title: Local Models Over API - Enterprise Architecture

insight: API-based inference costs $720K/year at enterprise scale while local models cost $10K one-time - a 54x cost difference over 3 years. Local models also provide 10-30x latency improvement and true privacy by architecture.

reasoning_chain: 1M validations/month × 3 models × 500 tokens input = 1.5B tokens/month

reasoning_chain: Local inference on A6000: ~$20K hardware one-time + minimal electricity

reasoning_chain: Latency: 1-3 seconds API vs 100-300ms local

reasoning_chain: Privacy: data never leaves enterprise vs compliance workarounds

source_context: User asked: 'should our multimodels consensus... ever need to be api call based? or should our system have inbuilt 3 locally hosted models...'


title: Local-First Privacy Architecture

insight: P8 Privacy Gateway transforms from a workaround to a FEATURE when models are local - it becomes the intelligent router that decides which validations stay on-premise vs cloud, making privacy a competitive advantage.

reasoning_chain: API-based: P8 is a workaround to hide data before sending externally

reasoning_chain: Local-based: P8 becomes the orchestrator that keeps data on-prem

reasoning_chain: Regulated industries (legal, healthcare, government) REQUIRE on-prem

source_context: Genesis analyzed: 'P8 becomes a FEATURE, not a workaround, when models are local.'


title: Multi-Architecture Model Diversity

insight: Use 3 different model architectures (Llama 3.2 dense, Mistral sliding window, Phi-3 small) for TRUE consensus - different models fail differently, so agreement = high confidence, disagreement = flag for human review.

reasoning_chain: Same-architecture models make similar errors

reasoning_chain: Dense (Llama): Strong reasoning, consistency

reasoning_chain: Sliding Window (Mistral): Good structure, classification

reasoning_chain: Small/Fast (Phi-3): Fallback, quick validation

reasoning_chain: Consensus >90%: HIGH, 70-90%: MEDIUM, <70%: FLAG

source_context: Genesis: 'WHY 3 DIFFERENT ARCHITECTURES? Different models fail differently'


title: Classification vs Generation Task Analysis

insight: Validation is classification/scoring, not generation - a fine-tuned 7B model can match/exceed GPT-4 on specific tasks like rating confidence (0-100), extracting factual triplets, validating citation format.

reasoning_chain: We're NOT writing legal briefs - we're rating confidence 0-100

reasoning_chain: Tasks are: extract factual claims as triplets, validate citation format (yes/no), detect overconfidence (yes/no)

reasoning_chain: Fine-tuned 7B models excel at specific classification

reasoning_chain: LoRA fine-tuning: <1GB adapter, train in hours on A6000

source_context: Genesis: 'A fine-tuned 7B model can match or EXCEED GPT-4 on specific classification tasks!'


type: DIRECTIVE

title: Deployment Tier Options

insight: 4 deployment tiers: Genesis Cloud (multi-tenant), Dedicated Cloud (isolated), On-Premise (data sovereignty), Hybrid (sensitive routing) - giving enterprise customers choice based on their compliance requirements.

reasoning_chain: Government/legally sensitive: must be on-prem

reasoning_chain: Hybrid: intelligent routing based on P8 classification

source_context: Genesis defined: TIER 1: GENESIS CLOUD, TIER 2: DEDICATED CLOUD, TIER 3: ON-PREMISE, TIER 4: HYBRID


title: Hardware Tiers for Local Inference

insight: 4 hardware options scaled to customer: Budget ($2K RTX 4090 for pilots), Standard ($4K 2x4090 for SMB), Enterprise ($20-50K A6000s for scale), Massive ($200K+ H100 cluster) - 3-year TCO shows local is 54x cheaper than API.

reasoning_chain: Hardware is one-time cost + electricity/maintenance

reasoning_chain: Local inference solves cost, latency, privacy all at once

source_context: Genesis calculated: 'LOCAL IS 54x CHEAPER OVER 3 YEARS'