I have a hard time believing their numbers. If you can pay off a mac mini in 2-4 months, and make $1-2k profit every month after that, why wouldn’t their business model just be buying mac minis?

I installed this so you don't have to. It did feel a bit quirky and not super polished. Fails to download the image model. The audio/tts model fails to load.

In 15 minutes of serving Gemma, I got precisely zero actual inference requests, and a bunch of health checks and two attestations.

At the moment they don't have enough sustained demand to justify the earning estimates.

You have to install their MDM device management software on your computer. Basically that computer is theirs now. So don't plan on just handing over your laptop temporarily unless you don't mind some company completely owning your box. Still might be a validate use for people with slightly old laptops lying around, but beware trying to share this computer with your daily activities if you e.g. use a bank on a browser on this computer regularly. MDM means they can swap out your SSL certs level of computer access, please correct me if I'm wrong.

Unfortunately, verifiable privacy is not physically possible on MacBooks of today. Don't let a nice presentation fool you.

Apple Silicon has a Secure Enclave, but not a public SGX/TDX/SEV-style enclave for arbitrary code, so these claims are about OS hardening, not verifiable confidential execution.

It would be nice if it were possible. There's a lot of cool innovations possible beyond privacy.

They use the TEE to check that the model and code is untampered with. That's a good, valid approach and should work (I've done similar things on AWS with their TEE)

The key question here is how they avoid the outside computer being able to view the memory of the internal process:

> An in-process inference design that embeds the in- ference engine directly in a hardened process, elimi- nating all inter-process communication channels that could be observed, with optional hypervisor mem- ory isolation that extends protection from software- enforced to hardware-enforced via ARM Stage 2 page tables at zero performance cost.[1]

I was under the impression this wasn't possible if you are using the GPU. I could be misled on this though.

[1] https://github.com/Layr-Labs/d-inference/blob/master/papers/...

Cool idea. Just some back-of-the-envelope math here (not trusting what's on their site):

My M5 Pro can generate 130 tok/s (4 streams) on Gemma 4 26B. Darkbloom's pricing is $0.20 per Mtok output.

That's about $2.24/day or $67/mo revenue if it's fully utilized 24/7.

Now assuming 50W sustained load, that's about 36 kWh/mo, at ~$.25/kWh approx. $9/mo in costs.

Could be good for lunch money every once in a while! Around $700/yr.

The hardware-attested privacy path is the interesting part of this, but the economic side has a quieter risk the thread has not named: the load tax per request. MiniMax M2.5 239B from your catalog still has to load all 239B weights even though only 11B are active — that is roughly 120GB at Q4_K_M, and cold load from SSD on Apple Silicon is measurable in tens of seconds. Even the Qwen3.5 122B MoE lands around 65GB cold. If the coordinator routes request number two to a different idle Mac than request number one, or if the owner's machine spun the model out to free memory in between, each request pays that cold load before the first token. Keeping the model resident 24/7 solves the latency but eats into the power budget the operator is trying to amortize in the first place. How does the coordinator decide which provider to keep warm for which model? A 16GB or 32GB home Mac cannot host Qwen3.5 122B MoE at all, and the Mac Studios that can are a much smaller slice of the 100M machine estimate.

@eigengajesh - Your cost estimator lists Mac Mini M4 Pro with only 24 or 48GB options, but the M4 Pro mini can also be configured with 64GB. At least, I hope so, as I'm typing this on one. ;-)

Oh, also, you seem to have some bugs:

Gemma: WARN [vllm_mlx] RuntimeError: Failed to load the default metallib. This library is using language version 4.0 which is not supported on this OS. library not found library not found library not found

cohere: 2026-04-16T14:25:10.541562Z WARN [stt] File "/Users/dga/.darkbloom/bin/stt_server.py", line 332, in load_model 2026-04-16T14:25:10.541614Z WARN [stt] from mlx_audio.stt.models.cohere_asr import audio as audio_mod 2026-04-16T14:25:10.541643Z WARN [stt] ModuleNotFoundError: No module named 'mlx_audio.stt.models.cohere_asr'

Trying to download the flux image models fails with:

curl: (56) The requested URL returned error: 404

darkbloom earnings does not work

your documentation is inconstent between saying 100% of revenue to providers vs 95%

I think .. this needs a little more care and feeding before you open it up widely. :) And maybe lay off the LLM generated text before it gets you in trouble for promising things you're not delivering.

Interesting concept. Two sided marketplaces are hard to bootstrap but maybe just enough curiosity would get the flywheel going. Hell they should just try and convince people to enroll as providers but then also use the service even if it’s hitting their own machines until there’s some degree of supply and demand pressure then try and get only providers to sign up. Or set up some way to encourage providers to promote others to use the service (the 100% rev share kind of breaks that concept but anything can change).

