Vmlx
Free, MIT-licensed local LLM inference for Apple Silicon Macs, with SSD-backed prefix caching and native OpenAI and Anthropic APIs.
Pick vMLX if you're on an Apple Silicon Mac and your pain is long multi-turn threads or several coding agents sharing one loaded model. The two differences it claims over LM Studio and Ollama are the SSD-resident full-precision prefix cache and the native Anthropic Messages endpoint — the second matters if you point Claude Code at ANTHROPIC_BASE_URL. The built-in GGUF-to-MLX converter and JANG quantisation save you waiting for someone else's conversion of a fresh model. Pass if you want published TTFT numbers before switching; the vendor's speed page is still marked pending. It costs nothing to try, which makes the call easy either way.
Verified 1d ago · liveness 69/100 · cite: rightaichoice.com/tools/vmlx
- Mac developers running coding agents with long file contexts
- Teams sharing one loaded model across several agents
- Privacy-bound or air-gapped work with no account or telemetry
- Anyone already on Cursor, Continue, Zed, Claude Code or Open WebUI
- Windows, Linux or Intel Mac users — Apple Silicon and macOS 14+ only
- Buyers who need a published, reproducible TTFT benchmark table before adopting
- Casual users who want a zero-config chatbot
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Skip vMLX if you are not on an Apple Silicon Mac running macOS 14 or later, or if you need a published time-to-first-token benchmark table before you will switch local inference engines.
Model downloads are on you: pulling a multi-gigabyte MLX or GGUF checkpoint eats disk and bandwidth, and the app's only self-initiated network request is the one you start.
vMLX is MIT-licensed and free to run, with no feature gates, no account and no telemetry — the cost is your Apple Silicon hardware and the time to configure it. Against free local runners LM Studio and Ollama the trade is the same $0 for a different feature set, and against paid cloud inference the comparison is your Mac's unified memory versus a per-token bill.
In short
Vmlx — Free, MIT-licensed local LLM inference for Apple Silicon Macs, with SSD-backed prefix caching and native OpenAI and Anthropic APIs. Best for Mac developers running coding agents with long file contexts, Teams sharing one loaded model across several agents, Privacy-bound or air-gapped work with no account or telemetry. Free to use.
What people actually say about Vmlx — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
36 mentions across 3 sources (Hacker News, YouTube, GitHub) · researched Aug 6, 2026.
Average across the 3 sources that answered — each source counts once, not each post.
- +MLX-native engine gives ~4x prefill speedup over Ollama on M-series.
- +Multi-context prefix caching handles multiple concurrent conversations without eviction.
- +Continuous batching supports up to 256 concurrent sequences for high throughput.
- +OpenAI-compatible API with streaming, tool calls, and structured output.
- +Completely free and open source with no cloud dependencies.
- −Frequent reliability bugs: nanobind crashes, tool-call failures, model-specific hangs.
- −Structured output is unreliable, often needs manual JSON/XML repair.
- −Documentation and guides are sparse; users must dig into GitHub issues.
- −Performance gains depend on MLX-compatible models, limiting choice.
- −Security default: API gateway not bound to localhost by default.
- • No monetary cost, but you'll spend time debugging issues.
- • Requires powerful hardware (large unified memory) for peak performance.
Viability Score
How well maintained and how widely used is Vmlx? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Prefix cache resumes from the already-processed conversation prefix instead of re-reading it
- Paged KV cache spills to SSD in fixed-size pages at full precision, not quantised
- Stored KV cache survives an app restart and restores the same prefix
- Continuous batching serves several agents from one loaded model without padding waste
- Speculative decoding drafts several tokens ahead and verifies them in one pass
- Native Anthropic Messages endpoint, not a compatibility shim
- OpenAI-compatible API with streaming over HTTP and SSE
- Ollama-compatible endpoint via OLLAMA_HOST
- Built-in GGUF-to-MLX converter plus JANG quantisation
- Unified memory use with no host-device copy and no VRAM ceiling
- Mac app with chat UI, sessions, model browser and per-turn cost view
- Gateway in front of every loaded model on 127.0.0.1:8000
- Install via pip package (vmlx serve) or signed Mac app
- Quantisation applies to live in-memory KV only, never to stored cache
- No account, no sign-in, no telemetry, no cloud call in the inference path
About Vmlx
vMLX is a free, MIT-licensed macOS app and pip package that runs large language models locally on Apple Silicon. You download the app or run `pip install vmlx && vmlx serve mlx-community/Qwen3-8B-4bit`, and you get an OpenAI- and Anthropic-compatible server on 127.0.0.1:8000 that Cursor, Continue, Zed, Claude Code, Open WebUI or your own scripts can talk to without config changes. The engine's claim is that a long conversation should not get slower every turn: a prefix cache recognises what has already been processed and resumes from it, a paged KV cache spills full-precision KV to SSD in fixed-size pages so a long thread survives a restart, continuous batching lets several agents share one loaded model, and speculative decoding drafts tokens ahead and verifies them in one pass. A built-in GGUF-to-MLX converter plus JANG quantisation means you are not waiting on someone else's conversion. The vendor's own comparison table, checked September 2026 against each project's documentation, concedes LM Studio and Ollama already match vMLX on OpenAI compatibility and offline operation, and claims three presence-or-absence gaps: SSD prefix caching at full precision, a native Anthropic Messages endpoint, and the built-in converter. The speed table is explicitly marked "benchmark pending" — no TTFT or decode figures are published until host, model and context length can be attached. Everything runs on M1 through M5 Macs on macOS 14+, with no account and no telemetry.
