NVIDIA Riva

Teams with GPU clusters that need low-latency on-prem transcription.

Riva is NVIDIA's containerised speech stack, with Parakeet and Canary models that are genuinely competitive on English WER. You run it yourself, so latency and data residency are fully under your control, but you also own the GPU ops cost. The 2026 Parakeet leaderboard results put it among the top open ASR systems.

Riva is the right answer when latency, sovereignty, or per-minute economics push you off SaaS. NVIDIA ships the speech stack as a set of Docker containers tuned for GPU acceleration, and the Parakeet TDT 1.1B model in particular is a serious open-weights contender that currently sits near the top of the Hugging Face open ASR leaderboard. For teams that have already invested in GPU clusters, the economics get interesting fast. At sufficient volume, running Riva on your own H100s costs less per minute than paying Deepgram or AssemblyAI, and you get full control over latency and data flow. Streaming latency under 300ms on appropriate hardware is achievable, which puts Riva in contention for real-time captioning and voice agent workloads where cloud round trips would be too slow. The trade-off is operational. You own the GPU servers, the Kubernetes orchestration, the model updates, the monitoring, and the on-call. For a small team without ML infrastructure experience, that's a serious investment compared to a SaaS API key. Language coverage is also narrower than Whisper, with English heavily prioritised. The Canary models add some multilingual range, but for global content teams Whisper or Speechmatics still cover more ground. NVIDIA AI Enterprise licensing is required for production support, which adds a layer of cost on top of the free model weights.