24–32 GB class
L4, A10, RTX 4090 or RTX 5090. Consider for smaller models only after measuring total memory, including runtime and cache.
Speech AI infrastructure
Speech AI infrastructure
Speech AI infrastructure: model selection, memory requirements, storage and deployment choices without invented performance claims.
Choose a configuration in Console to review its resources and estimated cost.
What to plan for
Transcription and speech synthesis have different latency, batching and audio-processing requirements.
Specify audio duration, concurrency and streaming needs. Include CPU preprocessing and an explicit retention policy for recordings.
Configuration choices
48–80 GB class
L40S, A40, A100 or H100. More memory may accommodate larger working sets; it does not guarantee concurrency or token speed.
141 GB and multi-GPU
H200, B200 or B300 and explicitly sized multi-GPU systems. Confirm exact model, precision, topology and serving framework.
Weight-only memory ≈ parameters × bytes per parameter. Runtime, KV cache, activations and optimizer state are additional. This is a sizing method, not a deployment recommendation.
Smart Infra Finder
Your workload, your requirementsCheck availability: Speech AI infrastructure
Check the current price and configuration in Console. Your choice is saved through registration. The current lookup uses mock adapters; no real capacity is reserved.
Related products and guides
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Explore →AI infrastructure
Plan infrastructure for model hosting, inference, training and fine-tuning with explicit memory and workload requirements.
Explore →Cloud storage
Compare object, block and file storage, backups and snapshots by access pattern, durability needs and restore workflow.
Explore →Cloud backup
Cloud backup: access model, capacity planning and recovery considerations before choosing a configuration.
Explore →Specification sources
Hardware and model documentation; these sources do not confirm rental availability.
Official documentation 1 →