What Is an AI Inference Stack?
An AI inference stack is the full chain of software and hardware that turns a user request into a model's output — not just “a GPU,” but every layer between a product and the silicon it actually runs on. Most write-ups describe it as three or five loosely-defined layers. Here it's seven concrete ones, each with real, linkable examples, because the layers you can't name are the ones that cause incompatible deployments.
Prefer to click through it instead of reading? Trace your own stack in the interactive browser.
Application Type
The top of the stack is the product itself — the archetype shapes everything below it. A conversational assistant, an agent that calls tools in a loop, and a batch document-summarization pipeline all eventually hit a model, but they have very different latency, concurrency, and context-length needs, which is what drives the choices at every layer beneath them.
Inference Workload
The application archetype maps to one or more concrete inference workloads — the actual computation being run. Text generation, retrieval-augmented generation, embeddings, image/video synthesis, and speech all have different compute profiles (autoregressive decoding vs. one-shot encoding vs. iterative diffusion), which is the first real fork in which frameworks and hardware make sense.
Inference Frameworks
Inference frameworks are the runtimes that actually load a model's weights and expose a serving API — continuous batching, KV-cache management, quantization, and speculative decoding all live here. This is also the first layer where the choice starts to depend on the silicon underneath: some frameworks are vendor-neutral, others are fused tightly to one architecture.
Orchestration
A single framework instance rarely runs alone in production. The orchestration layer schedules requests across a fleet of model replicas, handles autoscaling, and — increasingly — does payload-aware routing so long and short requests, or requests needing different model variants, land on the right backend instead of a single round-robin queue.
Hardware Abstraction
Underneath orchestration sits the hardware abstraction layer: the compute API and driver stack that lets the software above target a chip without hand-writing kernels for it. Each silicon vendor has its own — this is the layer that actually locks a framework build to a specific accelerator family, and it's why 'GPU' isn't a single target.
Silicon
The physical accelerator chip — memory capacity and bandwidth here set the ceiling on model size and batch size, and architecture generation determines what quantization formats and attention kernels are even available. This is usually the layer people mean when they say 'what GPU do I need,' but by itself it doesn't determine what will run on it.
Cloud Instances
Finally, the silicon has to be provisioned somewhere — as a managed cloud instance family, a bare-metal box, or an on-prem cluster. The same chip is often available through multiple providers with different networking, availability, and instance shapes, which is a separate decision from which chip to use in the first place.
See It End to End
Rather than take a layer-by-layer breakdown on faith, here's how two real, cited vendor blueprints thread through all seven — see the full set at reference architectures.
Generative AI inference architecture and best practices
AWSAWS's own prescriptive guidance covers the full decision space rather than one fixed stack — it explicitly compares vLLM, TensorRT-LLM, and Triton, and NVIDIA GPUs against AWS's own Trainium/Inferentia silicon, across self-managed Kubernetes, ECS, and SageMaker.
Enterprise RAG pipeline
NVIDIANVIDIA's official blueprint for enterprise RAG: NIM-packaged models behind a LangChain orchestrator, with a vector database (Milvus or Elasticsearch) for retrieval. Silicon isn't pinned in the blueprint itself — it's specified per-model in NIM's separate support matrix.
Common Questions
- What's the difference between an inference stack and a training stack?
- A training stack is optimized for throughput on large batches over many hours or days, using frameworks built around gradient computation and checkpointing. An inference stack is optimized for latency and cost per request on a model whose weights are already fixed — that's why it has its own frameworks, orchestration layer, and often its own silicon (like AWS Inferentia, which is inference-only).
- Do I need Kubernetes for inference?
- No — it's one orchestration option among several. Triton Inference Server and Ray Serve can run standalone, and Kubernetes + NIM is a specific pattern for containerized, autoscaled deployments. Which one fits depends on whether you're running a handful of replicas or a large multi-tenant fleet.
- What's the difference between vLLM and TensorRT-LLM?
- vLLM is a PagedAttention-based serving engine with continuous batching and an OpenAI-compatible API, designed to run broadly across hardware. TensorRT-LLM is NVIDIA's fused-kernel inference library, optimized specifically for Hopper and Blackwell GPUs with FP8/INT4 quantization — it trades portability for lower latency on NVIDIA silicon specifically.
- Does the inference stack change across GPU vendors?
- Yes, starting at the hardware abstraction layer. NVIDIA GPUs run on CUDA, AMD GPUs on ROCm, Google TPUs on XLA, and AWS Trainium/Inferentia on the Neuron SDK — a framework built against one usually needs a different build, or a different framework entirely, to target another.