Behind the Verdict
vMLX is a narrow, honest tool with a clear thesis. Most local runners re-read your whole conversation on every turn, so a long coding thread gets slower the longer it runs; vMLX keeps the processed prefix on disk at full precision and resumes from it. That is the product. Around it sit four supporting pieces: a paged KV cache that spills to SSD in fixed-size pages so a conversation survives a restart, continuous batching so a coding agent and a chat window do not fight over the GPU, speculative decoding to bank draft tokens, and a GGUF-to-MLX converter with JANG quantisation so you can bring an unpublished model yourself. Strengths. The install paths are short and real — a signed Mac app with a chat UI, sessions, a model browser and a per-turn cost view, or `pip install vmlx` followed by `vmlx serve mlx-community/Qwen3-8B-4bit`. Both expose the same server on 127.0.0.1:8000, and both API dialects are described as native rather than shimmed, which is why Claude Code can be pointed at it with ANTHROPIC_BASE_URL. Unified memory is used properly: no host-device copy and no VRAM ceiling. Privacy is structural rather than a setting — no account, no telemetry, and the only network request the app makes is one you start (a model download or an update check). The licence is MIT, so nothing is gated behind a tier. Weaknesses. It is Apple Silicon and macOS 14+ only — Windows, Linux and Intel Macs are out. It is not a zero-config chatbot; the engine exposes real tuning surface and you will configure flags and models. The vendor's own comparison table, checked September 2026, concedes that LM Studio and Ollama already match vMLX on OpenAI compatibility and fully-offline operation, so the case rests entirely on caching, scheduling and the Anthropic endpoint. Most importantly, the headline speed page is explicitly marked "benchmark pending" and deliberately withholds time-to-first-token and prefix-hit-rate figures — the vendor argues a TTFT number means nothing without the context length it was measured at, which is a fair point, but it does mean you cannot check the central claim before installing. Model size is bounded by unified memory, so a 16 GB Mac caps what you can load. Where it fits. Mac developers running Cursor, Continue, Zed, Claude Code or Open WebUI against long file contexts, teams that want several agents sharing one loaded model instead of queueing, air-gapped or privacy-bound work, and researchers converting unpublished GGUF models locally. Where it doesn't: anyone on non-Apple hardware, anyone who needs a reproducible benchmark table before adopting, and casual users who want a zero-config chat app.
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Real-world workflow fit
Concrete scenarios for the personas Vmlx actually fits — and what changes day-one when you adopt it.
Set ANTHROPIC_BASE_URL to http://127.0.0.1:8000, serve a JANG-quantised model through `vmlx serve`, and let the prefix cache resume the thread instead of re-reading the whole file context each turn.
Outcome: The long multi-turn thread stops degrading turn over turn, the conversation survives a restart from the SSD-resident full-precision KV, and no prompt leaves the Mac.
Load one model once and let continuous batching serve a coding agent and a chat window from the same loaded weights, rather than queueing one request at a time.
Outcome: Agents share the GPU without padding waste, and the per-turn cost view in the app shows what each session actually consumed.
Use the built-in GGUF-to-MLX converter and JANG quantisation in the app to bring the model over locally instead of waiting for someone to publish an MLX conversion.
Outcome: The model runs on the same 127.0.0.1:8000 endpoint as everything else, with no upload of the weights to a third party.
Use Cases
- Run an MLX-converted model locally on an M-series Mac for instant chat completions with no cloud account
- Point Claude Code at 127.0.0.1:8000 via ANTHROPIC_BASE_URL and keep working without changing your client config
- Serve several concurrently chatting agents from one loaded model using continuous batching
- Keep a long multi-turn coding thread fast by resuming from the SSD-resident full-precision prefix cache
- Convert and quantise an unpublished GGUF model to MLX locally instead of waiting for a hosted conversion
- Run a fully offline, telemetry-free inference server for air-gapped or privacy-bound work
Models Under the Hood
as of 2026-10-17
Limitations
- macOS 14+ on Apple Silicon (M1–M5) only; Windows, Linux and Intel Mac users are excluded.
- It is not a zero-config chatbot — the engine exposes real tuning surface, so expect to configure flags and models manually.
- The vendor's headline speed table is explicitly marked "benchmark pending," so no time-to-first-token, prefix-hit-rate or decode figures are published yet.
