LLM Request Speed: Batch or Parallel — What Actually Works

LLM inference engines like GPT-4 and Claude generate text using an autoregressive decoding process: one token is produced, appended to the input, and the next token is generated. This loop continues until the final token is produced. As a result, the total number of tokens in an answer directly correlates with the total latency experienced.

When faced with five unrelated questions, you might wonder whether to consolidate them into a single message or dispatch multiple requests simultaneously. Benchmarks and first-principles analysis consistently reveal that splitting requests into parallel independent calls is almost always the faster option.

How LLMs Process Text: Token-by-Token Generation

To grasp the speed differences between batching and splitting, you need to understand the mechanics behind how LLMs generate responses. Modern LLMs employ an autoregressive generation strategy, producing one token at a time. After each token is generated, it is appended to the current input context, and the model generates the next token based on this updated context.

This process continues until the entire response is complete. Consequently:

• - A 100-token answer requires 100 sequential inference steps. - A 500-token answer requires 500 sequential inference steps. - Total answer length directly determines total response latency.

The Longer the Combined Request, the Slower It Becomes

Suppose you combine five independent questions, each requiring approximately 200 tokens to answer, into a single message:

Please answer the following questions separately: 1. [question 1] 2. [question 2] 3. [question 3] 4. [question 4] 5. [question 5]

Under the hood, the LLM must generate a total output volume of roughly 1,000 tokens (200 tokens × 5 questions). Because the model operates autoregressively, these 1,000 tokens are produced sequentially — the 201st token cannot be generated until the first 200 tokens are complete.

This leads to a significant slowdown:

• - Total latency equals ~1,000 × average per-token generation time. - Additional overhead arises from context switching between questions. - Extended key-value (KV) cache processing increases attention computation at each step. - Actual output length often surpasses 1,000 tokens due to formatting, transition phrases, and other elements.

Parallel Independent Requests: Latency Equals the Slowest Call

Alternatively, imagine you dispatch five separate requests for the same five questions, all at the same time:

import asyncio

async def fetch_answer(question):
    """Dispatch a single independent LLM request."""
    response = await client.text_generation(question)
    return response

async def main():
    questions = [q1, q2, q3, q4, q5]
    tasks = [fetch_answer(q) for q in questions]
    results = await asyncio.gather(*tasks)
    return results

Each request independently generates approximately 200 tokens. Provided the LLM service maintains adequate concurrent processing capacity — which all modern providers do — these five requests are handled in parallel.

As a result, the total latency equals the slowest individual request, roughly:

• - 200 × average per-token generation time. - Minimal context switching overhead. - Reduced KV cache processing time.

Why Parallelism Wins: Continuous Batching on GPUs

You might reasonably ask: if you send five simultaneous requests, won’t they simply queue up behind one another, negating any speed advantage? The answer lies in the advanced inference engines powering modern LLMs, such as vLLM, TensorRT-LLM, and Text Generation Inference (TGI). These services implement a sophisticated strategy known as continuous batching.

Here’s how it works:

• - Multiple independent requests share the same GPU matrix operation. - GPUs excel at parallel computing tasks. - Consolidating tokens from five different requests into one batch enables a single forward pass to generate one token per request simultaneously.

• - Dynamic scheduling allocates GPU resources efficiently. - Shorter output length requests finish first. - Their slots are immediately repurposed for new incoming requests.

• - Throughput and latency are decoupled. - Larger batches boost GPU utilization. - More total tokens are processed per unit time.

From the server’s perspective, the difference is stark:

• - Five short parallel requests → GPU performs five-way batched inference, generating five tokens per step. - One long combined request → GPU performs single-sequence inference, generating only one token per step.

The Prefill Phase: Long Inputs Slow Down Both Phases

LLM inference involves two distinct phases:

• - Prefill phase → Process all input prompt tokens to compute the KV cache. - Prefill latency scales roughly linearly with input length. - Decode phase → Generate output tokens one at a time. - Decode latency scales with total output token count.

When you combine five unrelated questions into a single request:

• - Prefill phase → Longer input prompt means longer prefill time. - Decode phase → Longer total output means longer decode time.

