Latency Optimization for Large Language Models: Streaming, Batching, and Caching

Why Your LLM Feels Slow-And How to Fix It

Imagine asking a chatbot a question and waiting 3 seconds for a reply. You type again. Wait another 2.5 seconds. By the third time, you’ve given up. That’s not frustration-it’s latency. For large language models (LLMs), latency isn’t just an annoyance; it’s a dealbreaker. Users notice delays over 500ms. In real-time applications like customer support bots or AI assistants, anything above 200ms starts hurting engagement. The good news? You can cut that delay by 70% or more using three proven techniques: streaming, batching, and caching.

Streaming: Deliver Words as They’re Generated

Traditional LLMs wait until the entire response is ready before sending anything. That means if your model takes 2 seconds to generate a 100-word reply, the user sees nothing for 2 seconds. Streaming changes that. Instead of waiting, the system sends tokens-words or parts of words-as soon as they’re ready.

This isn’t just about speed. It’s about perception. When users see the first word appear in under 200ms, they feel the system is responsive-even if the full reply takes longer. Companies like Amazon Bedrock use streaming to reduce time-to-first-token (TTFT) by over 97% at the 90th percentile. For a chatbot, that means the user gets a reply before they even finish reading their own question.

Tools like vLLM and NVIDIA’s TensorRT-LLM handle streaming efficiently by overlapping token generation with network transmission. You don’t need a massive GPU setup to start. Even on a single A100, streaming can cut perceived latency by 20-30%. The catch? Your frontend must support chunked responses. Most modern frameworks like React, Vue, or even simple JavaScript fetch() with readable streams can handle it.

Batching: Group Requests to Maximize GPU Use

GPUs are powerful, but they’re expensive. If you run one request at a time, you’re wasting 90% of your hardware. Batching solves this by combining multiple user requests into a single inference pass.

There are two types: static and dynamic. Static batching groups requests ahead of time-like collecting 8 questions and running them together. It’s simple but inflexible. If one request is long, the whole batch waits. Dynamic (or in-flight) batching, used by vLLM and DeepSpeed, constantly adjusts. New requests join the batch as it runs. If one user’s prompt ends early, the GPU immediately starts processing the next one.

According to vLLM’s 2024 benchmarks, dynamic batching boosts throughput by 2.1x compared to static batching at the 95th percentile latency. For a service handling 100 requests per second, that means you can cut your GPU count in half. But there’s a trade-off: batching can increase tail latency. During traffic spikes, some users might wait longer because they’re stuck behind a slow request. That’s why smart systems cap batch sizes and prioritize urgent queries.

Start small. If you’re serving 10-20 requests per second, try batching 4-8 requests. Monitor your 95th percentile latency. If it jumps above 600ms, reduce the batch size. You’re not optimizing for peak throughput-you’re optimizing for user experience.

Caching: Don’t Recalculate What You’ve Already Done

Not every question is new. In customer service bots, 30-50% of queries repeat: “How do I reset my password?” “What’s your return policy?” “Do you ship to Canada?”

Key-value (KV) caching stores the internal attention states from previous responses. When the same or similar prompt comes in again, the model skips the heavy computation and jumps straight to generating the reply. This isn’t storing the answer-it’s storing the model’s internal thinking.

Redis-based KV caches have shown 2-3x speed improvements for repeated queries. Snowflake’s Ulysses technique and FlashInfer’s block-sparse cache formats push this further, cutting long-context latency by up to 30%. For a support bot handling 5,000 daily tickets, caching could mean cutting your inference costs by 40%.

But caching has risks. If you cache too aggressively, you might return outdated or incorrect responses. One Reddit user reported hallucinations when cached attention states mixed with slightly altered prompts. The fix? Use similarity matching, not exact string matches. Tools like FAISS or approximate nearest neighbor search can group semantically similar questions. Also, set memory limits. If your GPU hits 80% cache usage, start evicting older entries. Don’t let caching eat your memory.

