Average Ratings 0 Ratings
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
LMCache is an innovative open-source Knowledge Delivery Network (KDN) that functions as a caching layer for serving large language models, enhancing inference speeds by allowing the reuse of key-value (KV) caches during repeated or overlapping calculations. This system facilitates rapid prompt caching, enabling LLMs to "prefill" recurring text just once, subsequently reusing those saved KV caches in various positions across different serving instances. By implementing this method, the time required to generate the first token is minimized, GPU cycles are conserved, and throughput is improved, particularly in contexts like multi-round question answering and retrieval-augmented generation. Additionally, LMCache offers features such as KV cache offloading, which allows caches to be moved from GPU to CPU or disk, enables cache sharing among instances, and supports disaggregated prefill to optimize resource efficiency. It works seamlessly with inference engines like vLLM and TGI, and is designed to accommodate compressed storage formats, blending techniques for cache merging, and a variety of backend storage solutions. Overall, the architecture of LMCache is geared toward maximizing performance and efficiency in language model inference applications.
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
Free
Free Trial
No
Free Version
Yes
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
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
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
No
Live Training (Online)
Yes
In Person
No
Vendor Details
Company Name
Base Compute
Founded
2026
Country
Australia
Website
www.basecompute.co/getbasert
Vendor Details
Company Name
LMCache
Country
United States
Website
lmcache.ai/