As organisations increasingly adopt Large Language Models (LLMs) for chatbots, summarisation, generation, and analytics, the real challenge shifts from training models to serving them efficiently in production.
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Unlike traditional ML models, LLMs are:
- Huge (billions of parameters)
- Compute-hungry
- Latency-sensitive
- Expensive to serve at scale
This makes designing a robust LLM serving architecture critical.
In this guide, we’ll break down the three pillars of high-performance LLM serving:
- Batching — maximising GPU throughput
- Caching — reducing redundant computation
- Autoscaling — balancing cost and availability
You’ll learn how modern AI platforms (OpenAI, Anthropic, HuggingFace Text Generation Inference, vLLM, FastAPI-based servers, Ray Serve, Kubernetes, etc.) implement these concepts — and how you can build a scalable system of your own.
Why LLM Serving Is Hard
Before we dive deeper, let’s understand the challenges:
1. High computational cost
Decoding tokens requires multiple GPU operations — even for short queries. Parallelism is limited by model architecture.
2. Highly variable workloads
Traffic may spike during:
- product launches
- chatbot events
- marketing campaigns
LLM servers must be elastic.
3. Strict latency requirements
Even 200–300ms delays can hurt user experience in chat apps.
4. Expensive GPU infrastructure
Idle GPUs waste thousands of dollars each month if not optimised.
This brings us to the core: Batching + Caching + Autoscaling.
1. Batching — The Heart of Efficient LLM Serving
Batching multiple requests together is the MOST important optimisation for LLMs.
Why batching works
Modern GPUs are built for parallel computation. Running 1 request = expensive. Running 20 requests at the same time = much cheaper per request.
Batching transforms:
20 requests → 20 GPU passes
into:
20 requests → 1 GPU pass
Batching Techniques
A. Static Batching
- Predefine batch sizes.
- Wait until the batch is full.
- Pros: Efficient.
- Cons: Adds queue delay.
Use when traffic is high and predictable.
B. Dynamic Batching (most recommended)
Combine requests on the fly based on arrival time and max wait thresholds.
The server:
- checks the queue every X milliseconds
- batches as many requests as possible
- sends to the GPU
Frameworks like:
- vLLM
- Triton Inference Server
- Ray Serve
- Text Generation Inference (TGI)
Already supports dynamic batching.
C. Continuous Batching (state-of-the-art)
Used by vLLM and OpenAI.
Features:
- Add requests mid-generation
- Efficient for token-by-token decoding
- No batching boundaries
This produces:
- Higher throughput
- Lower latency
Batching Best Practices
| Area | Recommendation |
|---|---|
| Max batch size | 16–64 for 7B models, up to 128+ for 70B |
| Max wait time | 10–30ms |
| Queueing algorithm | FIFO with priority for streaming |
| Multi-model batching | Avoid — models differ in shape |
2. Caching — Stop Repeating Work
Many LLM requests are similar, especially in chat or RAG workflows.
Caching avoids repeating expensive computations.
There are three main types of cache:
A. Prompt Caching
If the prompt is identical, serve the previous output.
Useful for:
- system prompts
- RAG questions from large corpora
- Repeated tasks (classification, tagging)
Backend stores:
- prompt hash
- model version
- output tokens
Tools:
- Redis
- Memcached
- Pinecone (for semantic caching)
B. KV Cache Reuse (Transformer-specific)
During LM decoding, each token produces key/value tensors (“KV cache”).
For repeated prefixes (like prompts), we can reuse previous computation.
This reduces:
- decoding latency
- GPU memory usage
- cost per token
vLLM pioneered “PagedAttention,” allowing huge KV caches without fragmentation.
This is the #1 optimisation for high-throughput LLM serving.
C. Semantic Caching
Match new queries with semantically similar embeddings.
For example:
“Explain transformers” ≈ “What are transformer models?”
If similarity > threshold → return cached output.
