ANRAK AI Infrastructure — From Bare Metal to Production Agents Market Context: $5.2T AI data center spend projected by 2030 (McKinsey). 156GW AI-related data center capacity demand by 2030. 46.3% annual growth rate of AI agents market. 88% of organizations now use AI. We work directly with data centers and enterprises to provision GPUs, measure compute output, train custom LLMs and SLMs, connect teacher models from Anthropic and OpenAI, and deploy production-ready AI agents — all under one roof. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ GPU CONSULTANCY & DATA CENTER OPERATIONS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1. GPU Selection & Procurement We evaluate your workload requirements and match them to the right hardware. Whether you need NVIDIA H100s for large-scale training, A100s for inference, or L40S for mixed workloads — we handle vendor negotiations, volume pricing, and delivery logistics. • Workload-to-GPU mapping and benchmarking • Volume-negotiated pricing with NVIDIA partners • Lease vs. buy analysis for different deployment horizons • Multi-cloud and colocation vendor comparison 2. Data Center Output Measurement We embed directly with your data center operations team to instrument every layer of AI compute. Real-time dashboards that translate GPU hours into business metrics. • GPU utilization and idle-time tracking across clusters • Thermal and power efficiency monitoring (PUE optimization) • Cost-per-inference and cost-per-training-run calculations • Capacity forecasting and scaling recommendations 3. Colocation & Cloud Strategy We design hybrid strategies that put latency-sensitive inference at the edge, heavy training in dedicated colocation, and burst capacity in the cloud. • Colocation facility evaluation and selection • Hybrid cloud architecture design • Network topology for multi-site GPU clusters • Disaster recovery and failover planning GPU COMPARISON TABLE: | GPU | VRAM | Performance | Best For | Use Case | | NVIDIA H100 SXM | 80 GB HBM3 | 989 TF (FP16) | Pre-training, RLHF | Large-scale LLM training (13B+) | | NVIDIA A100 | 80 GB HBM2e | 312 TF (FP16) | Fine-tuning, batch inference | Training & high-throughput inference | | NVIDIA L40S | 48 GB GDDR6 | 366 TF (FP16) | Inference, multimodal | Mixed AI/graphics workloads | | NVIDIA H200 | 141 GB HBM3e | 989 TF (FP16) | 70B+ models, research | Largest model training & inference | ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ CUSTOM LLM & SLM DEVELOPMENT ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1. Custom LLM Training (7B–70B+ parameters) Pre-training on proprietary datasets, distributed training across multi-node GPU clusters, custom tokenizer design, RLHF and DPO alignment. 2. Small Language Model Development (1B–7B) Efficient SLMs that outperform general-purpose models 10x their size on specific domains. Knowledge distillation, quantization (INT4/INT8) for edge deployment, LoRA and QLoRA fine-tuning. 3. Fine-Tuning & Guardrail Removal Supervised fine-tuning, preference optimization (DPO/RLHF), guardrail configuration and removal for enterprise deployment, safety evaluation and adversarial testing. TEACHER MODEL INTEGRATION: | Model | Role | Method | | Anthropic Claude | Reasoning, analysis, code generation | Knowledge distillation, evaluation benchmarking, hybrid routing | | OpenAI GPT-4/GPT-4o | General knowledge, creative, multimodal | Synthetic data generation, response quality evaluation, A/B testing | | Open Source (Llama, Mistral, Qwen) | Fine-tuning, domain adaptation, edge | Full fine-tuning, LoRA adaptation, quantization, distillation | | Your Custom Models | Production inference, internal tools | Deployed on your infra with monitoring, versioning, rollback | ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ AGENT DEVELOPMENT ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ • Multi-Agent Orchestration — Multiple AI agents collaborate on complex tasks with routing, delegation, memory sharing • Tool Use & Function Calling — Agents that search databases, call APIs, execute code, interact with external systems • Memory & Context Systems — Long-term memory, session persistence, RAG pipelines • Human-in-the-Loop — Approval workflows, escalation paths, confidence thresholds ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SECURITY & RED TEAMING ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ • Prompt Injection Defense — Multi-layer filtering and input sanitization • Data Exfiltration Prevention — Output monitoring and content filtering • Red Team Testing — Adversarial testing before production • Access Control & Audit Logs — Role-based access with full audit trails • Model Versioning & Rollback — Git-like versioning for models • Compliance Documentation — SOC 2, GDPR, and industry-specific 14-WEEK PROCESS: Week 1-2 Infrastructure Audit → Week 2-4 GPU Provisioning → Week 4-10 Model Development → Week 10-14 Agent Deployment Contact: https://anrak.io/contact