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particionamento-de-modelos-de-ia-com-lora-dinamico

Implements per-tenant LLM personalization using dynamically loaded LoRA adapters on shared GPU infrastructure.

Updated Jul 19, 2026
Search Tags:#multi-tenant#fine-tuning#gpu#saas#lora#tenant isolation#llm inference
One-click install
npx skills add https://github.com/Ryanzucchi/Eldritch_Lich --skill particionamento-de-modelos-de-ia-com-lora-dinamico-ryanzucchi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: particionamento-de-modelos-de-ia-com-lora-dinamico
Source: https://github.com/Ryanzucchi/Eldritch_Lich/tree/main/.agents/skills/particionamento-de-modelos-de-ia-com-lora-dinamico
Command: npx skills add https://github.com/Ryanzucchi/Eldritch_Lich --skill particionamento-de-modelos-de-ia-com-lora-dinamico-ryanzucchi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Multi-tenant SaaS platforms that offer AI co-writing features risk leaking one tenant's private manuscripts into another tenant's generated text when fine-tuning shared models. This Skill designs an isolation architecture where each tenant gets a personalized language model via lightweight LoRA adapters, preventing intellectual property cross-contamination while keeping GPU memory usage efficient. ## Core Features & Use Cases - Per-Tenant LoRA Fine-Tuning: Trains small (~40MB) adapter tensors on each tenant's own manuscripts while the base model stays frozen and unmodified. - Dynamic Adapter Overlay at Inference: Routes requests by X-Tenant-ID header, loading the correct adapter from S3-compatible storage into VRAM on cache miss. - LRU VRAM Cache Management: Evicts least-recently-used adapters when VRAM usage exceeds 85% to make room for active tenants. - Use Case: A creative writing platform serves hundreds of authors on one GPU. When author B requests text continuation, the system applies only author B's LoRA adapter, so the response reflects their style without exposing author A's characters or plot. ## Quick Start Ask the AI to design a multi-tenant LLM inference architecture with per-tenant LoRA adapters, dynamic VRAM caching, and cross-contamination isolation for a shared GPU server.

Frequently Asked Questions about particionamento-de-modelos-de-ia-com-lora-dinamico

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I serve fine-tuned LLMs for multiple tenants on one GPU?▼

Keep the base model frozen in VRAM and train a small LoRA adapter (~40MB) per tenant on their own data. At inference, load the requesting tenant's adapter from storage and overlay it on the base model, using an LRU cache to manage VRAM.

How to prevent data leakage between tenants in LLM fine-tuning?▼

Train a separate LoRA adapter per tenant using only that tenant's corpus, never fine-tune the shared base model. Validate isolation with cross-contamination tests confirming no tenant's content influences other tenants' outputs.

What is the latency overhead of dynamic LoRA adapter switching?▼

Loading an adapter from remote S3 storage adds up to 1.2 seconds of cold-start latency. Mitigate this by pre-warming active tenants' adapters at session start and keeping them in VRAM cache, targeting under 8% TTFT overhead.

When should I not use multi-tenant LoRA adapters?▼

Avoid this approach for single-user local deployments where the model runs entirely on the author's machine, or for purely local analysis AI. For early platform versions, sharing one base model with database-level RLS isolation is a simpler fallback.

How much VRAM do LoRA adapters use per tenant?▼

Each LoRA adapter averages about 40MB with a default rank of 16, far smaller than a full model copy. An LRU eviction policy triggers when VRAM usage exceeds 85%, removing the least-recently-used tenant's adapter.

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