The Complete Guide to ML Model Serving
Architectures, Optimizations & Operations for Production AI
From training to serving—the real challenge begins when your model goes live. Master the discipline that converts frozen weights into production-grade, revenue-generating AI systems.
The lifecycle of a machine learning model splits into two distinct phases: the computationally intensive process of training and the operational, reliability-focused phase of serving (inference).
While training optimizes weights via gradient descent, serving applies these frozen capabilities to new data in production. In 2025, model serving has evolved from simple REST API wrappers to complex distributed systems orchestrating multi-modal LLMs, autonomous agents, and real-time decision engines.
What Makes Serving Different
Training is about optimization—minimizing loss over hours or weeks. Serving is about reliability—responding to millions of requests with consistent latency, managing GPU memory efficiently, and maintaining uptime SLAs. The infrastructure, metrics, and engineering challenges are fundamentally different.
Serving Paradigms
The selection of a serving paradigm is the foundational architectural decision that dictates infrastructure costs, latency profiles, and system complexity. Four primary paradigms dominate the modern landscape.
Serving Paradigm Explorer
Select a paradigm to explore its characteristics
Real-Time (Online)
User Action / API Call
Critical (ms to s)
Variable (bursty)
Immediate
Architecture Pattern
Real-Time Metrics
For LLMs, real-time performance is quantified by — latency before the user sees the first character—and , which governs generation smoothness. A typical chatbot SLA is TTFT under 200ms.
Tail Latency
The critical engineering challenge is minimizing — ensuring even the 99th percentile of requests are served quickly despite "noisy neighbor" effects in multi-tenant cloud environments.
Privacy & Confidential Computing
Traditional security protects data at rest (disk encryption) and in transit (TLS). But the unique vulnerability of ML inference lies in data in use. To perform matrix multiplications, data must be decrypted in memory—creating exposure windows.
addresses this gap by isolating computation within hardware-protected enclaves called . The NVIDIA H100 was the first GPU architecture to support this natively.
Zero-Trust Inference Flow
Hardware-based security with Confidential Computing
Step 1: Enclave Initialization
Inference server boots inside a Confidential VM (CVM) with AMD SEV-SNP or Intel TDX protection.
Trusted Execution Environment Architecture
Data-in-Use Protection
Memory is encrypted at the hardware level—inaccessible to OS, hypervisor, or cloud admins.
Zero Trust Model
Infrastructure provider is assumed untrusted. Client verifies enclave integrity before sending data.
GDPR/HIPAA Compliant
Process sensitive data in public cloud while maintaining technical compliance with data sovereignty requirements.
CPU-Based Isolation Technologies
AMD SEV-SNP
provides VM-level isolation where memory is opaque to the hypervisor. Used for "Confidential Containers" in Kubernetes.
Intel TDX
creates secure "Trust Domains" with encrypted memory protected from the platform. Powers Azure Confidential VMs.
The GPU Hierarchy
Hardware selection dominates the economics of model serving. The remains premier for LLMs, but its scarcity and cost drive adoption of alternatives like and .
GPU Hierarchy Compare
Compare performance, cost, and capabilities
H100
NVIDIA H100 SXM5
A100
NVIDIA A100 80GB
Cost/Performance Analysis
H100 delivers 233 TFLOPS/$
A100 delivers 97 TFLOPS/$
GPU Virtualization & Partitioning
To maximize ROI on expensive GPUs, single-tenant allocation is often replaced by multi-tenancy. Three mechanisms exist: , , and .
GPU Virtualization Explorer
Compare MIG, Time-Slicing, and MPS strategies
Multi-Instance GPU
Hardware partitioning of SMs, memory, and cache
Up to 7 instances
Very Low
Multi-tenant SaaS, strict SLAs, mixed workload clusters
- •Only A100/H100
- •Fixed partition sizes
- •Reduces available memory per instance
Visual Representation
Token Economics vs. Iron Economics
The prevailing public model—paying per token—scales linearly. The millionth token costs the same as the first. Private hosting involves high upfront costs but drives marginal inference cost toward zero.
