LLM Serving Infrastructure Analysis

Executive Summary

Modern LLM serving stacks converge on three strong options. Your best fit depends on model type, latency/SLA targets, batch characteristics, and the level of control you want over scheduling and memory.

vLLM: optimized KV‑cache & continuous batching deliver excellent throughput/latency for decoder‑only models with minimal integration work.
Ray Serve: general‑purpose distributed serving with autoscaling, traffic splitting, and Python DAGs; integrates well when you already run Ray.
NVIDIA Triton: high‑performance inference server with backends for multiple frameworks and dynamic batching; strong when mixing GPU/CPU models and need tight production controls.

Feature Comparison

Capability	vLLM	Ray Serve	NVIDIA Triton
Primary focus	LLM text inference (decoder‑only focus)	General model serving on Ray	Multi‑framework inference server
Batching	Continuous batching, Paged KV cache	Dynamic batching via replicas	Dynamic/sequence batching
Multi‑GPU scaling	Tensor/PP via backends; strong single‑node perf	Distributed scaling, autoscaling, canaries	Multi‑GPU, MIG, model ensembles
Observability	Prometheus metrics; simple tracing hooks	Metrics/tracing via Ray/OTel	Prometheus, model stats, inference logs
Latency profile	Excellent token throughput at low p99	Good; depends on routing/topology	Excellent with optimized backends
Ops footprint	Lightweight server + model weights	Ray cluster (head/worker) + Serve	Triton + runtimes (TensorRT, PyTorch, ONNX)

Notes: exact capabilities vary by version/backends. Benchmark in your environment and models.

Performance & Throughput

Batching is the biggest lever. Aim for high effective batch sizes without starving tail‑latency SLOs.
KV cache residency drives memory pressure. Prefer page/cache‑aware schedulers and reduce max sequence lengths where possible.
Token generation: tune tensor/pp parallelism and fused kernels; enable FP8/INT8 where quality allows.

Cost & Operations

Use spot/preemptible where safe; keep warm pools for SLOs.
Right‑size GPU type to model size and target throughput.
Automate model rollout, shadow traffic, and safety/regression checks.

Recommendations

Fast Path for LLMs

Start with vLLM for decoder‑only chat/completion APIs when you need strong throughput with minimal ops overhead.

Complex Pipelines

Choose Ray Serve when you orchestrate multiple Python stages (retrieval, re‑ranking, tools) with traffic shaping.

Mixed Backends

Use Triton for heterogeneous fleets (TensorRT, ONNX, PyTorch) and tight GPU utilization controls.

Next Steps

Need workload‑specific benchmarks?

We can run targeted benchmarks with your prompts, models, and latency SLOs to recommend a stack and sizing guide.

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