you're using Ollama or LM Studio for personal, single-user chat, batch size is mostly not something you need to touch — the default single-request behavior is already optimized for your use case. If you're using vLLM to serve a model to multiple users or applications, batch size (and vLLM's continuous batching, which dynamically groups requests as they arrive rather than waiting for a fixed batch to fill) becomes one of the most consequential settings for how many concurrent users your hardware can actually support.

**The practical rule of thumb.** Optimizing for throughput and optimizing for latency are genuinely different goals, and batch size is the dial that trades one against the other. Know which one you're actually optimizing for before you touch the setting — a config tuned for maximum throughput on a multi-user server will feel sluggish for a single person, and a config tuned for instant single-user response will badly underuse a GPU serving several people at once.

Batch Size, Explained for People Who Just Want Faster Local Inference

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