LLM Production

LLM Serving Architectures — 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 — vLLM, TGI, TensorRT-LLM, Triton Inference Server
Memory Management — PagedAttention, KV cache optimization, continuous batching
Architectural Patterns — Streaming, load balancing, request routing

The bottleneck in LLM deployment is not inference computation—it 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.

DfLLM Serving

An LLM serving system is a distributed inference infrastructure that manages model loading, request scheduling, memory allocation, and response streaming for large language models, optimizing for throughput, latency, and cost efficiency under concurrent workloads.

The KV Cache Bottleneck

The primary bottleneck in LLM serving is managing the Key-Value (KV) cache, which stores attention state for autoregressive generation.

KV Cache Memory Requirements

M_{kv} = 2 \\times L \\times n_{heads} \\times d_{head} \\times s \\times b

Here,

$M_{kv}$ =Total KV cache memory
$L$ =Number of transformer layers
$n_{heads}$ =Number of attention heads
$d_{head}$ =Dimension per head
$s$ =Sequence length
$b$ =Batch size

KV Cache Size Calculation

For a 70B parameter model with 80 layers, 64 heads, 128 head dimension, batch size 32, and sequence length 4096: M_kv = 2 x 80 x 64 x 128 x 4096 x 32 = 17.2 GB (FP16) This is memory dedicated solely to caching attention states, separate from model weights.

PagedAttention

PagedAttention (vLLM) solves KV cache fragmentation by dividing the cache into fixed-size blocks, analogous to virtual memory paging.

DfPagedAttention

PagedAttention partitions the KV cache into fixed-size blocks (pages) that can be non-contiguous in physical memory. A block table maps logical blocks to physical blocks, enabling memory-efficient KV cache management with near-zero waste from internal fragmentation.

Memory Utilization with PagedAttention

\\eta = \\frac{\\text{Used KV Blocks}}{\\text{Total KV Blocks}} \\geq 1 - \\frac{1}{B_{size}}

Here,

$\eta$ =Memory utilization efficiency
$B_{size}$ =Block size (tokens per block)

Serving Frameworks

vLLM

vLLM is a high-throughput LLM serving engine built on PagedAttention.

vLLM achieves 2-4x higher throughput than naive implementations by eliminating KV cache memory waste and enabling continuous batching. It supports tensor parallelism, pipeline parallelism, and speculative decoding.

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.

Triton excels when you need to serve multiple models simultaneously or require complex request routing. For pure LLM throughput, vLLM or TensorRT-LLM typically outperform Triton's LLM backend.

Serving Patterns

Continuous Batching

Unlike static batching, continuous batching allows new requests to join a batch as soon as slots become available.

Throughput Improvement from Continuous Batching

\\text{Throughput}_{continuous} = \\frac{N \\times T_{gen}}{\\max(T_{gen}) + T_{prefill}}

Here,

$N$ =Maximum concurrent requests
$T_{gen}$ =Generation time per request
$T_{prefill}$ =Prefill time for context

Speculative Decoding

Speculative decoding uses a smaller draft model to propose tokens, which are then verified by the target model in parallel.

DfSpeculative Decoding

Speculative decoding accelerates autoregressive generation by using a lightweight draft model to generate K candidate tokens, which are then verified by the full model in a single forward pass. Verified tokens are accepted; rejected tokens trigger resampling from the corrected distribution.

Request Routing and Load Balancing

Architecture Diagram

┌─────────────────────────────────────────────────────┐
│                   Load Balancer                      │
│         (Round-Robin / Least-Connections)           │
└─────────┬───────────┬───────────┬───────────┬───────┘
          │           │           │           │
    ┌─────┴─────┐┌────┴────┐┌────┴────┐┌────┴────┐
    │  vLLM     ││  vLLM   ││  TGI    ││ TensorRT│
    │  Worker 1 ││ Worker 2││ Worker 1││  Worker │
    └───────────┘└─────────┘└─────────┘└─────────┘

Prefix Caching

For workloads with shared system prompts, prefix caching avoids redundant prefill computation.

Prefill Computation Savings

\\text{Savings} = \\frac{L_{prefix}^2 \\times d}{L_{total}^2 \\times d + L_{suffix} \\times L_{total} \\times d}

Here,

$L_{prefix}$ =Length of cached prefix
$L_{suffix}$ =Length of unique suffix
$d$ =Model hidden dimension

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

Throughput and latency numbers vary significantly based on model size, hardware, batch size, and sequence length. Always benchmark with your specific workload before selecting a framework.

Practice Exercises

Conceptual: Explain why PagedAttention improves throughput compared to naive contiguous KV cache allocation. What are the trade-offs?
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.
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.
Research: Compare the latency profiles of speculative decoding versus standard autoregressive decoding for a 70B model with a 7B draft model.

Key Takeaways:

KV cache memory management is the primary bottleneck in LLM serving
PagedAttention eliminates memory waste through block-based allocation
Continuous batching dramatically improves throughput over static batching
Framework selection depends on hardware, workload, and ecosystem requirements
Speculative decoding can reduce latency without sacrificing output quality

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.

LLM Serving Architectures

LLM Serving Architectures — From Research to Production

LLM Serving Architectures

DfLLM Serving

The KV Cache Bottleneck

KV Cache Memory Requirements

KV Cache Size Calculation

PagedAttention

DfPagedAttention

Memory Utilization with PagedAttention

Serving Frameworks

vLLM

Text Generation Inference (TGI)

TensorRT-LLM

Triton Inference Server

Serving Patterns

Continuous Batching

Throughput Improvement from Continuous Batching

Speculative Decoding

DfSpeculative Decoding

Request Routing and Load Balancing

Prefix Caching

Prefill Computation Savings

Performance Comparison

Practice Exercises

What to Learn Next

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