Tools:
- Pinecone
- Weaviate
- Qdrant
- Redis Vector Store
3. Autoscaling — Scale Up When Busy, Scale Down When Idle
Autoscaling ensures:
- High availability during peak usage
- Low cost during low traffic
LLM inference servers scale differently from typical web apps because:
One GPU → One model → One replica
So the rules are special.
Types of Autoscaling
A. Horizontal Autoscaling (HPA)
Add/remove GPU instances based on metrics:
- queue size
- GPU utilization
- requests per second
- average token throughput
Tools:
- Kubernetes HPA
- KServe
- Ray Serve Autoscaler
- AWS Sagemaker Autoscaling
B. Vertical Autoscaling
Change GPU type based on demand.
Examples:
- switch to A100/H100 during high-traffic
- switch to L4/T4 during off-hours
Most useful for enterprise cost optimisation.
C. Event-driven Autoscaling
For serverless LLM setups:
Use:
- GCP Cloud Run
- AWS Lambda + Inferentia
- Modal
- Anyscale Endpoints
Triggered by:
- Function invocations
- Queue messages
- Pub/Sub events
Best for pipelines and background processing.
Autoscaling Policies
| Policy Type | Example | Best Use Case |
|---|---|---|
| Time-based | Nightly scale-down | predictable traffic |
| Metric-based | Queue > 50 → add node | chatbots |
| Hybrid | combine time + metrics | enterprise workloads |
Designing The Complete LLM Serving Architecture
A modern production system may look like this:
Architecture Overview
Client → API Gateway → Dynamic Batching Queue → GPU Workers → KV Cache → Output
↑
| Autoscaler
Detailed Components
1. API Gateway
- Auth
- Rate limiting
- Routing
- Monitoring
2. Request Queue + Scheduler
- dynamic batching
- prioritization
- max-wait thresholds
3. GPU Worker Pool
- LLM runtime (vLLM / TGI / TensorRT)
- KV cache memory
- multi-instance GPU loading
4. Caching Layer
- Redis for exact caching
- Vector DB for semantic caching
- local KV cache for decoding
5. Autoscaler
- monitors queue depth
- spins workers up/down
- shifts to cheaper GPUs
6. Observability Stack
Metrics:
- tokens/sec
- GPU utilization
- latency (p50–p99)
- queue wait time
Tools:
- Prometheus
- Grafana
- OpenTelemetry
Example Setup: vLLM + FastAPI + Kubernetes
This is a popular stack used by startups and mid-sized companies.
Why vLLM?
- continuous batching
- advanced KV caching
- 2–10× higher throughput than naive serving
Workflow
- FastAPI handles incoming requests
- Requests enter a queue
- vLLM batches them dynamically
- GPU decodes tokens
- Autoscaler scales replicas based on queue pressure
This architecture can handle:
- 1000+ concurrent users
- sub-second response latency
- up to 80–90% cost optimisation vs naïve serving
Best Practices Cheat Sheet
Batching
- 20–30ms max wait
- batch as large as GPUs can handle
- enable continuous batching when available
Caching
- always enable KV caching
- enable semantic caching for RAG systems
- Invalidate cache on model version updates
Autoscaling
- scale on queue length, not CPU
- Use warm pools to avoid cold GPU starts
- Set min replicas to avoid warm-up delays
Conclusion
Designing an efficient LLM serving architecture is essential for delivering fast, reliable, and cost-effective AI applications.
By mastering the three pillars:
- Batching
- Caching
- Autoscaling
You can reduce latency, improve throughput, and dramatically cut compute costs, while ensuring your LLM-powered applications scale effortlessly.
This strategy is used by industry leaders like OpenAI, Anthropic, MosaicML, and HuggingFace, and now you can apply the same principles to your own infrastructure.
Parvesh Sandila is a passionate web and Mobile app developer from Jalandhar, Punjab, who has over six years of experience. Holding a Master’s degree in Computer Applications (2017), he has also mentored over 100 students in coding. In 2019, Parvesh founded Owlbuddy.com, a platform that provides free, high-quality programming tutorials in languages like Java, Python, Kotlin, PHP, and Android. His mission is to make tech education accessible to all aspiring developers.