Darkbloom – Private inference on idle Macs

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.

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?

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.

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.

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.

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

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.

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.

So basically ... Pied Piper.

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.

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.

"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."

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

What could possibly go wrong?

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

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?

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

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

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

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?

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

I cant buy credits - says page could not load

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

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.

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