- Model size is bounded by unified memory rather than a setting.
- The vendor's own comparison table, checked September 2026, concedes that LM Studio and Ollama already match vMLX on OpenAI compatibility and fully-offline operation, so the case rests on caching, scheduling and the native Anthropic endpoint.
as of 2026-10-10
Verification history
We have re-verified Vmlx 10 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 10 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Vmlx tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free (MIT)
$0
Ideal for
Mac developers, researchers and privacy-bound teams on Apple Silicon who want local OpenAI- and Anthropic-compatible inference without an account or subscription.
What this tier adds
Starting tier and only tier: full inference engine with no feature gates, no sign-in, no telemetry and no cloud call in the inference path.
Where the pricing makes sense
The company stage and team size where Vmlx's pricing actually pencils out — and where peers do it cheaper.
vMLX is MIT-licensed and free to run, with no feature gates, no account and no telemetry — the cost is your Apple Silicon hardware and the time to configure it. Against free local runners LM Studio and Ollama the trade is the same $0 for a different feature set, and against paid cloud inference the comparison is your Mac's unified memory versus a per-token bill.
Setup time & first value
How long it actually takes to get something useful out of Vmlx — broken out by persona, not the marketing-page minute.
App path: download the signed Mac app, drag it to Applications, open it — minutes to a first chat once a model is downloaded. Engine path: `pip install vmlx` then `vmlx serve mlx-community/Qwen3-8B-4bit`, then point your client at 127.0.0.1:8000 — under ten minutes. Budget longer if you are converting a GGUF model yourself or tuning flags, since the engine exposes real configuration surface.
Switching to or from Vmlx
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Ollama: point OLLAMA_HOST at the vMLX endpoint, since vMLX exposes an Ollama-compatible endpoint alongside OpenAI and Anthropic.
- →From LM Studio: move your client config to http://127.0.0.1:8000 — Cursor, Continue, Zed and Open WebUI connect the same way.
- →From a cloud OpenAI endpoint: set the base URL to 127.0.0.1:8000 and keep your existing client code, since the OpenAI API dialect is native.
- →From a hosted Anthropic endpoint for Claude Code: set ANTHROPIC_BASE_URL to the local server and leave the client config otherwise unchanged.
- ↗To LM Studio: both expose an OpenAI-compatible endpoint and run offline, and the vendor's own September 2026 table shows parity on OpenAI compatibility.
- ↗To Ollama: same OpenAI-compatible and offline overlap, with an Ollama-compatible endpoint on both sides.
- ↗To a cloud inference API: switch the base URL back and accept per-token billing in exchange for hardware independence.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Vmlx”, and we withheld 6: 6 could not be judged, because “Vmlx” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Vmlx.
Official links
Tools that pair well with Vmlx
Common stack mates teams adopt alongside Vmlx, with the specific reason each pairing earns its keep.
Ollama
Ollama runs open-weight LLMs locally or in the cloud with one command and an OpenAI-compatible API.
RWKV Runner
Free, open-source desktop app for running and fine-tuning RWKV RNN language models locally with effectively unbounded context.
Atomic Chat
Atomic Chat runs 1,000+ local LLMs offline on your own hardware — free, open-source, no account, no rate limits.
Featured Head-to-Head Comparisons
Vmlx vs Spider Cloud
Spider Cloud and VMLX serve entirely different needs — one is a web data extraction API for AI agents, the other a local LLM inference engine for Apple Silicon. Choose Spider Cloud if you need real-time web data for RAG or AI pipelines; choose VMLX if you want private, high-speed local inference on a Mac with agentic features. They are not competitors but complementary tools for different stages of an AI workflow.
Vmlx vs Temporal Ai
Choose Temporal AI if you need resilient, stateful orchestration for AI agents or multi-step workflows across distributed systems, especially with human-in-the-loop and retry guarantees. Choose vMLX if your priority is running LLMs locally on a Mac with maximum speed and privacy, leveraging Apple Silicon's unified memory. They serve fundamentally different needs: Temporal is a workflow platform; vMLX is a local inference server.
Vmlx vs Voyage Ai
Voyage AI and vMLX serve entirely different needs. Voyage AI is a cloud embedding/reranker API for enterprises building high-accuracy RAG on domain-specific data (finance, legal) with long-context support. vMLX is a free, offline inference engine for Apple Silicon users who need fast local LLM execution with advanced caching and MCP tool integration. If your priority is retrieval accuracy at scale, choose Voyage AI. If you need local, low-latency LLM inference for agentic workflows, choose vMLX.
Alternatives to Vmlx
View all
Ollama
Ollama runs open-weight LLMs locally or in the cloud with one command and an OpenAI-compatible API.
RWKV Runner
Free, open-source desktop app for running and fine-tuning RWKV RNN language models locally with effectively unbounded context.
Atomic Chat
Atomic Chat runs 1,000+ local LLMs offline on your own hardware — free, open-source, no account, no rate limits.
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