[USE CASE]

Deploy AI Agents in Production

Long-running agentic workloads with tool use and memory.

Production AI agents combine LLM inference + tool execution + vector retrieval. Latency budget is dominated by sequential tool calls, not single-token gen. Pick fast small models (L40S) for the agent loop and burst to H200 for hard reasoning.

Recommended
Mixed: 4× L40S (agent loop) + 8× H200 (reasoning burst)
Est. cost
$0.012 – $0.05 per agent session
Duration
Steady-state

Step-by-step

  1. 1. Split fast/slow path
    Route routine tool calls to L40S; escalate hard reasoning to H200.
  2. 2. KV cache sharing
    Reuse prompt prefix across sessions to cut TTFT.
  3. 3. Vector retrieval
    Co-locate vector DB with inference for sub-10ms retrieval.
  4. 4. Observability
    Trace every tool call; replay failed sessions for prompt tuning.

FAQ

How do I keep agent latency under 2 seconds?
Use a small fast model (8B on L40S) for the agent loop, parallelize tool calls, and cache prompt prefixes. Reserve large-model calls for the rare hard step.
What's the best stack for agent deployment?
vLLM + Redis (session state) + a vector DB (Pinecone/Qdrant) + Servers.computer routing for multi-region failover.
Definition

What is Servers.Computer?

Servers.Computer is an AI compute routing and procurement layer that benchmarks, compares, and deploys GPU clusters (NVIDIA H100, H200, B200 and AMD MI300) across global cloud providers in real time.

Other use cases
Train a 70B Parameter LLMServe LLM Inference in Production