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BezaForge: Building Production-Grade Private LLM Inference Infrastructure

A complete private cloud infrastructure project that demonstrates how to build an enterprise-level environment supporting GPU-based large model inference, covering virtualization, container orchestration, network isolation, and observability.

私有云LLM推理ProxmoxDockerGPU可观测性VLAN基础设施
Published 2026-03-30 10:45Recent activity 2026-03-30 10:53Estimated read 8 min
BezaForge: Building Production-Grade Private LLM Inference Infrastructure
1

Section 01

BezaForge: Core Overview of Production-Grade Private LLM Inference Infrastructure

BezaForge is an open-source production-grade private cloud infrastructure solution designed for LLM GPU inference scenarios. It integrates virtualization, containerization, network isolation, and observability to help teams deploy and run large models in their own hardware environments, ensuring data privacy while achieving performance close to cloud services. This post will break down its architecture, components, deployment practices, and more.

2

Section 02

Background & Architecture Vision of BezaForge

With the popularity of AI large models, building secure, controllable, high-performance private inference infrastructure has become a core concern for enterprises. BezaForge was created to address this need. Developed and maintained by thejollydev, it aims to provide an end-to-end solution for teams to deploy LLMs locally, balancing data privacy and performance.

3

Section 03

Technical Architecture & Technology Stack Selection

BezaForge's tech stack is tailored for production environments:

  • Virtualization: Proxmox VE (open-source, stable, supports KVM/LXC)
  • Container Orchestration: Docker + Compose (lightweight, easy to maintain)
  • Network: 5-VLAN design (Management/VLAN10, Storage/VLAN20, Application/VLAN30, Database/VLAN40, External/DMZ/VLAN50) for security isolation.
  • Observability: Prometheus/Grafana/Loki (metrics, logs, visualization).
  • GPU Support: NVIDIA Container Toolkit (native CUDA, memory management).
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Section 04

Core Components of BezaForge

Proxmox Virtualization:

  • Cluster: 3+ nodes for HA, Ceph distributed storage, Proxmox Backup Server for incremental backups.
  • VMs: K8s control plane (optional), Docker hosts, GPU work nodes, monitoring nodes, storage nodes.

Docker Container Orchestration:

  • Uses Docker Compose for service orchestration (e.g., vllm inference service with NVIDIA runtime).
  • GPU Management: Controls GPU memory via NVIDIA Docker, supports multi-model concurrency and dynamic scheduling with MPS.

Observability:

  • Metrics: Prometheus collects infrastructure (CPU, memory), GPU (GPU memory, utilization), container, and application metrics.
  • Logs: Loki for lightweight log aggregation with tag indexing.
  • Visualization: Grafana panels for infrastructure overview, GPU monitoring, LLM performance, etc.
5

Section 05

Deployment & Daily Operation Practices

Initial Deployment:

  1. Hardware prep: Server setup, network wiring, GPU installation.
  2. Proxmox installation: ISO setup, cluster init, storage config.
  3. Network config: VLAN segmentation, firewall rules.
  4. VM deployment: Template-based VM creation.
  5. Container service: Docker Compose launch.
  6. Monitoring: Prometheus/Grafana setup.
  7. Model deployment: Weight download, inference service config.

Daily Ops:

  • Capacity planning: Monitor GPU memory utilization (>80% → expand), P99 latency, queue depth.
  • Backup: VM snapshots (daily,7d), config version control, model weight redundancy, database dumps.
  • Security: VLAN isolation, RBAC access control, audit logs, regular vulnerability scans.
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Section 06

Performance Optimization Strategies

LLM Inference Optimization:

  1. Model Quantization: INT8 (50% compression), GPTQ/AWQ (4bit,75% reduction), dynamic quantization.
  2. Batch Processing: Dynamic batching, continuous batching (vLLM), pre-fill optimization.
  3. Caching: KV Cache reuse, prefix sharing, smart eviction.

Infrastructure Optimization:

  • Storage: NVMe cache, storage tiering (SSD for models, HDD for logs), RDMA network.
  • Network: Jumbo frames (MTU9000 for storage), SR-IOV (GPU passthrough), DPDK (optional).
7

Section 07

Application Scenarios & Limitations

Typical Scenarios:

  • Enterprise private AI assistant: Data stays local, customizable, cost-effective.
  • Code assistant: Secure (no source code to third parties), domain-adapted, low latency.
  • Document processing: Knowledge extraction, semantic search, content generation.

Challenges:

  • High hardware cost (GPU servers).
  • Technical threshold (multi-domain knowledge needed).
  • Operational complexity (vs public cloud APIs).

Applicable Boundaries:

  • Suitable: Data-sensitive industries (finance, healthcare), high inference load, teams with dedicated ops.
  • Not suitable: Startups/small teams, volatile loads, teams without ops capability.
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Section 08

Conclusion & Community Ecosystem

BezaForge provides a validated blueprint for private LLM infrastructure, covering full lifecycle from design to ops. It lowers the barrier for teams to build stable, efficient, secure AI platforms.

Community:

  • Contributions welcome: Monitoring panels, GPU optimizations, security scripts, multi-node extensions.
  • Related projects: Ollama (simplify model running), vLLM (high-performance inference), LangChain (app framework), Flowise (visual workflow).

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