Tensor Parallelism: When You Need Raw Speed

Streaming, batching, and caching work on the software side. Tensor parallelism works on the hardware side. It splits the model across multiple GPUs so each one handles a portion of the computation.

For example, if you’re running a 70B parameter model like Llama 3.1, one H100 GPU might struggle. But split it across four H100s with NVLink, and latency drops by 33% at batch size 16. NVIDIA’s 2024 guide shows that going from 2x to 4x parallelism cuts token latency significantly-especially when handling many users at once.

But this isn’t plug-and-play. You need NVLink for fast GPU-to-GPU communication. You need PyTorch and CUDA knowledge to configure it. And you need to accept that debugging becomes harder. As MIT’s Dr. Emily Rodriguez found, parallelized models make tracing errors 3-4x more difficult.

Only use tensor parallelism if you’re serving 50+ concurrent requests and your latency is still above 400ms after optimizing streaming and batching. For most applications, it’s overkill. But for high-volume APIs or enterprise chatbots with 10,000+ daily interactions, it’s essential.

Putting It All Together: The Real-World Stack

Most successful teams don’t pick one technique-they layer them.

Here’s what a production setup looks like in 2026:

1. Start with streaming. Reduce TTFT to under 200ms. This gives users immediate feedback. Use vLLM or Hugging Face TGI.
2. Add dynamic batching. Group requests to maximize GPU usage. Aim for batch sizes of 8-16. Monitor tail latency.
3. Implement KV caching. Cache responses for common questions. Use Redis or in-memory cache with LRU eviction. Set a 20GB limit per GPU for 7B models.
4. Use speculative decoding (optional). Run a smaller model (like Phi-3) to predict the next few tokens. If correct, skip computation. Gains: 2.4x speedup, with only 0.3% accuracy loss.
5. Scale with tensor parallelism (if needed). Only when you’re hitting GPU limits and need sub-150ms latency at scale.

A Fortune 500 company followed this exact path. Their average response time dropped from 850ms to 520ms. Costs fell by 32%. User satisfaction scores rose 18%.

What Not to Do

Optimizing for speed can backfire. Here are the top mistakes:

• Over-caching. Caching similar but not identical prompts causes hallucinations. Always validate cached outputs.
• Too-large batches. A batch size of 32 might boost throughput but make 10% of users wait over 2 seconds. That’s worse than no batching.
• Ignoring memory. KV caches eat RAM. Monitor GPU memory usage. If it’s above 85%, you’re asking for crashes.
• Skipping testing. Don’t assume caching works. Test edge cases: long prompts, special characters, multi-turn conversations.

Dr. Alan Chen from Tribe.ai found that 22% of production LLM failures came from aggressive caching policies. That’s not a bug-it’s a design flaw.

Tools and Frameworks to Use in 2026

Don’t build from scratch. Use battle-tested tools:

For most teams, vLLM is the sweet spot. It’s open-source, well-documented, and handles all three techniques out of the box. AWS Bedrock is best if you want zero infrastructure management. Triton is ideal if you’re already in the NVIDIA ecosystem.

What’s Next? The Future of LLM Latency

By 2026, latency optimization won’t be a feature-it’ll be standard. The next wave includes:

• Adaptive batching: Systems that auto-adjust batch size based on prompt length and traffic.
• Edge-aware deployment: Running smaller models closer to users to cut network delay by 30-50%.
• Predictive decoding: Using ML to guess how long a response will take and pre-allocate resources.

But the biggest change? Hardware-software co-design. NVIDIA’s Blackwell chips, AWS’s Trainium3, and custom ASICs are being built with LLM inference in mind. In five years, you won’t need to tweak batching-you’ll just pick a model and get sub-100ms responses.

For now, focus on the basics. Get streaming right. Tune your batch size. Cache wisely. You don’t need the latest GPU to make your LLM feel fast. You just need to know what to optimize-and what to leave alone.