Mirai is live · custom pricing · last verified 8 Nov 2026 · viability monitor
Mirai
On-device LLM inference for Apple Silicon, built to hit sub-200ms interactive AI latency
If a slow response is the thing that breaks your Apple app, Mirai is one of the few stacks designed from the silicon constraint upward. The batch-size-1 runtime and architecture-aware quantization are the right primitives, and MIT licensing on uzu and lalamo means your engineers can audit the machinery. It rewards teams with systems depth and frustrates anyone hoping for a drop-in hosted endpoint.
Verdict generatedVerified 5d ago · liveness 69/100 · cite: rightaichoice.com/tools/mirai
- iOS and macOS developers whose AI must answer inside a continuous interaction loop
- Teams building ambient or always-on assistants that must keep data on device
- Engineers with in-house ML or systems capability who want to own quantization and runtime
- Researchers working on on-device decoding, KV cache and speculative decoding
- Teams that need Android, Windows or web coverage today — Android is listed as Soon
- Buyers who want a managed endpoint they can call without touching the runtime
- Batch or throughput-shaped workloads — the engine targets batch size = 1
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Skip Mirai if you need Android, Windows or web coverage today, or if you want a managed inference endpoint you can call without owning model conversion and quantization yourself.
Commercial terms run through a contact form at contact@trymirai.com, so there's no published rate card to budget against until you've had the sales conversation.
Mirai sells through a contact form rather than published tiers, so there is no list price to compare. The free, MIT-licensed uzu and lalamo libraries mean solo developers and research teams can evaluate the engine at zero licence cost, and the paid commercial conversation is where an SDK or support agreement presumably lands. That puts Mirai in the same commercial shape as other deep-infrastructure vendors: cheap to trial, opaque to budget until you talk to them.
In short
Mirai — On-device LLM inference for Apple Silicon, built to hit sub-200ms interactive AI latency. Best for iOS and macOS developers whose AI must answer inside a continuous interaction loop, Teams building ambient or always-on assistants that must keep data on device, Engineers with in-house ML or systems capability who want to own quantization and runtime. Pricing is on request (custom quotes).
What is Mirai?
Mirai — On-device LLM inference for Apple Silicon, built to hit sub-200ms interactive AI latency. Best for iOS and macOS developers whose AI must answer inside a continuous interaction loop, Teams building ambient or always-on assistants that must keep data on device, Engineers with in-house ML or systems capability who want to own quantization and runtime. Pricing is on request (custom quotes).
Mirai Labs is chasing a specific number: 1,000 tokens per second on an M4 Pro, because at that speed a model stops behaving like a chatbox and starts behaving like an interface. The company builds the whole on-device stack for Apple Silicon rather than trimming a server model down to phone size — local architectures designed for memory-bound execution and realtime decoding, the uzu inference engine (open source in Rust under MIT), quantization co-designed with model architecture, and an application layer with lalamo for model conversion, a CLI that chats with models and serves them as a local API, a macOS app, a metrics dashboard, and a models library on Hugging Face (64.4K downloads at the latest scrape). The reasoning is laid out plainly on the site: cloud inference optimizes for big batches and saturates compute, while a device runs one request alone under memory and bandwidth pressure. Mirai's runtime executes at batch size = 1 and optimizes tensor multiplications and other ops for the neural accelerator with that constraint in mind. Sparse buffers handle the KV cache. Developers get drop-in snippets for Python, Swift, TypeScript and Rust, with SDK paths for chat, structured output and cloud execution. u zu picked up speculative decoding in a September 2026 writeup, and the team publishes research openly — an October 2026 post called "The Computer Should Know the Way" is the latest. It's aimed at iOS and macOS teams whose AI has to answer inside a continuous interaction loop, plus engineers with in-house ML capability who want to own quantization and the runtime rather than rent an endpoint. Where it sits: managed inference APIs sell you an endpoint and hide the machinery; cross-platform runtimes ask you to accept the same abstractions everywhere. Mirai's bet is narrower and deeper — Apple Silicon first, with Android marked "Soon" on the site.
