LOCAL AI INFRASTRUCTURE GPU CLUSTER

EXPERIMENTAL // HYBRID COMPUTE

Local AI Compute Stack

Private AI Development & Cybersecurity Compute Stack

An experimental compute and networking architecture designed to scale heavier local AI and security workloads, linking dedicated GPU acceleration to the local development environment via hardened remote connectivity patterns and isolated runtimes.

PROJECT STATUS

Experimental // Active

COMPUTE BACKEND

NVIDIA RTX Dedicated GPU

CONTAINER LAYER

Dockerized Ollama & WebUI

NETWORK SECURITY

Hardened Interconnect

GPU Compute Ollama Open-WebUI Qwen Models Kali Linux Hardened Networking OpenCode Zero Cloud Telemetry

arrow_back Back to Projects code View Source Repository open_in_new

info Implementation details documented at project repository

verified

Information Source & Research Boundary: Based on the documented architecture and infrastructure specifications in the portfolio and resume. Framing: Personal Infrastructure and Engineering Research. Implementation files are maintained in the developer's external repository.

fact_check

Standardized Case Study Specification (13 Parameters)

CLASSIFICATION: Infrastructure · Personal Lab

target 1. Objective

Design, assemble, and optimize a dedicated high-performance local AI computing infrastructure for offline security research.

computer 2. Environment

Dedicated Linux bare-metal host, NVIDIA RTX compute hardware, CUDA runtime, Docker, Ollama, vLLM.

schema 3. Architecture

Layered compute stack: Hardware -> Linux Kernel & NVIDIA Drivers -> Container Runtime -> Inference Server -> Application APIs.

build 4. Tools & Stack

Linux, Bash, NVIDIA CUDA, Docker, Prometheus, Grafana, nvtop, Python.

code 5. Implementation

Custom power profiles, thermal management daemons, memory paging configurations, and automated model weight loading.

flaky 6. Testing & Verification

VRAM allocation stress tests, sustained load thermal benchmarking, token generation latency metrics.

inventory_2 7. Evidence & Artifacts

Grafana telemetry dashboards, VRAM saturation logs, thermal dissipation curves under 100% compute load.

visibility 8. Security Findings

Proper VRAM quantization (4-bit/8-bit GGUF/AWQ) allows running 14B-32B parameter models locally at 30+ tokens/sec on consumer GPUs.

speed 9. Operational Impact

100% sovereign compute capability with zero subscription fees and zero cloud privacy exposure.

shield 10. Defensive Remediation

Firewall isolation restricting inference endpoints to localhost/LAN; physical access controls.

psychology 11. Lessons Learned

Memory bandwidth and thermal throttling are the primary bottlenecks in sustained local inference, not raw compute FLOPs.

update 12. Future Roadmap

Multi-GPU tensor parallelism scaling and automated model quantization pipelines.

info OVERVIEW & INFRASTRUCTURE ROLE // 01

What is the Local AI Compute Stack?

lan Architecture Focus & Purpose

Running large parameter models (such as 30B code-generation models) locally requires substantial compute resources that can quickly saturate mobile laptops. At the same time, relying on public cloud compute compromises data sovereignty and telemetry privacy.

The portfolio documentation describes this project as an experimental compute and networking architecture designed to scale heavier local AI and security workloads. It links dedicated GPU acceleration to the local development environment via hardened remote connectivity patterns and isolated runtimes.

shield Privacy, Air-Gapping & Containerization

The stack runs containerized instances of Ollama and Open-WebUI directly against the host GPU, exposing hardened API endpoints to local development machines through restricted interconnects.

This ensures that security research prompts, vulnerability analysis batches, and private codebases are processed with complete physical data isolation and zero cloud telemetry.

DOCUMENTED ARCHITECTURE FOCUS

lan GPU Compute Orchestration · Scalable Model Runtimes · Secure Local Interconnect

Provides high-throughput neural inference for coding, multi-agent development, and security telemetry analysis across developer workstations.

schema INFRASTRUCTURE ARCHITECTURE // 02

Documented Infrastructure Architecture

Based on the documented architecture in the portfolio engineering specifications, the stack links client environments to dedicated compute via hardened networking:

