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Average Ratings 0 Ratings
Description
BaseRT offers a robust inference runtime for LLMs specifically optimized for Apple Silicon, allowing developers to seamlessly access models from Hugging Face, engage in local conversations, or utilize an API compatible with OpenAI through a single command-line interface. Enhanced by meticulously crafted Metal kernels, BaseRT aims to provide exceptional prefill and decoding efficiency on M-series Macs, with benchmark results indicating it performs up to 6.4 times faster in prefill tasks compared to llama.cpp, 3.9 times faster than MLX, and achieves a decoding speed that is 1.33 times quicker. The basert CLI is equipped to manage tasks such as model downloading, conversion, interactive chat, serving capabilities, completion generation, benchmarking, inspection, and bundle signing. Its server functionalities are extensive, encompassing chat interactions, text completions, embeddings, transcription services, tool calls, continuous batching, paged key-value caching, and prefix caching, with support for models that can handle text, vision, and audio data. BaseRT employs a proprietary .base model format that incorporates Q2–Q8 affine quantization, optional AWQ calibration, and signed bundles, and it is capable of converting GGUF, Hugging Face, and MLX checkpoints. Furthermore, this innovative runtime is tailored to maximize the capabilities of Apple Silicon, making it an essential tool for developers in the AI space.
Description
On June 23, 2025, Microsoft unveiled Mu, an innovative 330-million-parameter encoder–decoder language model specifically crafted to enhance the agent experience within Windows environments by effectively translating natural language inquiries into function calls for Settings, all processed on-device via NPUs at a remarkable speed of over 100 tokens per second while ensuring impressive accuracy. By leveraging Phi Silica optimizations, Mu’s encoder–decoder design employs a fixed-length latent representation that significantly reduces both computational demands and memory usage, achieving a 47 percent reduction in first-token latency and a decoding speed that is 4.7 times greater on Qualcomm Hexagon NPUs when compared to other decoder-only models. Additionally, the model benefits from hardware-aware tuning techniques, which include a thoughtful 2/3–1/3 split of encoder and decoder parameters, shared weights for input and output embeddings, Dual LayerNorm, rotary positional embeddings, and grouped-query attention, allowing for swift inference rates exceeding 200 tokens per second on devices such as the Surface Laptop 7, along with sub-500 ms response times for settings-related queries. This combination of features positions Mu as a groundbreaking advancement in on-device language processing capabilities.
API Access
Has API
Yes
API Access
Has API
No
Integrations
Gemma 3
Yes
Gemma 4
Yes
Hugging Face
Yes
Llama 3.1
Yes
Llama 3.2
Yes
Mistral AI
Yes
OpenAI
Yes
Phi-3
Yes
Qwen3
Yes
Qwen3.5
Yes
Integrations
Gemma 3
No
Gemma 4
No
Hugging Face
No
Llama 3.1
No
Llama 3.2
No
Mistral AI
No
OpenAI
No
Phi-3
No
Qwen3
No
Qwen3.5
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
Yes
Linux
No
Chromebook
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
Base Compute
Founded
2026
Country
Australia
Website
www.basecompute.co/getbasert
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
blogs.windows.com/windowsexperience/2025/06/23/introducing-mu-language-model-and-how-it-enabled-the-agent-in-windows-settings/