Both phases favor splitting requests into parallel independent calls, where shorter inputs are processed more efficiently and outputs are generated faster.

Overlooked Risks of Combined Requests: Quality and Reliability

Beyond speed, batching five unrelated questions into one request introduces several potential quality issues:

• - Attention dilution → More irrelevant context in a single prompt reduces the LLM’s focus on each task. - Research shows that longer irrelevant input prompts lead to lower answer quality, as described in the "Lost in the Middle" phenomenon. - Format confusion → Numbering errors, omissions, or mismatched responses easily occur in multi-part answers. - Error propagation → If the answer to question two is incorrect, the LLM may be influenced in subsequent answers due to its autoregressive nature.

Parallel independent requests completely isolate each question’s context, ensuring the LLM maintains full attention on its task and delivers more reliable results.

Exceptions: When Combining Makes Sense

While parallelism is usually superior, there are specific scenarios where combining multiple questions into one request may be more appropriate:

• - Hidden correlations between questions → Even if they appear unrelated, the LLM might provide more consistent answers by seeing the full context. - For example, different sections of the same analytical report. - - Strict API rate limits → If your service quota allows only three requests per minute, you have no practical choice but to consolidate five questions into one or two calls. - - Network latency significantly exceeds token generation time → If each API call incurs two seconds of network overhead but token generation only takes 0.5 seconds, splitting five times (five × two seconds = ten seconds total network time) might exceed the combined generation time. However, this situation is rare in practice, as modern API network latency typically ranges between 100 and 300 milliseconds — far below token generation time. - - Extremely short answers → If each question only needs a single word or symbol as an answer, the prefill overhead of multiple independent requests may outweigh the benefits, making combined requests more efficient.

Benchmark Your Own Setup to Confirm Best Practices

If you want to empirically validate whether batching or splitting yields faster results for your specific use case, consider this lightweight Python setup:

import asyncio
import time

class LLMBenchmark:
    def __init__(self, model="gpt-4", api_key="your_key"):
        ""Initialize an async LLM benchmark client.""
        self.model = model
        self.api_key = api_key

    async def send_batched_request(self, questions):
        ""Send a single combined request for multiple questions."""
        start_time = time.time()
        response = await self.client.batch_text_generation(questions)
        latency = time.time() - start_time
        return latency, response

    async def send_split_requests(self, questions):
        ""Send parallel independent requests for each question."""
        start_time = time.time()
        tasks = [self.client.text_generation(q) for q in questions]
        responses = await asyncio.gather(*tasks)
        latency = time.time() - start_time
        return latency, responses

async def main():
    questions = ["Q1", "Q2", "Q3", "Q4", "Q5"]
    benchmark = LLMBenchmark()
    
    batched_latency, _ = await benchmark.send_batched_request(questions)
    split_latency, _ = await benchmark.send_split_requests(questions)
    
    speedup = batched_latency / split_latency
    print(f"Approximate speedup from splitting: {speedup:.2f}x")

if __name__ == "__async__":
    asyncio.run(main())

To accurately measure latency differences, you should:

• - Run multiple iterations to average out noise. - - Test with varying question lengths. - - Confirm your LLM provider’s concurrent processing capacity.

The Future of LLM Request Optimization

As LLM inference engines continue to evolve, they will likely introduce more granular control over batch sizing, request prioritization, and dynamic latency optimization. In the meantime, developers can optimize their workflows by:

• - Defaulting to parallel independent requests for unrelated tasks. - - Monitoring API rate limits and adjusting request batching accordingly. - - Benchmarking latency differences between batching and splitting in their specific environments.

The choice between consolidating multiple questions into one request or splitting them into parallel independent calls is not merely a matter of preference — it is dictated by the underlying mechanics of LLMs and their inference engines. Parallelism consistently delivers faster, more reliable, and higher-quality results, making it the clear winner for most practical applications today.

Tomorrow, as these technologies mature further, we may see new strategies that bridge the gap between raw inference speed and contextual accuracy, reshaping how we interact with LLMs at scale.