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    local llm workloads

    1

    Manage the full local LLM lifecycle: optimize VRAM fit, deploy secure LAN-served apps, and orchestrate GPU training.

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    local llm workloads

    local llm workloads

    Example session with this skill installed

    Check if Llama 3.1 8B fits on GPU-NODE-2 with 100% VRAM residency, then pull and serve it for a local chat app.

    • Read your context and instructions
    • Compiled the local llm workloads

    I have verified the hardware constraints for GPU-NODE-2 and the model specifications for Llama 3.1 8B. The model requires approximately 5.5GB of VRAM for the 4-bit quant, which fits well within the 24GB pool even with overhead.

    Deployment Plan for Llama 3.1 8B

    StepActionCommand/EndpointTarget NodeResult
    1Health Checkcurl http://gpu-node-2:11434/api/psGPU-NODE-2Idle
    2Pull Modelollama pull llama3.1GPU-NODE-2Success
    3Create ModelfileFROM llama3.1\nPARAMETER num_gpu 99GPU-NODE-2100% VRAM
    4Serve API:11434/v1/chat/completionsLANActive
    5SecurityApply Path-Traversal GuardMAIN-NODESecure

    Resource Allocation

    • VRAM Residency: 100% GPU (42 layers total)
    • Disk Space: 4.7GB utilized on /var/lib/ollama
    • Latency Strategy: Direct /v1 routing to bypass agent loop overhead
    • Timetable Status: Clear for the next 4 hours (next render scheduled 02:00)

    Next steps

    • Run ollama ps to verify the PROCESSOR column shows 100% GPU.
    • Point your frontend to the http://gpu-node-2:11434/v1 endpoint.
    • Monitor the storage share if you plan to move to a fine-tuning phase to avoid root FS saturation.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Ensure 100% VRAM residency for local models to eliminate CPU-offload latency.Deploy conversational backends with LAN-safe security guards and voice-out.Orchestrate Unsloth fine-tuning jobs on Linux/WSL nodes.Verify model fit and disk availability before pulling large LLM weights.

    About this skill

    The problem

    Running LLMs on local hardware often results in silent CPU-offloading, high latency, or out-of-memory errors. Developers struggle to manage VRAM residency across multiple nodes while balancing GPU availability against scheduled render tasks and cron jobs.

    What it does

    • Calculates exact layer counts and parameter sizes to ensure 100% VRAM residency and zero CPU-offload.
    • Deploys conversational wrappers using a proven /v1 API call shape with static path-traversal guards for LAN security.
    • Orchestrates fine-tuning workflows on GPU nodes using pinned Unsloth and Transformers version matrices.
    • Verifies machine liveness and timetable availability before initiating resource-heavy model pulls or training runs.

    Frameworks & tools

    Ollama, Linux/WSL, Unsloth, Transformers, PEFT, Python, and Qwen2 architectures.

    Why this beats prompting it yourself

    General AI prompts often ignore physical hardware constraints like root filesystem disk limits or specific VRAM splits. This skill enforces strict lab-tested rules for model loading and version-pinning that prevent common 404 client-config traps and dependency hell.

    Use cases

    • Provisioning a local model on a specific GPU node with verified 100% GPU processing.
    • Building a low-latency tutor or quiz app using browser-based voice-out and a static backend.
    • Executing fine-tuning jobs for specialized corpora while respecting scheduled maintenance windows.

    Known limitations

    Requires SSH access to Linux-based nodes. Cannot fine-tune directly from served GGUF quants, requires base weights.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

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    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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    Recently published to Agensi

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    Security scanned

    Verified clean 6 days ago

    • Passed all security checks, Safe to install

    Listed6 days ago

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