memory PRIVATE AI COMPUTE & NETWORKING TOPOLOGY HARDENED LOCAL INTERCONNECT

TIER 1 · CLIENT WORKSPACES

terminal Developer Clients

• OpenCode Workstations
• Kali Linux CLI Tools
• Interactive Browser UI

Client Query Invocations

TIER 2 · NETWORKING

vpn_lock Hardened Interconnect

• SSH Tunnels & TLS
• Firewall Ingress Rules
• Restricted Host ACLs

Zero Public Exposure

TIER 3 · CONTAINER RUNTIME

package_2 Docker Runtimes

• Containerized Ollama
• Open-WebUI Daemon
• Model Cache Mounts

Isolated Execution Space

TIER 4 · GPU ACCELERATION

developer_board NVIDIA Dedicated GPU

• NVIDIA RTX GPU Hardware
• Qwen Family Models
• High-Throughput Tensor Ops

Low-Latency Token Streams

DEVELOPER CLIENT → HARDENED INTERCONNECT → CONTAINERIZED OLLAMA → DEDICATED RTX GPU

Component Breakdown & Data Contract

Component	Responsibility	Inputs	Outputs	Relationships
Client Layer (OpenCode / CLI)	Originates coding and security prompts from developer workstations.	Developer queries, code contexts	API inference requests	Communicates through secure local tunnel
Hardened Networking Layer	Restricts traffic to authorized local IPs and blocks public WAN ingress.	Inbound client connections	Sanitized local packet flow	Protects the compute server boundary
Containerized Ollama Server	Manages model lifecycle, layer offloading, and streaming completions.	Sanitized prompt payloads	SSE token streams, embeddings	Interfaces directly with NVIDIA container runtime
Open-WebUI Daemon	Provides an interactive web graphical interface for research queries.	Browser sessions	Rendered Markdown chats	Connected locally to Ollama backend
NVIDIA RTX Dedicated GPU	Executes tensor operations and offloads quantized weights in VRAM.	Tensor graphs from Ollama	Evaluated logits & tokens	Physical hardware execution foundation

route EXECUTION LIFECYCLE // 03

Step-by-Step Documented Workflow

1 INPUT

Inference Request

A developer workstation issues an inference prompt, code review task, or log correlation query from OpenCode.

2 PROCESSING

Tunnel & Routing

The request routes through a hardened local network interconnect, strictly authenticated and isolated from external networks.

3 DECISION

Container Scheduling

The containerized Ollama daemon schedules the job, checking VRAM availability and model weights readiness.

4 ACTION

GPU Tensor Exec

The NVIDIA RTX GPU executes parallel matrix multiplications, generating token probabilities at high inference speed.

5 OUTPUT

Private Stream Return

The generated response streams directly back to the workstation client with zero cloud transmission or telemetry logging.

code IMPLEMENTATION DETAILS // 04

Technologies & Infrastructure Implementation

memory Dedicated GPU Compute Host

Built upon dedicated NVIDIA RTX GPU hardware running under Kali Linux. Utilizes the NVIDIA Container Toolkit to bridge physical CUDA cores directly into Docker containers with minimal virtualization overhead.

Stack: NVIDIA RTX GPU, CUDA Drivers, Kali Linux Kernel

package_2 Containerized Inference Services

Deploys Ollama and Open-WebUI in isolated Docker containers. Persistent volumes preserve model weights and local chat histories while insulating the underlying operating system.

Containers: Docker Compose, Ollama Daemon, Open-WebUI

security Hardened Networking Interconnect

Inbound access is strictly limited to authorized local IP ranges via iptables firewall rules. Remote development connections are established via authenticated, encrypted SSH port forward tunnels.

Networking: Encrypted SSH Tunnels, Local Subnet Filtering

model_training Qwen Family Model Deployment

Maintains localized deployments of Qwen open-weights models (including Qwen3-Coder 30B) optimized for multi-agent reasoning, code synthesis, and vulnerability analysis.

Models: Qwen3-Coder 30B, Qwen Reasoning Models

Infrastructure configuration scripts and Docker compose files are documented at the project repository. View Source Repository open_in_new

shield SECURITY DESIGN & ISOLATION // 05

Defensive Architecture & Physical Isolation

lock Zero Outbound Telemetry

Container configurations disable all telemetry flags. Firewall rules drop unauthorized outbound DNS and HTTP connections from inference containers.

hub Ingress Perimeter Filtering

The compute host does not listen on public internet interfaces; access is strictly confined to local Ethernet interfaces or authenticated cryptographic tunnels.

view_in_ar Container Sandboxing

Ollama and Open-WebUI run with dropped Linux capabilities and unprivileged user mappings, isolating potential model execution exploits from host root.

neurology GPU INFERENCE OPTIMIZATION // 06

Model Runtime & VRAM Scheduling

The compute stack optimizes GPU inference throughput across heavy developer workloads:

Layer Offloading & KV Caching

Ollama is configured with explicit GPU layer offloading parameters, maximizing the residency of attention layers in VRAM to sustain long context windows without swapping to system disk.

Multi-Client Concurrency

Maintains request queues allowing simultaneous prompt completions for both interactive OpenCode programming sessions and background security log correlation tasks.

checklist TESTING & VALIDATION // 07

Infrastructure Stability Validation

The compute stack is evaluated through operational stability and network isolation benchmarks:

check_circle Continuous Workload & Isolation Auditing VERIFIED IN INFRASTRUCTURE

Tested under continuous multi-agent inference runs to confirm thermal stability, VRAM release upon context completion, and packet capture audits verifying zero outbound telemetry.

Detailed hardware metrics, throughput logs, and container compose configurations are maintained in the project repository.

sync CURRENT STATUS // 08

Experimental (Hybrid Compute)

EXPERIMENTAL // HYBRID COMPUTE

The dedicated GPU host, containerized Ollama runtime, and hardened network interconnect are operational and serving active development and security engineering workloads.

update FUTURE ROADMAP // 09

Planned Improvements

[Planned] Multi-node cluster scaling to distribute large model parameters across secondary nodes.
[Planned] Automated dynamic model quantization switching based on active task urgency.
[Planned] Prometheus and Grafana dashboards for real-time VRAM and GPU thermal tracking.

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