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 simple question, and waiting five seconds for a reply. You tap again. Nothing. Then, suddenly, it starts typing - one word at a time - like a tired typist. That’s not a glitch. That’s latency. And if you’re running a customer service bot, a real-time assistant, or any LLM-powered app, this delay is killing your users.

Studies show that users notice delays over 500ms. Beyond that, engagement drops. If your first token takes longer than 200ms, you’re already losing people. The good news? You don’t need a bigger model. You need smarter inference. Three techniques - streaming, batching, and caching - can cut response times by 60% or more without touching your model weights.

Streaming: Deliver Words as They’re Born

Traditional LLMs wait until the whole response is generated before sending anything. That’s like baking a cake and only serving it when the oven timer dings. Streaming changes that. Instead of holding back, the system sends each new token the moment it’s ready.

This isn’t just about feeling faster. It’s about perception. When users see the first word appear in 80ms, they feel like the system is responsive - even if the full answer takes 1.2 seconds. That’s the magic of time-to-first-token (TTFT). Companies like Amazon Bedrock cut TTFT P90 by 97% just by enabling streaming.

Real-world impact: A support chatbot using streaming saw 31% fewer users abandoning conversations mid-response. Why? Because they felt heard from the first word.

Tools like vLLM and NVIDIA’s TensorRT-LLM make streaming easy to enable. But don’t assume it’s free. Streaming increases memory pressure because the system must hold partial states for multiple concurrent requests. You’ll need to monitor GPU memory usage closely. If you’re running 7B-parameter models, expect 25-30GB of VRAM per GPU just for active streams.

Batching: Turn One Request Into Ten

GPUs are powerful, but they’re lazy if you only give them one job at a time. Batching is the art of grouping multiple user requests together and processing them in parallel. Static batching means you collect five or ten requests, then run them together. Dynamic (or in-flight) batching is smarter: it keeps adding new requests to the batch as long as there’s room, and processes them as soon as the GPU is ready.

Here’s the math: At batch size 1, a 7B model might generate 15 tokens per second. At batch size 16, that same GPU can hit 70 tokens per second. That’s a 3.6x throughput boost. vLLM’s continuous batching achieved 2.1x higher throughput than static batching at the 95th percentile latency, according to 2024 benchmarks.

But there’s a catch. Batching can make tail latency worse. If one user sends a 2,000-token prompt, everyone else in the batch waits. During traffic spikes, this can push response times from 300ms to 600ms. That’s why smart systems use adaptive batching - like Snowflake’s Ulysses - which splits long prompts across multiple GPUs to keep the batch moving.

Best practice: Start with batch sizes of 4-8. Monitor your 95th percentile latency. If it jumps above 500ms during peak hours, scale up GPU count or reduce max batch size. Don’t chase 100% GPU utilization - chase consistent response times.

Caching: Don’t Answer the Same Question Twice

How many times does a user ask, “What’s your return policy?” Or “How do I reset my password?” In customer support, these questions repeat. Every time the model recalculates the attention weights for those prompts, you’re wasting compute.

Key-value (KV) caching solves this. It stores the computed attention states from past prompts. When the same (or similar) prompt comes back, it skips the heavy lifting and starts generation from the cached state. Redis-based KV caches have shown 2-3x speedups for repetitive queries.

FlashInfer, a 2024 innovation, takes this further. It uses block-sparse cache formats and JIT compilation to cut inter-token latency by up to 69%. That means faster replies even for long conversations.

But caching has risks. If you cache too aggressively, you risk hallucinations. One Reddit user reported that cached responses started mixing up facts when prompts were slightly reworded. That’s because caching assumes identical inputs. If a user types “How do I reset my password?” and then “How do I reset my account password?”, the system might reuse an old cache - and give a wrong answer.

Solution: Use similarity matching, not exact string matching. Tools like Clarifai recommend caching only when cosine similarity between prompts exceeds 0.92. Also, set memory limits. Once GPU memory hits 80%, start evicting the oldest or least-used caches. Don’t let caching become your bottleneck.