The break-even arrives faster than most expect. For organizations generating over 100K-1M requests/day, self-hosting typically wins on total cost of ownership.
Inference Cost Calculator
API pricing vs. self-hosting economics
Workload Profile
Model Class
API Provider
Self-Hosted
Self-hosting saves 87% annually
At 500K requests/day, save $755.6K/year by self-hosting
Semantic caching can reduce API calls by 20-30% for common queries.
Route simple queries to cheap models, complex ones to frontier models.
Use spot/preemptible instances for 60-90% savings on batch workloads.
* Estimates based on typical cloud pricing. Actual costs vary by provider, region, and volume discounts.
Serving Runtimes Deep Dive
The "Serving Runtime" executes the neural network's computational graph. In 2025, the ecosystem has bifurcated: LLM-specific engines like and optimize for autoregressive generation, while general-purpose runtimes handle diverse workloads.
The Memory Bottleneck
LLM inference is memory-bound, not compute-bound during decoding. Each token requires reading the entire from memory. (vLLM's innovation) manages this memory like OS virtual memory, eliminating fragmentation for 2-4x higher throughput.
Serving Runtime Benchmark
Compare performance, flexibility, and features
Versatile Large Language Model
PagedAttention eliminates KV cache fragmentation, achieving 2-4x higher throughput
High-throughput LLM serving, production chatbots, API endpoints
| Runtime | Latency | Throughput | Flexibility | Focus |
|---|---|---|---|---|
| vLLM | 85 | 95 | 80 | LLM Serving |
| TensorRT-LLM | 98 | 98 | 50 | Maximum Performance |
| TGI | 80 | 85 | 75 | Production-Ready |
| TorchServe | 70 | 70 | 95 | PyTorch Native |
| Ray Serve | 75 | 80 | 100 | Composite AI |
| ONNX Runtime | 80 | 75 | 90 | Universal |
When to Use What
High-throughput production LLM serving. OpenAI-compatible API.
Lowest latency on NVIDIA silicon. Compiler-based optimization.
Composite AI, multi-model pipelines, fractional GPU allocation.
Quantization & Optimization
To make serving economically viable, engineers compress models through quantization—reducing precision from 16-bit to 8-bit or 4-bit. Modern techniques like and preserve accuracy while dramatically reducing memory footprint.
Quantization Explorer
Compare precision trade-offs and memory savings
Memory Footprint
GPU Compatibility
AWQ (4-bit)
Activation-aware weight quantization protecting salient weights
Any GPU (CUDA)
FP8: Best Quality
Native H100 format. Nearly lossless with 2x compression. Use when you have the hardware.
AWQ: Best Balance
4x compression with 97% accuracy. Protects important weights. Most versatile choice.
GPTQ: Consumer HW
4-bit inference on RTX 4090s. Hessian-based optimization for consumer deployments.
FP8: The New Standard
is native to H100 GPUs. Unlike INT8, its floating-point nature naturally represents neural network value ranges. Nearly lossless with 2x compression.
S-LoRA: Multi-Tenant Efficiency
hosts thousands of adapters on a single GPU, swapping them into VRAM on-demand. One endpoint, infinite tasks.
Speculative Decoding
exploits the memory-bandwidth bottleneck. A small draft model generates candidate tokens, then a large verifier validates them in parallel—achieving 2-3x speedup by trading compute for bandwidth.
Speculative Decoding Demo
Watch draft + verify parallelism in action
Draft Model (7B)
FastGenerates candidate tokens quickly but with lower accuracy
Verifier Model (70B)
AccurateVerifies all draft tokens in parallel (single forward pass)
Generated Output
1. Draft Phase
Small model (e.g., 7B) quickly generates N candidate tokens autoregressively.
