About This Site
Native macOS server for fast local AI model inference oMLX is a native macOS inference server for running local AI models on Apple Silicon using Apple's MLX framework. It provides paged SSD KV caching to reduce time-to-first-token for coding agents, continuous batching for higher throughput, and OpenAI- and Anthropic-compatible APIs. oMLX supports LLM, vision-language, embedding, and reranker models, along with tool calling, MCP integration, multi-model serving, and a native menu bar application with a web dashboard.
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Abliteration.ai
Abliteration.ai is an LLM provider for professional teams whose legitimate work gets blocked by the refusal behavior of other LLM providers. Security researchers, red teams, trust and safety teams, and ML engineers use it to run sensitive queries, test AI applications for jailbreaks and prompt injection, classify content, and generate synthetic training data at scale. Access is through OpenAI- and Anthropic-compatible APIs that serve models with refusal behavior reduced through abliteration. Your existing code keeps working after a one-line base-URL change. Control stays with you: you write the usage policy for each project, enforcement happens at the API layer with scoped keys, and every decision is logged with a reason code you can audit. A zero data retention option means prompts are processed and discarded, never stored and never used for training. A free tier is included so you can test the models before spending anything. Paid usage is per token, with prepaid credits that never expire and monthly plans from $20.
Abliteration.ai is an LLM provider built for high-risk industries: cybersecurity, AI red teaming, trust and safety, synthetic data generation, healthcare research, and defense and government workflows. Access is through OpenAI- and Anthropic-compatible APIs, so existing code migrates with a one-line base-URL change.
Most LLM providers tune their models for consumer chat, so they refuse exactly the prompts that professional teams need to run. A security engineer reproducing a CVE, a trust and safety analyst labeling coded harassment, an ML team generating jailbreak examples for an eval set: all of them hit the same wall. Abliteration.ai removes that wall by serving models with refusal behavior reduced through abliteration, while putting governance in your hands instead of the provider's.
How it works:
The core of the service is a chat completions API compatible with both the OpenAI and Anthropic formats. Point the OpenAI or Anthropic SDK, LangChain, LlamaIndex, or the Vercel AI SDK at the Abliteration.ai base URL and your existing code runs unchanged. The API supports streaming, image input (PNG, JPEG, WEBP, GIF up to 15 MB), document and image extraction, and an optional web search add-on billed per thousand searches.
On top of inference sits the Policy Gateway. You write rules per project that decide which request categories are allowed, which are rewritten, which are refused, and what must be logged. Scoped API keys enforce those rules per workload, and every allow, rewrite, and refusal carries a reason code. Audit logs can stream into your SIEM, so compliance and governance reviewers get the paper trail they ask for. Shadow mode lets you test policy changes against live traffic before enforcing them, and per-user and per-project token quotas keep spend predictable.
Privacy is configurable down to zero data retention: prompts and responses are processed in memory and discarded, never stored and never used for training.
Pricing starts with a free tier for testing. After that, usage is pay-per-token with prepaid top-ups ($20, $100, $500, or custom amounts) that never expire, or monthly plans at $20, $50, and $200 with usage discounts of 2.5%, 5%, and 10%. Enterprise plans add dedicated throughput, custom routing, and volume pricing.
Key Features AI
Core Features
Paged SSD KV caching for faster responses and reduced time-to-first-token
Continuous batching for concurrent request throughput
OpenAI-compatible and Anthropic-compatible APIs
Native macOS menu bar application
Web dashboard for model management, chat, and real-time metrics
Multi-model serving for LLM, VLM, embedding, and reranker models
Tool calling and MCP integration
Support for MLX-format models from Hugging Face
LRU model eviction when memory is limited
Automatic reuse of Hugging Face and LM Studio model caches
Advantages
Optimized specifically for Apple Silicon and macOS
SSD KV caching can significantly reduce agent response delays
Supports both OpenAI and Anthropic API formats
Works with Claude Code, OpenClaw, Cursor, and other compatible clients
Supports concurrent requests through continuous batching
Reads existing Hugging Face and LM Studio model directories without requiring re-downloads
Includes a signed and notarized native macOS application
Open source under the Apache 2.0 license
缺点:Requires Apple Silicon and macOS 15 or later
缺点:At least 16GB RAM is required, while larger models may need substantially more
缺点:Performance and supported model sizes depend heavily on available unified memory
缺点:Requires MLX-format models for model support
缺点:No hosted cloud service or cross-platform Windows and Linux application is described
缺点:Setup from source requires Python 3.10+ and command-line installation
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