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.

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.

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/...

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.

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.

Also they don’t list the M5 air.

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.

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.

- 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.

What could possibly go wrong?

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).

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.

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.

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."

https://www.dataseam.org/research/

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.

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.

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

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

    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.

on 15.1 it failed to serve models.

updated to latest 15.5 and it fails to run binary.

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

How do they do this then?

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.

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

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

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.

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.