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Average Ratings 0 Ratings
Description
Router acts as a gateway designed to lower inference costs by selecting the most cost-effective model that satisfies performance requirements for each request. It simplifies access for developers by providing a single endpoint and API key, allowing them to utilize a variety of both closed and open-source AI models from numerous providers, including OpenAI, Anthropic, Grok, and Fireworks, thereby eliminating the need to connect to each provider individually. Initially, requests are processed through Router, which enables tracking of usage, model selection, provider information, and associated costs, ensuring that workloads are efficiently directed to alternative options when quality remains intact. With Router Strategies, developers can establish their own cost and performance priorities for different request types or rely on pre-set benchmarks derived from actual production experiences. The system is responsive to real-time conditions such as latency, availability, failures, and rate limits, allowing for the seamless rerouting of eligible requests to other available models when a particular provider is unable to fulfill them. This flexibility enhances the overall efficiency and reliability of the service, ensuring that developers can meet their application demands effectively.
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
Integrations
Anthropic
Yes
OpenAI
Yes
Claude Code
No
Cursor
No
Fireworks AI
Yes
GLM-4.1V
No
GPT-5.6 Luna
Yes
GPT-5.6 Sol
Yes
Gemma
No
Gemma
No
Integrations
Anthropic
Yes
OpenAI
Yes
Claude Code
Yes
Cursor
Yes
Fireworks AI
No
GLM-4.1V
Yes
GPT-5.6 Luna
No
GPT-5.6 Sol
No
Gemma
Yes
Gemma
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
No
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
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)
No
In Person
No
Vendor Details
Company Name
Ramp
Founded
2019
Country
United States
Website
router.com
Vendor Details
Company Name
oMLX
Country
United States
Website
omlx.ai/