PowerInfer
TOOLHigh-speed LLM inference for local deployment on consumer GPUs. Achieves up to 11x speedup over llama.cpp on RTX 4090 by exploiting power-law neuron activation patterns. MIT licensed
View on GitHubOverview
Free tool — PowerInfer. High-speed LLM inference for local deployment on consumer GPUs. Achieves up to 11x speedup over llama.cpp on RTX 4090 by exploiting power-law neuron activation patterns. MIT l
What this tool does
"PowerInfer" is a tool you can copy, adapt, and run with any modern AI assistant. High-speed LLM inference for local deployment on consumer GPUs. Achieves up to 11x speedup over llama.cpp on RTX 4090 by exploiting power-law neuron activation patterns. MIT licensed On OpenRuna it sits inside a connected graph of related tools, tools, and datasets, so branching into adjacent resources is one click away. Open a new conversation and paste it in, wire it into an agent, or keep it in your team's prompt library.
Use cases
- Keep it in a shared library as the canonical version of this tool for your organisation.
- Fork it as a baseline and layer in your own project context, constraints, and examples.
- Hand "PowerInfer" to a new teammate so their tool output matches your team's quality bar from day one.
- Use "PowerInfer" when you need a repeatable tool for professional work without rewriting instructions every time.
Example output
Ask the model to apply "PowerInfer" to your scenario and it returns a structured answer — clear sections, actionable steps, and assumptions stated upfront — ready to paste into docs, tickets, or code comments. Expect 2–3 iterations to dial in tone and depth for your context.
Tips by platform
Claude
With Claude, drop this tool into Project knowledge so every chat in the project inherits it. Ask Claude to restate the goal first, then run — it catches edge cases early.
ChatGPT
In ChatGPT, start a fresh chat and paste this tool verbatim, then follow up with "apply this to [your context]." Pick a current GPT model for coding or reasoning tasks.
Cursor
Cursor users can store this tool as a project rule so Agent mode applies it automatically. Mention it with @ when you want it scoped to a single task.
Frequently asked questions
- What is "PowerInfer"?
- It is a tool listed on OpenRuna — High-speed LLM inference for local deployment on consumer GPUs. Achieves up to 11x speedup over llama.cpp on RTX 4090 by exploiting power-law neuron activation patterns. MIT licensed You can copy and adapt it for ChatGPT, Claude, Cursor, or any other AI assistant.
- Is "PowerInfer" free to use?
- Most OpenRuna resources are open or CC0-licensed. Check the license shown on this page before commercial use; premium collections are clearly marked as such.
- How do I get the best results from this tool?
- Replace any placeholders, add your project context, and ask the model to confirm its assumptions first. Iterate over 2–3 follow-up turns rather than expecting a perfect first response.
- Does "PowerInfer" work with both Claude and ChatGPT?
- Yes — it is model-agnostic text, so it runs on Claude, ChatGPT, Gemini, and Cursor. The tips on this page cover each of those assistants specifically.
- Where can I find resources related to "PowerInfer"?
- Scroll to the Related resources section on this page, or open the matching category hub on OpenRuna to find connected prompts, tools, agents, and datasets in the same topic area.
Related resources
- Tool
v0 Prompts and Tools — ReadFile
Reads file contents intelligently - returns complete files when small, paginated chunks, or targeted chunks when large based on your query. **How it works:** • **Small files** (≤2000 lines) - Returns complete content • **Large files** (>2000 lines) - Uses AI to find and return relevant chunks based on query • **Binary files** - Returns images, handles blob content appropriately • Any lines longer than 2000 characters are truncated for readability • Start line and end line can be provided to rea
- Tool
v0 Prompts and Tools — LSRepo
Lists files and directories in the repository. Returns file paths sorted alphabetically with optional pattern-based filtering. Common use cases: • Explore repository structure and understand project layout • Find files in specific directories (e.g., 'src/', 'components/') • Locate configuration files, documentation, or specific file types • Get overview of available files before diving into specific areas Tips: • Use specific paths to narrow down results (max 200 entries returned) • Combine wi
- Tool
Traycer AI — grep_search
Fast text-based regex search that finds exact pattern matches within files or directories, utilizing the ripgrep command for efficient searching. Results will be formatted in the style of ripgrep and can be configured to include line numbers and content. To avoid overwhelming output, the results are capped at 50 matches. Use the include patterns to filter the search scope by file type or specific paths. This is best for finding exact text matches or regex patterns. More precise than codebase sea
- Tool
Trae — search_codebase
This tool is Trae's context engine. It: 1. Takes in a natural language description of the code you are looking for; 2. Uses a proprietary retrieval/embedding model suite that produces the highest-quality recall of relevant code snippets from across the codebase; 3. Maintains a real-time index of the codebase, so the results are always up-to-date and reflects the current state of the codebase; 4. Can retrieve across different programming languages; 5. Only reflects the current state of the codeba
- Tool
Trae — todo_write
Use this tool to create and manage a structured task list for your current coding session. This helps you track progress, organize complex tasks, and demonstrate thoroughness to the user. It also helps the user understand the progress of the task and overall progress of their requests.
- Tool
Same.dev — task_agent
Launches a highly capable task agent in the USER's workspace. Usage notes: 1. When the agent is done, it will return a report of its actions. This report is also visible to USER, so you don't have to repeat any overlapping information. 2. Each agent invocation is stateless and doesn't have access to your chat history with USER. You will not be able to send additional messages to the agent, nor will the agent be able to communicate with you outside of its final report. Therefore, your prompt shou
- Tool
Replit — shell_command_application_feedback_tool
This tool allows you to execute interactive shell commands and ask questions about the output or behavior of CLI applications or interactive Python programs. ## Rules of usage: 1. Provide clear, concise interactive commands to execute and specific questions about the results or interaction. 2. Ask one question at a time about the interactive behavior or output. 3. Focus on interactive functionality, user input/output, and real-time behavior. 4. Specify the exact command to run, including any ne
- Tool
Replit — str_replace_editor
Custom editing tool for viewing, creating and editing files * State is persistent across command calls and discussions with the user * If `path` is a file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep * The `create` command cannot be used if the specified `path` already exists as a file * If a `command` generates a long output, it will be truncated and marked with `<response clipped>` * The `undo_edi