Running AI Models Locally: GPT-OSS-120B Deployment Guide

Healthcare organizations, financial institutions, and government agencies can't send sensitive data to external APIs. Local AI deployment keeps data on your infrastructure while providing GPT-level capabilities. GPT-OSS-120B and similar open-weight models now match GPT-4 quality while running on premise. Initial setup costs $20K-100K but operating costs are 70-90% lower than API-based solutions at scale.

On-premise GPU infrastructure for self-hosted AI models

Hardware Requirements & GPU Selection

GPU Specifications for Different Model Sizes

Small Models (7B-13B parameters - Llama 3.2, Mistral 7B): Single NVIDIA RTX 4090 (24GB VRAM) or A6000 (48GB). Handles 10-20 concurrent users. Cost: $1,500-4,500 consumer card or $4K-6K professional card. Sufficient for internal tools, low-traffic applications, or development environments. Our internal documentation chatbot runs on single RTX 4090.

Medium Models (30B-70B - Llama 4 Maverick, GPT-OSS-70B): 2-4× A100 GPUs (80GB each) or H100 GPUs (80GB). Handles 50-100 concurrent users. Cost: $20K-40K for A100s, $50K-100K for H100s. Suitable for departmental deployments, mid-sized enterprises, or specialized applications requiring better quality than small models. Medscribe prototypes test on 2× A100 setup before cloud deployment.

Large Models (120B+ - GPT-OSS-120B, Falcon 180B): 8× A100 or 4× H100 in multi-node setup. Handles 200-500 concurrent users. Cost: $80K-200K hardware investment. Enterprise-scale deployments only. At this hardware cost, seriously evaluate whether cloud APIs might be more economical unless data sovereignty requirements mandate local deployment.

Optimization Techniques

Quantization (INT8/INT4): Reduces model memory requirements 50-75% with minimal quality loss. 70B model normally requiring 140GB VRAM fits in 40-60GB after quantization. Enables running larger models on available hardware or serving more concurrent users per GPU. Quality impact: 2-5% accuracy reduction—acceptable trade-off for most applications.

Batching & Request Queuing: Process multiple requests simultaneously maximizing GPU utilization. Individual request might wait 200-400ms for batch to fill, but total throughput increases 3-5x. For background tasks (document processing, content generation) where sub-second latency isn't critical, batching dramatically improves cost efficiency.

GPU utilization monitoring for optimized local AI model inference

Cost Analysis: Local vs Cloud APIs

Break-Even Calculation: Cloud APIs (GPT-5) at 500K requests monthly = $15K-18K/mo. Local deployment: $60K hardware + $2K/mo power/cooling = $7K monthly year one, $2K monthly thereafter. Break even month 4-6. Year two+ savings: $13K-16K monthly = $156K-192K annually. For enterprises processing millions of requests, local deployment becomes mandatory for cost control.

Hidden Costs: DevOps overhead maintaining GPU infrastructure (1-2 FTE @ $100K-150K/year), model updates and testing, hardware refreshes every 3-4 years. Total cost of ownership often 30-50% higher than hardware alone. Factor these operational costs when calculating break-even—local isn't free, just differently expensive. For under 200K monthly requests, cloud APIs typically win on TCO.

When Local AI Makes Sense

Data Sovereignty & Compliance Requirements

Healthcare (HIPAA): Patient data never leaves premises simplifies compliance. No cloud BAA agreements needed. Reduced attack surface and privacy risks. For hospital networks already operating on-premise infrastructure, adding GPU servers is natural extension. Cloud migration for sensitive health data introduces compliance complexity local deployment avoids.

Financial Services (PCI DSS, SOC 2): Regulatory requirements often mandate data residency. Customer financial data, transaction histories, account information—keeping these on-premise satisfies compliance while enabling AI innovation. Cloud data processing creates audit complexity and potential regulatory issues local deployment sidesteps.

Government & Defense: Classified or sensitive government data cannot touch commercial cloud services. Local AI deployment enables intelligence analysis, document processing, and decision support for government agencies requiring air-gapped systems. This use case mandates local deployment regardless of cost considerations.

High-Volume Operations

Scale Economics: Processing 1M+ API calls monthly costs $30K-75K with cloud providers. Local deployment with $80K-150K hardware delivers same throughput for $5K-10K monthly operating costs after initial investment. Payback in 2-4 months. At enterprise scale, local becomes obvious choice—savings of $300K-750K annually fund AI infrastructure team and continuous optimization.

Predictable Costs: Cloud API pricing scales linearly with usage—growth means proportional cost increases. Local deployment has fixed infrastructure costs—usage growth doesn't increase expenses. This predictability matters for financial planning and enables aggressive AI adoption without budget concerns.