Key features
- On-device LLM inference on Apple Silicon (M-series)
- uzu: open-source Rust inference engine (MIT, 3.2K GitHub stars)
- Speculative decoding in uzu (September 2026)
- lalamo: open-source model conversion and optimization tooling (MIT)
- Batch-size-1 runtime built for latency-first execution
- Hardware-aware optimization of tensor multiplications and other ops
- Quantization co-designed with model architecture
- Sparse buffers for KV cache
- CLI that chats with models and serves them as a local API
- macOS app for testing and deploying local models
- Metrics dashboard for runtime performance
- Pre-optimized models library on Hugging Face (64.4K downloads)
- Drop-in snippets for Python, Swift, TypeScript and Rust
- SDK paths for chat, structured output and cloud execution
- 1,000 tokens/second execution-speed target on M4 Pro
Behind the verdict
Pick Mirai when the latency budget is the product. A continuous assistant that parses, validates, ranks and renders while the user keeps talking has no room for a cloud round-trip, and Mirai's batch-size-1 runtime is built for exactly that shape of workload. If you're shipping a macOS or iOS feature where the AI has to feel instant and the data has to stay on device, this is the shortest route we've seen to owning the whole loop. The second reason to choose it is licensing. uzu and lalamo are open source in Rust under MIT, so your engineers can read the tensor optimizations, the sparse-buffer KV cache handling and the September 2026 speculative decoding work instead of treating the runtime as a black box. For a team with in-house ML capability, that's worth more than a slightly better managed API. Pass if you need Android, Windows or web coverage today — Android is listed as Soon on the vendor site, and the whole bet is Apple Silicon. Pass too if you want a managed endpoint you can call without ever touching conversion or quantization. The abstractions here are thin on purpose, and thin abstractions require people who know what they're doing. Also skip throughput-shaped work. The runtime targets batch size = 1, so if you're batching requests for cost efficiency, a cloud provider is the correct tool and Mirai isn't. In practice, budget for the ramp. You'll spend real time on model conversion through lalamo, tuning quantization for your accuracy floor, and watching the metrics dashboard to see where latency actually goes. The 1,000 t/s figure is an execution-speed target on M4 Pro that the company states plainly rather than dressing up as a benchmark — treat it as a direction, then measure your own pipeline end to end. The closest alternative is a managed inference
Limitations
- Apple Silicon only — iOS and macOS — with Android labeled 'Soon' on the site.
- The stack is developer infrastructure: a Rust inference engine (uzu), model conversion tooling (lalamo), a CLI and a macOS app, so real ML or systems engineering fluency is required rather than drop-in usage.
- The homepage states 1,000 tokens per second as an execution speed the company is aiming for, not a guaranteed benchmark.
- There is no published pricing; commercial terms go through the contact form at contact@trymirai.com, so a conversation is needed before you can budget.
as of 2026-10-09
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Is Mirai still active?
Mirai is live · custom pricing · last verified 8 Nov 2026 · viability monitor.
Viability score
How well maintained and how widely used is Mirai? 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: November 2026
How we score →What's new in Mirai
Checked 8 days agoAcross the latest 3 updates: 3 community discussions.
The Computer Should Know the Way
Mirai Labs published a research post, "The Computer Should Know the Way," on October 19, 2026.
Tackling random foreign characters
Mirai Labs published a post on addressing random foreign character output, dated September 22, 2026.
Speculative decoding in uzu
Mirai Labs detailed speculative decoding in uzu, its open-source inference project, in a September 3, 2026 post.
What do users say about Mirai?
What people actually say about Mirai — is it worth it?
We scanned public community sources for Mirai on Nov 1, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 0 of the posts we fetched could be positively tied to Mirai. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
How we verified this
Every listed fact about Mirai — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 8 Nov 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified Mirai 13 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.
- — changed description, who should skip it, our verdict, summary, features, pricing page
- — changed our verdict, features, description, summary
- — changed pricing page
- — changed SSL certificate
- — changed who it suits, who should skip it, description, summary, our verdict, features
- — changed pricing page, platforms
Showing the 6 most recent of 13 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Mirai cost in 2026?
Mirai: Pricing is on request (custom quotes). Pricing last verified 8 Nov 2026.
Where the pricing makes sense
The company stage and team size where Mirai's pricing actually pencils out — and where peers do it cheaper.
Mirai sells through a contact form rather than published tiers, so there is no list price to compare. The free, MIT-licensed uzu and lalamo libraries mean solo developers and research teams can evaluate the engine at zero licence cost, and the paid commercial conversation is where an SDK or support agreement presumably lands. That puts Mirai in the same commercial shape as other deep-infrastructure vendors: cheap to trial, opaque to budget until you talk to them.
Has Mirai's pricing changed?
No plan price for Mirai has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Mirai?
Pick Mirai if you are iOS and macOS developers whose AI must answer inside a continuous interaction loop, Teams building ambient or always-on assistants that must keep data on device, Engineers with in-house ML or systems capability who want to own quantization and runtime, Researchers working on on-device decoding, KV cache and speculative decoding.
