A high-performance KV cache compression extension for llama.cpp designed to maximize context windows and model size on Apple Silicon.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install turboquant-plus
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install turboquant-plus using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is TurboQuant+?
TurboQuant+ is a sophisticated implementation of the TurboQuant research for llama.cpp, specifically optimized for Metal GPU kernels on Apple Silicon. This skill addresses the primary bottleneck of local LLM inference—memory consumption—by providing massive compression for the transformer KV cache. By integrating these advanced techniques into Openclaw Skills, users can fit significantly larger models and maintain longer context windows than previously possible on standard hardware, often achieving a 3.8x to 6.4x reduction in cache size with minimal perplexity impact.
TurboQuant+ Use Cases
- Running high-parameter models like Llama-70B or Command-R+ 104B on machines with limited RAM.
- Extending context windows up to 128K for deep document analysis without performance degradation.
- Optimizing local AI agents built with Openclaw Skills to handle larger memory-intensive tasks.
- Rescuing model quality on low-bit weight models using asymmetric quantization strategies.
How TurboQuant+ Works
- The system utilizes PolarQuant and Walsh-Hadamard rotation to normalize the distribution of KV cache tensors.
- It applies a user-selected compression level (turbo2, turbo3, or turbo4) to reduce the bit-width of the cache.
- The skill executes specialized Metal GPU kernels to handle real-time quantization and dequantization during the inference process.
- Through asymmetric K/V processing, it maintains the Key cache at higher precision to preserve attention quality while aggressively shrinking the Value cache.
- Layer-aware strategies, such as Boundary V, protect sensitive transformer layers to recover the majority of the quality gap found in standard quantization.
TurboQuant+ Setup
To leverage this capability within your Openclaw Skills workflow, you must use the TurboQuant fork of llama.cpp. Configure your local server with the following flags:
# Standard high-quality configuration (turbo4)
llama-server -m your_model.gguf --cache-type-k turbo4 --cache-type-v turbo4 -fa 1
# Maximum compression for large contexts (turbo3)
llama-server -m your_model.gguf --cache-type-k turbo3 --cache-type-v turbo3 -fa 1
# Memory-saving asymmetric setup for Q4_K_M models
llama-server -m model-Q4_K_M.gguf --cache-type-k q8_0 --cache-type-v turbo4 -fa 1
TurboQuant+ Data Schema & Taxonomy
TurboQuant+ organizes memory allocation based on the specific bit-rates assigned to the Key and Value caches. The following schema defines the compression performance:
| Cache Type | Bit-Width | Compression Ratio | Perplexity Impact (vs q8_0) |
|---|---|---|---|
| turbo2 | 2-bit | 6.4x | +6.48% |
| turbo3 | 3-bit | 4.6x-5.1x | +1.06% |
| turbo4 | 4-bit | 3.8x | +0.23% |
| q8_0 | 8-bit | 1.9x | Baseline |
TurboQuant+ Advanced Features
- Asymmetric K/V Compression: Allows keeping K at high precision (q8_0) while compressing V to turbo2 or turbo4 to maximize quality.
- Boundary V Protection: Automatically keeps the first and last layers at q8_0 to recover 37-91% of potential quality loss.
- Sparse V Dequantization: Skips low-weight V positions during the decode phase, increasing speed by up to 22.8% at 32K context.
- Custom GPU Memory Limits: Supports manual system overrides via sysctl to maximize context on high-end 128GB+ Mac systems.
- Full compatibility with the Openclaw Skills ecosystem for seamless local LLM automation.
SKILL.md
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