LLM Production
LLM Serving Architectures β From Research to Production
Serving LLMs in production requires specialized architectures optimized for throughput, latency, and cost. This guide covers the major serving frameworks and architectural patterns used to deploy LLMs at scale.
- Serving Frameworks β vLLM, TGI, TensorRT-LLM, Triton Inference Server
- Memory Management β PagedAttention, KV cache optimization, continuous batching
- Architectural Patterns β Streaming, load balancing, request routing
The bottleneck in LLM deployment is not inference computationβit is memory management.
LLM Serving Architectures
Deploying LLMs in production environments presents unique challenges that traditional model serving frameworks cannot address. The key constraints are memory-bound operations (particularly KV cache management), variable-length sequences, and the need for high-throughput concurrent inference.
The KV Cache Bottleneck
The primary bottleneck in LLM serving is managing the Key-Value (KV) cache, which stores attention state for autoregressive generation.
PagedAttention
PagedAttention (vLLM) solves KV cache fragmentation by dividing the cache into fixed-size blocks, analogous to virtual memory paging.
Serving Frameworks
vLLM
vLLM is a high-throughput LLM serving engine built on PagedAttention.
Key Features:
- PagedAttention for efficient KV cache management
- Continuous batching (dynamic batching)
- Tensor and pipeline parallelism
- Speculative decoding
- Prefix caching (automatic prompt caching)
Text Generation Inference (TGI)
TGI by Hugging Face provides production-ready LLM serving with optimized CUDA kernels.
Key Features:
- Flash Attention integration
- Quantization support (GPTQ, AWQ, bitsandbytes)
- Token streaming
- Guided generation (JSON schema enforcement)
- Watermarking
TensorRT-LLM
NVIDIA's TensorRT-LLM provides highly optimized inference using NVIDIA's compiler stack.
Key Features:
- Graph-level optimizations (kernel fusion, layout optimization)
- FP8 and INT4 quantization on Hopper/Ada GPUs
- In-flight batching
- Multi-GPU tensor parallelism
Triton Inference Server
NVIDIA Triton provides a model-agnostic serving platform with dynamic batching.
Serving Patterns
Continuous Batching
Unlike static batching, continuous batching allows new requests to join a batch as soon as slots become available.
Speculative Decoding
Speculative decoding uses a smaller draft model to propose tokens, which are then verified by the target model in parallel.
Request Routing and Load Balancing
Prefix Caching
For workloads with shared system prompts, prefix caching avoids redundant prefill computation.
Performance Comparison
| Framework | Throughput (tok/s) | Latency (ms/tok) | Memory Efficiency | Best For |
|---|---|---|---|---|
| vLLM | High | Medium | Excellent | General LLM serving |
| TGI | Medium-High | Low | Good | HuggingFace ecosystem |
| TensorRT-LLM | Highest | Lowest | Excellent | NVIDIA hardware |
| Triton | Medium | Medium | Good | Multi-model serving |
Practice Exercises
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Conceptual: Explain why PagedAttention improves throughput compared to naive contiguous KV cache allocation. What are the trade-offs?
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Mathematical: Calculate the KV cache memory required for a 13B parameter model with 40 layers, 40 attention heads, 128 head dimension, serving 64 concurrent requests with 2048 token sequences.
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Practical: Deploy a 7B model using vLLM with continuous batching. Measure throughput as a function of concurrent requests (1, 4, 8, 16, 32) and identify the saturation point.
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Research: Compare the latency profiles of speculative decoding versus standard autoregressive decoding for a 70B model with a 7B draft model.
What to Learn Next
-> LLM Monitoring and Observability Logging, tracing, metrics, and drift detection for production LLM systems.
-> Cost Optimization for LLMs Token economics, caching strategies, and batching for cost efficiency.
-> LLM Security Best Practices Protecting LLM systems from adversarial attacks and data privacy risks.
-> LLM Versioning and Rollouts Model versioning, A/B testing, and gradual rollout strategies.
-> Multi-Tenant LLM Systems Tenant isolation, resource sharing, and customization at scale.
-> LLM Evaluation in Production Online evaluation, user feedback loops, and quality assurance.