Look elsewhere if you are Teams that need Android, Windows or web coverage today — Android is listed as Soon, Buyers who want a managed endpoint they can call without touching the runtime, Batch or throughput-shaped workloads — the engine targets batch size = 1, Teams without ML or systems depth to work close to conversion and quantization.
Skip it if Skip Mirai if you need Android, Windows or web coverage today, or if you want a managed inference endpoint you can call without owning model conversion and quantization yourself.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Mirai actually fits — and what changes day-one when you adopt it.
You clone uzu from GitHub, run lalamo to convert and quantize a model for Apple Silicon, then use the CLI to chat with it and serve it as a local API before wiring the same path into your Swift app via the drop-in snippets.
Outcome: You get a measurable latency number on your own device before committing to a commercial conversation, and the CLI-as-local-API path lets you prototype the interaction loop without a cloud round-trip.
You read the 'Trees from Marginals' drafting research and the speculative decoding post, then test speculative decoding in uzu against a baseline on the same model to see how much latency the drafting recovers at batch size 1.
Outcome: You get a direct read on whether speculative decoding moves your latency budget, using an MIT-licensed engine you can inspect line by line rather than a vendor benchmark.
You take the cloud model currently behind a feature, run it through lalamo for on-device conversion and quantization, and compare output quality and latency against the existing cloud path using the macOS app and metrics dashboard.
Outcome: You find out whether the co-designed quantization holds interaction quality on your specific model, and whether removing the round-trip actually buys the sub-200ms response you need.
Use cases
- Run a language model locally on an iPhone for a real-time conversational UI.
- Automate Apple device workflows with on-device inference that avoids cloud round-trips.
- Build an assistant that parses, validates and calls tools without leaving the device.
- Quantize and convert a custom transformer model for offline inference in a Mac app.
- Replace a cloud AI feature with local execution to reduce data exposure and latency.
- Serve a model as a local API from the CLI for testing and prototyping.
Setup time & first value
How long it actually takes to get something useful out of Mirai — broken out by persona, not the marketing-page minute.
Individual developer: the docs advertise drop-in snippets for Python, Swift, TypeScript and Rust with the claim that one developer can get it running in minutes — realistically an afternoon to first local generation via the CLI. ML engineer with a custom model: conversion and quantization through lalamo is the long pole, plan days rather than hours depending on architecture. Team buying
Switching to or from Mirai
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a cloud LLM API: move the feature to on-device execution with uzu, using the CLI's local API mode to keep the same request shape while you test.
- →From a general local runtime: port your model through lalamo for Apple-Silicon-specific conversion and quantization, then compare decode latency against your existing setup.
- →From a hand-rolled Core ML pipeline: replace the custom op graph with uzu's hardware-aware kernels and pick up speculative decoding and sparse KV cache buffers as engine features.
- ↗To a general local runtime: convert your quantized weights back to a portable format, since uzu and lalamo are MIT-licensed and the conversion tooling is yours to use.
- ↗To a managed cloud API: revert the on-device path to a hosted endpoint if you need Android, Windows or web coverage and can accept the network round-trip.
- ↗To another Apple-Silicon stack: models you converted are the main lock-in, so keep the original checkpoints and conversion recipes rather than only the optimized artifacts.
Integrations
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Mirai”, and we withheld 6: 6 could not be judged, because “Mirai” 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 Mirai.
Official links
What are the best Mirai alternatives?
The closest alternatives to Mirai we list are Vmlx, Picollm and Mlx Serve. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Vmlx | Free | Free | — |
| Picollm | Freemium | Pricing not published | — |
| Mlx Serve | Free | Free | — |
Alternatives to Mirai
View all
Tools that pair well with Mirai
Common stack mates teams adopt alongside Mirai, with the specific reason each pairing earns its keep.
Vmlx
Free, MIT-licensed local LLM inference for Apple Silicon Macs, with SSD-backed prefix caching and native OpenAI and Anthropic APIs.
Picollm
picoLLM runs quantized LLMs fully on-device — local inference for phones, embedded Linux, and microcontrollers with no cloud calls.
Mlx Serve
Free, open-source local AI server that runs LLMs, image, music, video and 3D generation entirely on your Apple Silicon Mac.
Mirai vs its closest rival
Mirai's closest head-to-head is with Spider Cloud.
Our Mirai vs Spider Cloud verdict written Jul 3, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
Frequently asked questions
For makers of Mirai
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