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Description
Xinference serves as a comprehensive AI inference platform tailored for organizations aiming to utilize open models without the hassle of constructing their own serving infrastructure. Initially, teams can access over 300 open models via the Model API, all accessible through a singular OpenAI-compatible endpoint located in Australia. Transitioning from a current service provider is remarkably straightforward, requiring merely two lines of code. As demand increases, workloads can effortlessly shift to Dedicated Inference on allocated GPUs or even to a private setup within the client’s own cloud or data center. Each deployment is equipped with a unified control plane that features per-request logging, real-time TTFT and TPOT monitoring, role-based access management, audit logs, and single sign-on capabilities. Notably, Xinference prioritizes privacy by not training on or retaining customer data by default. Typical applications of the platform encompass enterprise retrieval-augmented generation (RAG), virtual customer assistants, intelligent agents, function calling, coding support, document extraction, and both speech and image generation. Furthermore, the flexibility of Xinference allows businesses to adapt their AI capabilities as their needs evolve.
Description
oMLX is an MLX server specifically designed for macOS, enhancing the efficiency and speed of local AI operations on Apple Silicon. It caters to the functional dynamics of coding agents by implementing paged SSD KV caching, which enables the persistence of cache blocks on disk; this means that previously accessed prefixes can be retrieved quickly across different requests and even after server restarts, thereby eliminating the need to recompute them from scratch. As a result, the time taken to generate the first token in lengthy contexts can be significantly reduced, dropping from a range of 30 to 90 seconds down to less than five seconds after the initial interaction. The server adeptly manages simultaneous requests through a continuous batching mechanism via mlx-lm’s BatchGenerator, which enhances overall generation throughput without requiring requests to queue up behind a single task. oMLX is capable of simultaneously serving a variety of models, including LLMs, vision-language models, embedding models, and rerankers, utilizing LRU eviction to manage memory constraints effectively. Furthermore, it is compatible with any MLX-format model sourced from Hugging Face, such as Qwen, LLaMA, Mistral, Gemma, DeepSeek, MiniMax, and GLM, and can also utilize models that are already present in the standard Hugging Face cache, directories associated with LM Studio, or any custom storage locations, ensuring a versatile user experience. This flexibility in model integration enhances the overall usability and practicality of oMLX for developers and researchers alike.
API Access
Has API
Yes
API Access
Has API
Yes
Screenshots View All
No images available
Integrations
Anthropic
No
Claude Code
No
Cursor
No
DeepSeek
No
GLM-4.1V
No
Gemma
No
Gemma
No
GitHub
No
Hugging Face
No
JSON
No
Integrations
Anthropic
Yes
Claude Code
Yes
Cursor
Yes
DeepSeek
Yes
GLM-4.1V
Yes
Gemma
Yes
Gemma
Yes
GitHub
Yes
Hugging Face
Yes
JSON
Yes
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
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
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
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
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)
No
In Person
No
Vendor Details
Company Name
Xinference
Founded
2026
Country
Australia
Website
xinference.co
Vendor Details
Company Name
oMLX
Country
United States
Website
omlx.ai/