Batch or Split? How to Send Multiple Questions to LLMs Faster

The Hidden Performance Question Every AI Developer Faces

Imagine you have five completely unrelated questions you need answered by an LLM. Do you pack them into a single prompt — 'Hey, answer these five questions for me' — or fire off five separate API requests simultaneously?

It sounds like a trivial decision, but the answer reveals something fundamental about how large language models actually work. And for developers building AI-powered applications at scale, getting this wrong can mean the difference between a snappy user experience and a frustratingly slow one.

The short answer: splitting into multiple independent parallel requests is almost always faster. This is not a gut feeling — it is determined by the underlying inference mechanism of LLMs. Let us walk through the reasoning from first principles.

How LLMs Generate Text: Autoregressive Decoding

To understand this problem, you first need to grasp how LLMs produce output. Models like OpenAI's GPT-4o, Anthropic's Claude, and Meta's Llama all use a technique called autoregressive decoding. This means they generate text one token at a time, where each new token depends on every token that came before it.

When you ask a model a question, it does not compute the entire answer in one shot. Instead, it performs a forward pass through the neural network to produce token #1, then another forward pass (now including token #1 as context) to produce token #2, and so on. Each step is sequential and cannot be parallelized within a single request.

This is the critical insight: the total generation time for a single request scales linearly with the number of output tokens.

If each token takes roughly 30 milliseconds to generate, a 200-token answer takes about 6 seconds. A 1,000-token answer — which is what you might get when you batch five questions together — takes about 30 seconds.

The Math Behind Batching vs. Splitting

Let us put concrete numbers to this. Suppose each of your five questions produces a 200-token answer, and each token takes 30ms to generate.

Scenario A: One Batched Request

You combine all five questions into a single prompt. The model now needs to produce roughly 1,000 output tokens (5 × 200), plus additional tokens for formatting, labels, and transitions between answers. Realistically, you are looking at 1,100–1,200 tokens of output.

• Total time: ~1,150 tokens × 30ms = ~34.5 seconds

The model churns through the entire output sequentially. There is no way around it — token 800 cannot be generated until tokens 1 through 799 are complete.

Scenario B: Five Parallel Requests

You send five separate API calls at the same time, each asking a single question. Each request independently generates ~200 tokens.

• Time per request: ~200 tokens × 30ms = ~6 seconds each
• Total wall-clock time: ~6 seconds (since all five run in parallel)

The speedup is dramatic: roughly 5–6x faster in wall-clock time. The total compute used across all five requests may be similar (or even slightly higher due to overhead), but from the user's perspective, the results come back in a fraction of the time.

Why Modern LLM Infrastructure Enables This

This parallel approach works so well because of how modern LLM serving infrastructure is built. Major providers like OpenAI, Anthropic, Google, and open-source serving frameworks like vLLM and TensorRT-LLM all use a technique called continuous batching (sometimes called in-flight batching) on the server side.

Here is what happens when you send five simultaneous requests to an API endpoint:

1. The server receives all five requests nearly simultaneously.
2. The inference engine batches them together at the GPU level, processing multiple sequences in a single forward pass.
3. Each sequence generates its tokens independently but shares the GPU's compute resources efficiently.

This means your five parallel requests do not compete for resources the way you might expect. The server is already designed to handle concurrent requests efficiently. In many cases, the per-token latency for each individual request barely increases compared to sending a single request in isolation.

vLLM, the popular open-source serving engine used by numerous companies, pioneered PagedAttention specifically to handle this scenario — managing memory for multiple concurrent sequences without waste. TensorRT-LLM from NVIDIA offers similar capabilities optimized for their GPU hardware.

The Prefill vs. Decode Distinction

There is one more nuance worth understanding. LLM inference has two distinct phases:

• Prefill phase: The model processes all input tokens in parallel. This is fast because input tokens can be computed simultaneously using matrix operations that GPUs excel at.
• Decode phase: The model generates output tokens one at a time. This is the bottleneck.

When you batch five questions into one prompt, you create a longer input (more prefill work) AND a much longer output (more decode work). The prefill penalty is usually modest — processing 500 input tokens versus 100 is not a huge difference on modern hardware. But the decode penalty is severe because it is strictly sequential.

When you split into five parallel requests, each request has a short prefill phase and a short decode phase. The server handles the prefill phases quickly (often in a single step per request) and then interleaves the decode steps across all five sequences efficiently.

When Batching Might Still Make Sense

Despite the clear speed advantage of parallel requests, there are legitimate scenarios where batching questions into a single prompt could be preferable:

1. Rate Limits and Cost Constraints

Most LLM API providers impose rate limits — for example, OpenAI's GPT-4o has per-minute request caps that vary by usage tier. If you are already near your rate limit, five requests could trigger throttling. A single batched request uses only one request slot. Additionally, there is a small per-request overhead in input tokens (system prompts, formatting), so batching can be marginally cheaper.

2. Cross-Question Context

If your questions are not truly independent — if the answer to question 3 might benefit from the model having just thought about question 2 — then batching preserves this contextual advantage. However, the premise of this analysis assumes the questions are genuinely unrelated.

3. Simplicity of Implementation

Managing five concurrent async requests, handling partial failures, and aggregating results adds code complexity. For a quick prototype or script where latency is not critical, a single batched request is simpler to implement and debug.

4. Very Short Answers Expected

If each question only needs a 10-token answer, the total decode time for a batched request (say, 80 tokens including formatting) is so small that the latency difference becomes negligible. The overhead of establishing five separate HTTP connections might even negate the parallel advantage in extreme cases.

Practical Implementation Tips

For developers looking to implement the parallel approach, here are key recommendations:

• Use async HTTP clients: Libraries like Python's asyncio with aiohttp, or JavaScript's Promise.all(), make concurrent API calls straightforward.
• Handle failures gracefully: When one of five requests fails, you need retry logic that does not block the other four. Implement independent error handling per request.
• Monitor rate limits: Track your API usage and implement backoff strategies. Most providers return rate-limit headers you can inspect.
• Consider streaming: With parallel requests, you can stream responses back to the user as they complete, providing an even faster perceived experience — the first answer might arrive in 2 seconds while the others are still generating.
• Benchmark your specific use case: The exact speedup depends on your model, provider, prompt length, and expected output length. Run A/B tests with realistic workloads.

The Bigger Picture: Designing for Parallelism

This batching-vs-splitting question reflects a broader architectural principle in AI application design. As LLM-powered applications grow more sophisticated — think multi-agent systems, retrieval-augmented generation pipelines, and complex reasoning chains — the ability to decompose tasks and run them in parallel becomes a critical performance lever.

Frameworks like LangChain and LlamaIndex have built parallel execution into their pipeline architectures for exactly this reason. Microsoft's Semantic Kernel supports concurrent function calling. The trend is clear: modern AI engineering treats LLM calls like any other I/O-bound operation and optimizes accordingly.

The Bottom Line

When you have multiple independent questions for an LLM, the physics of autoregressive decoding strongly favors parallel requests. A single batched prompt forces the model into a long sequential decode that scales linearly with total output length. Parallel requests exploit the server's ability to process multiple sequences concurrently, delivering results in roughly the time it takes to answer just one question.

The rule of thumb is simple: if the questions are independent and latency matters, split and parallelize. Save batching for situations where rate limits, cost, or implementation simplicity take priority over speed.

In an era where every millisecond of LLM latency affects user experience and application viability, understanding this fundamental tradeoff is not optional — it is essential.