I wish this was self hostable, even for a license fee. Many businesses have fleets of Macs, sometimes even in stock as returned equipment from employees. Would allow for a distributed internal inference network, which has appeal for many orgs who value or require privacy.

Cool idea, though hats off to anyone who got cohere-transcribe to show up as serving the model. I could get device to show up, but kept having issues getting their server to properly serve the model though it could just be the device I tested.

This is one of those ideas I think makes perfect sense, but requires so much operational change for the entire stack, that it would be very difficult to scale:

- Convincing labs to run distributed, burst-y inference

- Convincing people to run their Mac all day, hoping to make a little profit

- Convincing users to trust a distributed network of un-trusted devices

I had a similar idea, pre-AI, just for compute in general. But solving even 1 of those 3 (swap AI lab for managed-compute-type-company, eg Supabase, Vercel) is nearly impossible.

[fix: remove hardcoded API_KEYS ](https://github.com/Layr-Labs/d-inference/pull/39/changes)

Having strong SETI@Home vibes from 25 years ago, except of course, this is not for the greater good of humanity, but a for-profit project.

Problem is, from a technical point of view, what kind of made sense back then (most people running desktops, fans always on, energy saving minimal) is kind of stupid today (even if your laptop has no fan, would you want it to be always generating heat?)...

I definitely want my laptops to be cool, quiet and idle most of the time.

As one of the only people running a Mac Studio M3 Ultra with 512 GB of RAM on the network, I can tell you at sustained 100% GPU utilization I am measuring 250 watts max (at the power outlet). My solar panels are easily producing this. The power calculation goes away once you connect a solar panel. You can get a 400 watt solar panel on Amazon for $300.

I'd love a way to do this locally -- pool all the PCs in our own office for in-office pools of compute. Any suggestions from anyone? We currently run ollama but manually manage the pools

You might not even know it as a user but the payment/distribution here is all built on crypto+stablecoins. This is a great use case for it.

So basically ... Pied Piper.

bittensor has something to say about this

How can one do this safely? If I create a new, non-sudo user, can I install the MDM profile only for that user? I don't understand how this all works obviously so maybe this is a very dumb question

Interesting to see an offering with this heritage [1] proposing flat earnings rates for inference operators here, rather than trying to sell a dynamic marketplace where operators compete on price in real-time.

Right now the dashboards show 78 providers online, but someone in-thread here said that they spun one up and got no requests. Surely someone would be willing to beat the posted rate and swallow up the demand?

I expect this is a migration target, but a tactical omission from V1 comms both for legitimate legibility reasons (I can sell x for y is easier to parse than 'I can participate in a marketplace') and slightly illegitimate legibility reasons (obscuring likely future price collapse).

Still - neat project that I hope does well.

[1] Layer Labs, formerly EigenLayer, is company built around a protocol to abstract and recycle economic security guarantees from Ethereum proof of stake.

I won't install some random untrusted binary off of some website. I downloaded it and did some cursory analysis instead.

Got the latest v0.3.8 version from the list here: https://api.darkbloom.dev/v1/releases/latest

Three binaries and a Python file: darkbloom (Rust)

eigeninference-enclave (Swift)

ffmpeg (from Homebrew, lol)

stt_server.py (a simple FastAPI speech-to-text server using mlx_audio).

The good parts: All three binaries are signed with a valid Apple Developer ID and have Hardened runtime enabled.

Bad parts: Binaries aren't notarized. Enrolls the device for remote MDM using micromdm. Downloads and installs a complete Python runtime from Cloudflare R2 (Supply chain risk). PT_DENY_ATTACH to make debugging harder. Collects device serial numbers.

TL;DR: No, not touching that.

Wasn't there an idea about 15 years ago where you would open your browser, go to a webpage and that page would have a JavaScript based client that would run distributed workloads?

I believe the idea was that people could submit big workloads, the server would slice them up and then have the clients download and run a small slice. You as the computer owner would then get some payout.

Intersting to see this coming back again.

client side of this kind of needs to be open source unless I'm running it on a dedicated machine and firewalling it from the rest of my network. Or the company needs to have a very strong reputation and certifications. curlbash and go is a pretty hard sell for me

I like the idea but it wont take off until Homomorphic Encryption for inference becomes a thing that's efficient and anyone can be a node.

I'm not sure how the economics works out. Pricing for AI inference is based on supply/demand/scarcity. If your hardware is scarce, that means low supply; combine with high demand, it's now valuable. But what happens if you enable every spare Mac on the planet to join the game? Now your supply is high, which means now it's less valuable. So if this becomes really popular, you don't make much money. But if it doesn't become somewhat popular, you don't get any requests, and don't make money. The only way they could ensure a good return would be to first make it popular, then artificially lower the number of hosts.

