GPU Cloud Infrastructure

Common Challenges & Technical Drivers

• Excessive third-party API latency and unpredictable per-token pricing.
• Data privacy risks and regulatory constraints preventing transmission of enterprise data to public models.
• Model hallucinations and inaccurate responses eroding user trust in AI features.
• Complexity of managing dedicated GPU compute clusters and optimizing vLLM batching.

What Is Included (In-Scope Deliverables)

• Deployment of dedicated, private LLM inference endpoints (vLLM, Triton, or Ollama).
• Retrieval-Augmented Generation (RAG) architecture with semantic chunking and hybrid vector search.
• Prompt caching, speculative decoding, and model quantization (AWQ/GPTQ) for cost efficiency.
• Observability and telemetry tracking token throughput, latency, and response quality.

Scope Boundaries & Transparent Exclusions

• Third-party model token API usage charges or cloud GPU instance hourly hardware fees.
• Training frontier foundation models from scratch (we specialize in fine-tuning, RAG, and inference).
• Manual document data labeling or manual cleaning of proprietary source archives.
• Legal advisory regarding AI copyright or synthetic content governance.

Tangible Engineering Deliverables

• Production Kubernetes manifests or Terraform code for self-hosted GPU inference serving.
• Complete RAG ingestion pipeline codebase with document parsers, chunking, and embedding generation.
• Evaluation test suite measuring retrieval accuracy, faithfulness, and latency benchmarks.
• Operational runbook detailing model weight updates, cache tuning, and GPU memory monitoring.

Select your target cloud environment and operational scale to view projected engineering capacity and sprint milestones.

Enterprise baseline declaration module deployed as part of this service scope. Fully auditable, zero-lockin, turnkey source code.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: vllm-inference-engine
spec:
  replicas: 2
  template:
    spec:
      containers:
      - name: vllm
        image: vllm/vllm-openai:latest
        resources:
          limits:
            nvidia.com/gpu: 1
        args: ["--model", "mistralai/Mistral-7B-Instruct-v0.3", "--port", "8000"]

Sprint Delivery Roadmap & Phased Milestones

Frequently Asked Questions