Choosing an Inference Engine: Why Choice Matters

What is an Inference Engine?

An inference engine is the runtime that loads a trained model, transforms or fuses parts of its compute graph, and executes it efficiently on specific hardware.

Large Language Models (LLMs) are the brains behind today’s AI-powered applications. They write helpful replies in customer support, summarize long documents, power natural-language search, and act as the control center for “agents” that can plan and take actions. While training and building these models is challenging, running them
efficiently is equally crucial.

That’s where inference comes in. Training builds the model, but inference puts it to work.

Why Does Choice Matter?

Think of the AI model as a recipe sitting on your counter. Your hardware is the stove waiting to be used. And the inference engine is the chef who makes it all happen—streamlining prep (fusing operations), selecting ideal cookware (kernels), juggling several dishes (batching), and ensuring no heat is wasted (memory efficiency). Same recipe, different chef—very different dinner. That’s why engine choice matters.

Inference engines span a broad design spectrum. Systems optimized for high-throughput text generation—like vLLM and Text Generation Inference (TGI)—prioritize efficient scheduling, batching, and production-grade serving. Lightweight CPU-centric runtimes such as llama.cpp (and wrappers like Ollama) emphasize simplicity, offline operation, and small-footprint deployment. General-purpose, hardware-agnostic runtimes such as ONNX Runtime aim for portability across CPUs, GPUs, and even web backends. For teams focused on CPU and Intel graphics, OpenVINO offers optimized kernels and practical quantization paths. Each approach represents a different balance of portability, performance, deployment friction, and developer experience.

How to Choose the Best Inference Engine?

There’s no one-size-fits-all solution. The best inference engine depends entirely on your requirements—giving you the flexibility and control to choose what fits. Start by asking:

1. Where will it run? (GPU server, office PCs, laptops, phones, browser)
2. What matters most right now? (speed, cost, privacy, portability)
3. How big is the model? (small/local vs. larger/hosted)
4. What’s your traffic like? (steady vs. spiky; short vs. long prompts)
5. Any deal-breakers? (no internet, strict privacy, limited memory, unsupported layers)

Your Goal → Start With → Why

With the myriad of inference servers available, you have to start with your goal.  Once you have that nailed down, it’s easy to pick your server.  Check out why choice becomes invaluable due to your differing goals.

How to Measure the Effectiveness of an Inference Engine?

Measuring an inference engine’s effectiveness is essential for making informed decisions. While dozens of metrics exist, these three offer the most practical insight:

1. Time to First Token (TTFT) — How soon users see the first words.
2. Tokens per second (or images/sec) — How much your system can deliver under load.
3. Peak memory/cost — The ceiling that governs your hardware and budget.

Inference Engines in SUSE AI

SUSE AI is an end-to-end platform designed to help you run and manage AI workloads in a reliable, scalable, and secure way. It includes foundational components such as the industry-leading SLES operating system, the RKE2 Kubernetes distribution, Rancher Prime, a library of AI apps, AI Observability, and more.

The SUSE AI library is a continuously expanding collection of AI applications—including the inference engines with Ollama and vLLM available as of today, with more coming in the near future! Check out the SUSE AI documentation to learn more!

Conclusion

Inference engines are opinionated: each embodies specific assumptions about hardware, workloads, and developer experience. The “right” choice is the one that aligns with your constraints—and often, it’s a portfolio strategy. Standardize on a portable path (ONNX Runtime or a compiler stack) for breadth, then specialize hotspot endpoints (TensorRT-LLM, OpenVINO, Neuron, vLLM) for depth. And always measure using your own data, sequences, and batch sizes—because the best engine isn’t the one that tops a benchmark; it’s the one that helps you ship your product.