When to Use What - And When to Avoid Them

Each technique has its sweet spot:

• Streaming is non-negotiable for chatbots, voice assistants, or any real-time interface. It’s the first thing you should enable.
• Dynamic batching is ideal for API services with unpredictable traffic - think SaaS platforms with 100+ concurrent users.
• KV caching shines in customer service, FAQ bots, or any app with repetitive queries. Avoid it if your prompts are highly unique.

Don’t combine all three blindly. A 2024 case study from a Fortune 500 company found that enabling speculative decoding (a technique that uses a smaller model to guess the next tokens) improved speed by 2.4x - but increased hallucination rates by 1.8%. They turned it off after two weeks of user complaints.

Start simple: Enable streaming first. Measure your TTFT. Then add dynamic batching. Monitor how your 95th percentile latency changes. Finally, test KV caching on a subset of queries. Track error rates. If your accuracy drops more than 0.5%, scale back.

Hardware and Costs: What You Really Need

You don’t need an H100 cluster to start. A single A100 (40GB) can handle streaming and batching for 7B-13B models with decent throughput. But if you’re serving 100+ requests per minute, you’ll need 2-4 H100s with NVLink for tensor parallelism.

Tensor parallelism splits the model across multiple GPUs. NVIDIA’s data shows it cuts latency by 33% at batch size 16 - but only 12% at batch size 1. So if your app gets one user at a time (like a mobile app), skip it. Save it for enterprise APIs.

Memory is your silent enemy. Each 7B model with KV cache needs 25-30GB of GPU memory. At 16 concurrent streams, you’re using 500GB of VRAM. That’s two H100s right there. Budget for it.

Cloud options like AWS Bedrock and Azure ML handle this for you. They auto-scale batching and caching. But if you’re running your own infrastructure, expect to spend 2-4 weeks tuning vLLM or Triton Inference Server. Most teams underestimate the debugging time. One GitHub issue found 47% of KV cache problems were memory fragmentation errors.

Real-World Results: Numbers That Matter

Here’s what actual teams achieved in 2024:

• Amazon Bedrock reduced TTFT P90 by 97% for Llama 3.1 70B using optimized streaming + batching.
• vLLM users cut 95th percentile latency from 1,200ms to 420ms with continuous batching - a 65% improvement.
• Snowflake’s Ulysses processed long-context prompts 3.4x faster while keeping GPU utilization above 85%.
• FlashInfer reduced inter-token latency by 29-69% on H100s using optimized cache formats.

These aren’t lab results. These are production gains. And they all came from tuning the inference pipeline - not the model itself.

What’s Next? The Future of LLM Latency

By 2026, latency optimization won’t be a feature - it’ll be the default. New tools are emerging:

• Adaptive batching that auto-adjusts based on prompt length (Snowflake, late 2024).
• Edge-aware deployment that routes requests to the nearest server, cutting network latency by 30-50%.
• Predictive scheduling that guesses how long a response will take and pre-allocates resources.

But the biggest shift? Hardware-software co-design. NVIDIA’s TensorRT-LLM 0.9.0, released in December 2024, now includes built-in speculative decoding. Cloud providers are bundling caching and batching into their APIs. You won’t have to build it - you’ll just turn it on.

Still, don’t ignore the human side. Dr. Alan Chen of Tribe.ai warns: “22% of our production failures came from aggressive caching that didn’t handle edge cases.” Optimization isn’t about speed alone. It’s about reliability.

Where to Start Today

Follow this simple roadmap:

1. Enable streaming in your LLM inference stack. Measure TTFT. Aim for under 200ms.
2. Switch from static to dynamic batching. Use vLLM or Triton. Watch your 95th percentile latency.
3. Implement KV caching for repetitive prompts. Use similarity thresholds, not exact matches.
4. Monitor memory usage. If GPU memory hits 80%, enable cache eviction.
5. Test for hallucinations. If accuracy drops, reduce caching or disable speculative decoding.

You don’t need a PhD to do this. But you do need to measure. Track TTFT, OTPS, and error rates. Without metrics, you’re guessing. With them, you’re optimizing.

Frequently Asked Questions