2. Verify Phase
Large model (e.g., 70B) verifies ALL candidates in a single parallel forward pass.
3. Accept/Reject
Verified tokens are accepted. Rejected tokens trigger correction. 2-3x overall speedup.
LLM decoding is memory-bound during single-token generation— the GPU has excess compute capacity. Speculative decoding leverages this by trading extra compute (verifying multiple tokens) for reduced memory bandwidth (fewer large-model forward passes).
Edge AI & Mobile Inference
pushes computation to the device—eliminating network latency and keeping data private. The constraints are severe: limited battery, thermal envelopes, and memory capacity.
ExecuTorch
. Ahead-of-Time compilation to a lightweight (<50KB) C++ executor.
Android, iOS, EmbeddedCoreML
. Leverages the Neural Engine (ANE). INT4 support enables 7B models on iPhone 15 Pro.
iOS, macOS, visionOSTensorFlow Lite
. Uses Delegates to offload to GPU, DSP, or NPU.
Android, Linux, IoTQuantization for Edge
Post-Training Quantization (PTQ)
Convert FP32 → INT8 after training. Simple but may degrade accuracy for sensitive models.
Quantization-Aware Training (QAT)
. Recovers up to 96% of PTQ's accuracy loss. Essential for mobile.
Deployment Strategies & MLOps
Deploying a model is just the beginning. "Day 2" operations focus on reliability, observability, and safe rollouts. Risk management relies on established patterns: , , and Blue/Green switching.
ML Deployment Strategies
Compare risk management approaches for model updates
Canary Rollout
Gradual rollouts, iterative improvements, gathering user metrics
Fast
~5-10% extra compute
Medium
Advantages
- +Limited blast radius
- +Real user feedback
- +Gradual confidence building
- +Lower cost than shadow
Limitations
- -Some users affected by issues
- -Requires traffic splitting infra
- -Metric aggregation complexity
- -Rollback affects some users
Traffic Flow
Stable
Canary
Recommendation
For ML model deployments, start with Shadow Deployment for new architectures, then use Canary Rollouts for iterative improvements. Reserve Blue/Green for major version changes when you have high confidence.
ML Observability Stack
Latency (P50/P99), throughput, GPU utilization, VRAM saturation.
—data drift and concept drift monitoring.
Hallucination rate, token usage, trace visualization (Langfuse, Arize).
RAG & Agentic Architectures
Modern serving rarely involves a single model endpoint. systems ground LLM responses in proprietary data, while agents introduce loops, decision-making, and multi-step tool execution.
RAG Pipeline Architecture
Retrieval-Augmented Generation serving flow
Query
User query is embedded into a vector representation
query = "What are the side effects of ibuprofen?"
embedding = embed_model.encode(query) # [0.023, -0.412, ...]Vector DB
Milvus / Pinecone
Embedding Model
E5 / BGE / Cohere
Reranker
Cross-Encoder / Cohere
LLM
GPT-4 / Claude / Llama
Hybrid Search
(semantic) with sparse BM25 (keyword) for superior retrieval. Don't miss exact term matches.
Durable Execution
Agents may run for hours. persists state after every step—if the worker crashes, resume exactly where you left off.
LangGraph: Agents as State Machines
models agents as nodes and edges—a graph of actions and decisions. Deployed to Kubernetes with persistent checkpointing (Postgres), it enables long-running, stateful "human-in-the-loop" interactions. The standard for production agent architectures.
Conclusion: From Weights to Value
The landscape of ML Model Serving in 2025 is defined by specialization and security. The "Flask app wrapper" era is over.
Successful deployment now requires: leveraging Confidential Computing for sensitive data, specialized engines like vLLM and TensorRT-LLM for GPU efficiency, quantization for memory optimization, and durable orchestration for agentic workflows.
As models scale in size and capability, the serving layer remains the critical bridge— converting potential intelligence into tangible business value.
Training creates potential.
Serving creates value.