Build an offline voice assistant with whisper and vLLM

Benefits of running a voice assistant offline

Voice-based AI assistants are becoming essential in customer support, productivity tools, and embedded interfaces. For example, a retail kiosk might need to answer product-related questions verbally without relying on internet access. However, many of these systems depend heavily on cloud services for speech recognition and language understanding, raising concerns around latency, cost, and data privacy.

In addition, a healthcare terminal or legal consultation assistant may need to handle voice queries involving sensitive personal information, where sending audio data to the cloud would violate privacy requirements. Running your voice assistant entirely offline solves these problems.

You avoid unpredictable latency caused by network fluctuations, prevent sensitive voice data from leaving the local machine, and eliminate recurring API costs that make large-scale deployment expensive. It also boosts trust for on-device deployments and compliance-sensitive industries.

By combining local speech-to-text (STT) with a locally hosted large language model (LLM), you gain complete control over the pipeline and eliminate API dependencies. You can experiment, customize, and scale without relying on external services.

Challenges of building a local voice assistant

While the benefits are clear, building a local voice assistant involves several engineering challenges.

Real-time audio segmentation requires reliably identifying when users start and stop speaking, accounting for natural pauses and background noise. Timing mismatches between STT and LLM components can cause delayed responses or repeated input, reducing conversational quality. You also need to balance CPU/GPU workloads to keep the pipeline responsive without overloading resources or blocking audio capture.

Why run offline voice AI on Arm-based DGX Spark?

Arm-powered platforms like DGX Spark allow efficient parallelism: use CPU cores for audio preprocessing and whisper inference, while offloading LLM reasoning to powerful GPUs. This architecture balances throughput and energy efficiency-ideal for private, on-premises AI workloads. To understand the CPU and GPU architecture of DGX Spark, refer to Unlock quantized LLM performance on Arm-based NVIDIA DGX Spark .

DGX Spark also supports standard USB interfaces, making it easy to connect consumer-grade microphones for development or deployment. This makes it viable for edge inference and desktop-style prototyping.

In this Learning Path, you’ll build a complete, offline voice chatbot prototype using PyAudio, faster-whisper, and vLLM on an Arm-based system-resulting in a fully functional assistant that runs entirely on local hardware with no internet dependency.