skills.rest
AmitabhainArunachala
AmitabhainArunachalaCOMMUNITY
Explore all skills by this author →
GitHub

universal-inference-runtime

Run LLM inference across Ollama and llama.cpp backends with hot model swapping.

1|Updated Feb 14, 2026
Search Tags:#deployment#inference#ollama#multi-backend#llama-cpp#model-serving
One-click install
npx skills add https://github.com/AmitabhainArunachala/clawd --skill universal-inference-runtime
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: universal-inference-runtime
Source: https://github.com/AmitabhainArunachala/clawd/tree/main/skills/universal-inference-runtime
Command: npx skills add https://github.com/AmitabhainArunachala/clawd --skill universal-inference-runtime

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests>=2.28.0, llama-cpp-python>=0.2.0, numpy>=1.24.0, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill simplifies AI model deployment and inference by providing a single, unified API that works across multiple backends like Ollama and llama.cpp, eliminating vendor lock-in and hardware constraints.

Core Features & Use Cases

  • Multi-Backend Support: Seamlessly switch between Ollama, llama.cpp, and potentially vLLM or cloud providers.
  • Hot Model Swapping: Change AI models on the fly without restarting the application.
  • Hardware Agnostic: Automatically detects and utilizes CUDA, ROCm, Metal/MPS, or CPU.
  • Use Case: A developer needs to test a prompt against Gemma and then Llama 3.1 models. They can load Gemma using Ollama, get a response, and then instantly swap to Llama 3.1 without reconfiguring their environment.

Quick Start

Initialize the runtime and load the 'gemma3:4b' model to generate a response to 'Hello!'.

Frequently Asked Questions about universal-inference-runtime

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

FAQPage Schema
How do I run LLM inference across multiple backends like Ollama and llama.cpp?▼

A unified inference runtime allows you to run LLM inference across multiple backends like Ollama and llama.cpp through a single API. This abstracts away backend-specific configurations and prevents vendor lock-in during AI model deployment.

Can I swap AI models on the fly without restarting my application?▼

Yes, hot model swapping allows you to change AI models on the fly without restarting your application. This feature lets you instantly switch between different LLMs to test prompts without reconfiguring your deployment environment.

Does this model serving runtime work with CUDA, ROCm, and Metal hardware?▼

Yes, this inference runtime works with CUDA, ROCm, and Metal/MPS hardware. It features hardware auto-detection to automatically identify and utilize the available compute resources, including CPU fallback, for flexible LLM deployment.

What is the best way to avoid vendor lock-in when deploying AI models?▼

Using a unified inference runtime is the best way to avoid vendor lock-in when deploying AI models. By supporting multiple backends like Ollama and llama.cpp, it removes hardware constraints and decouples your application from a single serving framework.

Do I need to reconfigure my environment to test prompts against different LLMs?▼

No, you do not need to reconfigure your environment to test prompts against different LLMs. The unified runtime API handles backend abstraction and enables hot model swapping, allowing immediate transitions between models during inference.

Related Skills

Explore More Software Engineering Skills→
CyapstayeCyapstaye
9

llama-cpp

Discover GGUF models on Hugging Face and run local inference with llama.cpp.

Community
Advanced
ovachieverovachiever
52

instructor

Extract structured data from LLM responses with Pydantic validation and retries.

Community
Advanced
legoutlegout
3

starhtml

Compile Python objects into Datastar-powered reactive HTML components.

Community
Intermediate
ihatesea69ihatesea69
3

sglang

Serve LLMs with RadixAttention prefix caching and constrained decoding.

Community
Advanced