GPU Infrastructure for Production AI Inference

Who is this for?

For AI application teams, SaaS companies and enterprises serving open-weight language, vision, speech or embedding models. Suitable when you need control over the model and deployment configuration.

Typical workloads

• LLM serving: assistants, RAG applications and structured-output APIs.
• Vision and audio: image processing, speech recognition and batch inference.
• Search pipelines: embeddings, reranking and document processing.

Recommended GPUs to evaluate

• L40S: evaluate for smaller models and vision workloads when the complete memory budget fits.
• A100 or H100: compare for larger models or higher throughput; shortlist H200 when weights, KV cache and concurrency need more memory.

Final selection depends on your software, memory budget and pilot results. Capacity and the full configuration are confirmed in the quote.

How many GPUs should I start with?

Begin with one GPU for a representative model and low-concurrency baseline. If the model cannot fit, assess model parallelism across two to four GPUs. Add replicas only after measuring peak traffic; replicas and model partitioning solve different problems.

How does the pilot work?

1. Scope: agree the model revision, precision, input/output lengths, peak concurrency and target response time.

2. Approve: confirm the configuration, provider, location, storage, pilot budget and AUD quote before activation. The pilot is charged only under the accepted quote.

3. Measure: test cold and warm requests, time to first token, output speed, errors, memory and cost per accepted result. Agree a scale-up plan from the results.

Agree the scope, duration, acceptance criteria and price in advance. A pilot is not a free-trial offer. No charge before you accept the quote; the infrastructure provider is disclosed before activation.

Other solutions

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