Inference Server

Full definition

Mature inference servers include NVIDIA Triton, NVIDIA TensorRT-LLM, vLLM, TGI (Text Generation Inference), and llama.cpp. They differ on supported hardware, throughput optimizations (continuous batching, paged attention, speculative decoding), and operational features (multi-model hosting, A/B routing, observability). Choice of inference server can change throughput severalfold on the same hardware: vLLM delivered 2–4× the throughput of FasterTransformer and Orca at the same latency (Kwon et al., SOSP 2023).

Why it matters

For self-hosted AI, the inference server is one of the highest-impact cost optimizations. A team running Llama 3 70B on vLLM with continuous batching can serve several times the requests per GPU of a naive PyTorch deployment (the vLLM team measured up to 24× the throughput of Hugging Face Transformers on LLaMA 7B and 13B), multiplying the return on the same hardware investment.

Example

A SaaS company switches its Llama 3 70B endpoint from naive Hugging Face Transformers serving to vLLM with paged attention. p95 latency drops 35%, throughput rises 6×, and per-token cost falls below the public OpenAI API's rate.

Next steps

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Related terms

• Inference
  Inference is the process of running a trained AI model on new, unseen inputs to produce predictions, classifications, or generated content — the part that runs every time a user interacts with the system.
• Inference Cost
  Inference cost is the dollar cost of running a trained AI model in production — per request, per user, or per business outcome — and the operating expense that determines whether an AI feature has positive unit economics at scale.
• KV Cache
  The KV (Key-Value) cache stores the attention keys and values from already-processed tokens so an LLM can generate each new token without recomputing past work — the single largest consumer of GPU memory during LLM inference, and the primary lever for serving throughput.
• GPU (in AI Context)
  A GPU (Graphics Processing Unit) is a massively parallel processor that, for AI workloads, executes the matrix multiplications at the heart of neural networks many times faster than a CPU — and the dominant hardware for both training and inference of modern AI models.
• MLOps
  MLOps is the discipline of operating machine-learning and AI systems reliably in production — covering data pipelines, model training, deployment, monitoring, drift detection, governance, and incident response — analogous to DevOps for traditional software.

More infrastructure terms

• Edge AI
  Edge AI is the practice of running AI models on local devices — smartphones, vehicles, sensors, factory equipment, AR/VR headsets — instead of the cloud, in order to deliver lower latency, stronger privacy, and continued operation when offline.
• Model Serving
  Model serving is the runtime infrastructure that hosts a trained model and exposes it as an API for low-latency online inference at scale, handling batching, autoscaling, GPU sharing, versioning, and routing.