Shared GPU for LLMs and speech: capacity consulting
Consulting to run LLMs and speech models on shared GPUs: model loading, waiting time and capacity limits measured on your workload.
The challenge
LLMs, transcription and speech synthesis can compete for one GPU. When each service decides independently when to load a model, memory conflicts and out-of-order handoffs become an operational risk.
Sharing hardware must meet the product's response-time requirements. Model loading and waiting for the GPU must be included in capacity planning.
Approach
- Assess models, memory, concurrency and latency targets.
- Coordinate model loading, unloading and service handoffs.
- Define access limits, monitoring and operational recovery.
Consulting scope
The proposal can include an infrastructure review, coordination deployment, product integration and documentation for your team.
Capacity and response time are defined after measuring the real workload. The assessment may also show that services need separate GPUs.
Application in your company
Suitable for text and speech services sharing hardware that need clear rules for ownership, recovery and model handoffs. The initial assessment checks whether sharing a GPU meets your latency requirements.
Let's assess your project
I reply with scope and a proposal.