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NVIDIA still dominates frontier-model training in 2026, but inference tells a different story. AMD MI300X/MI325X with mature ROCm, Intel Gaudi 3, Google TPU v6, and AWS Trainium/Inferentia deliver 20 to 50% lower cost per token without sacrificing quality. Here is when to choose each option.
NPUs stopped being an accessory and became the component that defines real performance in laptops, phones, and small servers. A practical look at the hardware that rules 2026, which workloads pay off, and where the traditional GPU still wins.
Un enrutador de inferencia decide qué modelo atiende cada petición en función de coste, latencia y complejidad. Bien diseñados reducen la factura de tokens sin que el usuario perciba degradación; mal diseñados introducen fallos sutiles difíciles de depurar.
vLLM remains the reference engine for serving LLMs on GPU in 2025: automatic prefix caching sharply cuts latency for repeated prompts, speculative decoding speeds up large models, and multi-LoRA support lowers the cost of multi-tenant SaaS, though multi-GPU support and non-NVIDIA hardware remain weak points.
A model trained in PyTorch or TensorFlow, running the same way on a server, a phone, a browser tab, or an ARM gateway on the factory floor: that is what ONNX Runtime solves. It turns the ONNX format into a genuinely portable artifact, exported once, at the cost of some peak performance versus a platform-native runtime.
Text Generation Inference (TGI) is the Hugging Face stack for serving open LLMs in production: continuous batching, 4-bit and 8-bit quantization, streaming, and an OpenAI-compatible API. After a brief restrictive-licence episode in 2023, it returned to Apache 2.0 in version 2.0.