I think it’s important that systems like this exist, but getting them off the ground is non-trivial.

We’ve been building something similar for image/video models for the past few months, and it’s made me think distribution might be the real bottleneck.

It’s proving difficult to get enough early usage to reach the point where the system becomes more interesting on its own.

Curious how others have approached that bootstrap problem. Thanks in advance.

Love the concept, with some similarity to folding@home, though more personal gain.

But trying it out it still needs work, I couldn't download a model successfully (and their list of nodes at https://console.darkbloom.dev/providers suggests this is typical).

And as a cursory user, it took me some digging to find out that to cash out you need a Solana address (providers > earnings).

the MDM profile requirement is suspect though I get why they are doing it. but it doesn't inspire confidence to see that their profile is unsigned and still using the default micromdn scep challenge...

Generate images requested by randoms on the internet on your hardware.

What could possibly go wrong?

"These are estimates only. We do not guarantee any specific utilization or earnings. Actual earnings depend on network demand, model popularity, your provider reputation score, and how many other providers are serving the same model.

When your Mac is idle (no inference requests), it consumes minimal power — you don't lose significant money waiting for requests. The electricity costs shown only apply during active inference.

Text models typically see the highest and most consistent demand. Image generation and transcription requests are bursty — high volume during peaks, quiet otherwise."

> Operators cannot observe inference data.

Is there some actual cryptography behind this, or just fundamentally-breakable DRM and vibes?

Like the concept. This is not a business - should be an open source GitHub repo maybe.

They lost me with just one microcopy - “start earning”. Huge red signal.

I've tried to install it on my mac, but not sure what macOS version it should support.

on 15.1 it failed to serve models.

updated to latest 15.5 and it fails to run binary.

I installed two models, but it just always reports:

    Available models (2):
    CohereLabs/cohere-transcribe-03-2026 (4.6 GB)
    flux_2_klein_9b_q8p.ckpt (20.2 GB)
    ...
    Advertising 0 model(s) (only loaded models)

Interesting idea, but needs some work.

Until we have breakthroughs in homomorphic encryption compute, I won't trust such privacy claims

Seems like an interesting way for those people that purchased a Mac Mini to run OpenClaw to pay off the hardware, since mostly it’s now idle.

This feels like defi... de-ai

They are almost claiming FHE, isn't it just a matter of creating the right tool to get the generated tokens from RAM before it gets encrypted for transfer. How is it fundamentally different than chutes?

They could consider registering as a provider on something like OpenRouter if they aren't getting enough inference requests on their own site.

Why does M1 Max project significantly higher revenue than M3 Max with double the ram?

Is this named after the 2011 split album with Grimes and d'Eon?

> Every request is end-to-end encrypted

Afaik you will need to decrypt the data the moment it needs to be fed into the model.

How do they do this then?

Actually more useful than Bitcoin. Brilliant idea.

How does the inference work correctly if the payloads are encrypted?

hey guys! i'm the creator. let me know if you have any questions.

Apple should build this, and start giving away free Macs subsidized by idle usage.

I could imagine this working for the openclaw community if the price is right

Why isn’t a MacBook Air M5 on the hardware list?

That solution actually makes great sense. So Apple won in some strange way again?

Guess there are limitations on size of the models, but if top-tier models will getting democratized I don’t see a reason not to use this API. The only thing that comes to me is data privacy concerns.

I think batch-evals for non-sensitive data has great PMF here.

I thought this was Apple’s plan all along. How is this not already their thing?

Broken calculator or am I missing something here?

  Macbook Air M2  8GB   12h/day -> $647/month

  Mac Mini M4     32GB  12h/day -> $290/month

Why only Macs? If we think of all PCs and mobile phones running idle, the potential is much larger.

Like Fold@home but for profit!

It's a good project that makes sense. I recommend adding a contractual layer as well, since it's free and makes sense. Operators could legally sign that they will not look into the inference layer. After all, the operators already have a financial relationship with this provider, so it makes sense to add a contract to it and keep operators from looking into other people's data that way, too. I wish this project a lot of success.

I really want this to succeed

I cant buy credits - says page could not load

Thanks, if this takse off. I have finally some motivation to do exploitation in kernel. :)

Too much to read.

Should have called it “Inferanet” with this idea.

Away this looks like a great idea and might have a chance at solving the economic issue with running nodes for cheap inference and getting paid for it.

latest (v0.3.8) tar doesn't contain image-bank or gRPCServerCLI dependencies so installer fails.

> That is not a technology problem. It is a marketplace problem.

I cringe every time I see this sentence structure. I know the joke is about emdashes, but the “Its not …. It’s ….” drives me crazy.