Tag: Multi-Tenant

4 articles

• LLM LoRA Adapter Cache in Production: Stabilizing Multi-Tenant Fine-Tuning Services with Heat Tiering, Cold-Start Budgets, and Eviction Policies

  Production strategies for LoRA adapter cache management in multi-tenant fine-tuning services: heat tiering, cold-start budgets, eviction policies, and a governance framework for stable model serving.

  07/09/2026 LLM Serving / LoRA / Adapter Cache / Multi Tenant / Model Serving / GPU / Fine Tuning / RAG / vLLM

• LLM Multi-Tenant Quota Gateway in Production: Token Budgets, Rate Limits, and Fair Scheduling for Shared Model Pools

  A systematic guide to building a multi-tenant quota gateway for shared LLM inference platforms, covering request rate limiting, token budgets, fair scheduling, cost attribution, and degradation strategies to prevent a few tenants from overwhelming the model pool and controlling billing risks.

  07/09/2026 LLM Gateway / Rate Limit / Multi Tenant / Inference Serving / Cost Control / Fair Scheduling / Token Budget

• LLM LoRA Adapter Cache in Production: Reducing Dynamic Loading Jitter with Hot/Cold Tiering and Eviction Strategies

  This article covers production governance for LoRA adapter caches in multi-tenant LLM serving, including dynamic loading, hot/cold tiering, eviction strategies, tenant quotas, version releases, and go-live checks to reduce first-token latency and GPU memory jitter.

  07/05/2026 LLM Serving / LoRA / Adapter Cache / Inference Infrastructure / Hot/Cold Tiering / Multi-Tenant / Dynamic Loading / Eviction Strategy

• Multi-LoRA Production Practices: Stable Multi-Tenant Inference with Adapter Caching and Hot/Cold Tiering

  A systematic guide to Multi-LoRA serving in production: adapter caching, hot/cold tiering, rank-aware scheduling, eviction strategies, and deployment checks to stably serve hundreds of custom LoRA adapters under GPU memory constraints.

  07/01/2026 LLM Serving / LoRA / Inference / GPU / Multi Tenant / Adapter Cache / vLLM