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Description
Mirai is an advanced platform tailored for developers that focuses on on-device AI infrastructure, enabling the conversion, optimization, and execution of machine learning models directly on Apple devices with a strong emphasis on performance and user privacy. This platform offers a cohesive workflow that allows teams to efficiently convert and quantize models, assess their performance, distribute them, and conduct local inference seamlessly. Specifically designed for Apple Silicon, Mirai strives to achieve near-zero latency and zero inference cost, while ensuring that sensitive data processing remains securely on the user's device. Through its comprehensive SDK and inference engine, developers can swiftly integrate AI functionalities into their applications, leveraging hardware-aware optimizations to maximize the capabilities of the GPU and Neural Engine. Additionally, Mirai features dynamic routing abilities that intelligently determine the best execution path for requests, whether that be locally on the device or utilizing cloud resources, taking into account factors such as latency, privacy, and workload demands. This flexibility not only enhances the user experience but also allows developers to create more responsive and efficient applications tailored to their users' needs.
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
API Access
Has API
Integrations
Llama
Anthropic
Claude Code
Cursor
DeepSeek
DeepSeek R1
Gemma
Gemma
Gemma 3
GitHub
Integrations
Llama
Anthropic
Claude Code
Cursor
DeepSeek
DeepSeek R1
Gemma
Gemma
Gemma 3
GitHub
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
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
Mirai
Founded
2024
Country
United States
Website
trymirai.com
Vendor Details
Company Name
oMLX
Country
United States
Website
omlx.ai/