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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
This innovative tool is designed for quantizing convolutional neural networks (CNNs). It allows for the transformation of both weights/biases and activations from 32-bit floating-point (FP32) to 8-bit integer (INT8) format, or even other bit depths. Utilizing this tool can greatly enhance inference performance and efficiency, all while preserving accuracy levels. It is compatible with various common layer types found in neural networks, such as convolution, pooling, fully-connected layers, and batch normalization, among others. Remarkably, the quantization process does not require the network to be retrained or the use of labeled datasets; only a single batch of images is sufficient. Depending on the neural network's size, the quantization can be completed in a matter of seconds to several minutes, facilitating quick updates to the model. Furthermore, this tool is specifically optimized for collaboration with DeePhi DPU and can generate the INT8 format model files necessary for DNNC integration. By streamlining the quantization process, developers can ensure their models remain efficient and robust in various applications.
API Access
Has API
API Access
Has API
Integrations
Gemma 3
Gemma 4
Hugging Face
Llama 3.1
Llama 3.2
Mistral AI
OpenAI
Phi-3
Qwen3
Qwen3.5
Integrations
Gemma 3
Gemma 4
Hugging Face
Llama 3.1
Llama 3.2
Mistral AI
OpenAI
Phi-3
Qwen3
Qwen3.5
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
$0.90 per hour
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Base Compute
Founded
2026
Country
Australia
Website
www.basecompute.co/getbasert
Vendor Details
Company Name
DeePhi Quantization Tool
Website
aws.amazon.com/marketplace/pp/prodview-bwtx6kzwg3gva