Deploying Multilingual Audio AI Across Cloud and Accelerator Infrastructure

When a global automotive manufacturer needed to operationalize in-vehicle voice features ahead of launch, spec-sheet comparisons couldn’t answer the questions that mattered most. In a focused engagement with LILT, the customer moved from infrastructure uncertainty to a data-backed deployment plan, with reproducible assets their internal teams now own.

The Challenge: Infrastructure Decisions Ahead of Launch

The customer was preparing to launch multilingual speech recognition (ASR) and voice activity detection (VAD) capabilities in production. Before scaling, they needed defensible answers to a tightly coupled set of questions:

• Which environments could meet real-time latency targets under sustained concurrency?
• What would inference actually cost across providers and accelerator types?
• Which deployment path would still hold up through future fine-tuning and traffic growth?

Answering those questions required engineering, deploying, and stress-testing licensed audio models across production-grade environments, covering streaming, near-real-time, and batch workloads under realistic load, with measurement rigorous enough to support a procurement decision.

The Solution: Benchmarking as Engineering

LILT treated the program as a rigorous engineering discipline, deploying and stress-testing three commercial-grade audio models across competing cloud environments. By using a standardized inference stack, LILT eliminated hardware bias and protected the customer from vendor lock-in.

The engineering focus covered four main areas:

• Multi-Mode Coverage: Tested across streaming, near-real-time, and batch processing to mimic true production environments.
• Cross-Platform Deployment: Compared traditional NVIDIA GPUs on Azure against purpose-built Inferentia2 accelerators on AWS.
• Standardized Inference Stack: Standardized on NVIDIA Triton with ONNX and TorchScript runtimes for maximum flexibility.
• Rigorous Measurement: Evaluated latency, throughput, and cost under a heavy load of 100 concurrent requests per model.

The Results: From Procurement Debate to Data-Backed Decision

LILT delivered the evidence the customer needed to commit to an infrastructure path with confidence.

• Validated Deployment Paths: Working configurations for multilingual ASR and VAD across both GPU and accelerator-based infrastructure, engineered against 100 concurrent requests per model.
• Quantified Tradeoffs: Latency, throughput, and cost differentials between Azure and AWS execution environments captured in a single, comparable framework, converting a procurement debate into a data-driven decision.
• Informed Platform Selection: Clear guidance for scaled rollout and the customer’s planned fine-tuning phases, grounded in measured performance rather than vendor claims.
• Reproducible Assets: Deployment templates, inference configurations, and measurement harnesses delivered to internal teams, enabling them to independently re-run the benchmarks against new model versions, instance types, or providers.

About LILT

LILT multilingual applied AI research lab, partners with researchers to design custom evaluations, closed benchmarks, and RL environments that measure real model behavior in business workflows. We integrate expert human judgment, research-grade delivery, and forward-deployed engineering to define, operationalize, and evaluate models—across domains and 200+ languages.