AetherOS: bare-metal Rust LLM runtime on Intel Gen9 — looking for adversarial review

[danielforface]

I have been working on AetherOS, a Rust #![no_std] x86_64 bare-metal OS/runtime for local LLM inference on a physical laptop.

The reviewed configuration boots from USB, loads a Llama 3.2 1B Instruct Q4_K_M GGUF model, and uses a hybrid CPU / Intel integrated-GPU path on PCI 8086:9b41.

I am not looking for a general “is this cool?” review.

I have published a bounded evidence release, and I would specifically like people to attack three claims:

1. ROUND18BY — causal register-corruption repair

A reconstruction helper used as a scalar move emitted an SIMD8 write footprint. The claimed mechanism is that the final write beginning at r109.3 crossed into r110.0, which was still a live ROTATE4 accumulator.

The public evidence includes the source findings, source diff, round report and physical log.

Unsafe reconstruction:
- 2048/2048 threads reproduced the predicted overwrite
- 0/2048 accumulator sentinels survived

Repaired reconstruction:
- 2048/2048 sentinels survived
- 0/2048 threads reproduced the overwrite

The repaired full kernel reached 8192/8192 exact outputs.

A later fence timeout is intentionally kept separate as a lifecycle failure rather than being hidden behind the arithmetic repair.

2. ROUND18DV — 7.768x local result that was NOT promoted

A controlled Q4 multicolumn weight-reuse experiment reached 7.768x at B=8 while remaining exact.

However, the complete FFN graph did not improve enough, so the candidate was not promoted to production.

I am specifically interested in whether the public evidence keeps the distinction between a local kernel benchmark and end-to-end inference performance clear enough.

3. ROUND18HAR — steady qualification at 7.0775 decode tok/s

A predecessor qualification had a measurement problem: the first candidate timing could include one-time construction/install/flush/readback work.

HAR changed the measurement protocol rather than lowering the acceptance threshold:

- explicit warm-up pair
- two measured pairs
- balanced/reversed ordering
- unchanged correctness/performance gates
- no fastest-sample cherry-picking

The fixed 128-token physical matrix was:

5.23, 7.08, 7.08, 7.08, 7.08, 7.08, 7.08, 7.07, 7.07 tok/s

The public steady summary is 7.0775 decode tok/s for this tested hardware/model configuration.

Important boundaries:

- this is internally produced evidence, not independent certification
- the inference path is hybrid CPU/iGPU, not pure GPU
- I am not claiming 7 tok/s on arbitrary hardware/models
- RUN01 reports vko_zero_copy=false
- the complete private kernel source is not currently public
- bounded source/log slices needed to inspect these three claims are public

I also want to disclose this upfront: I use AI coding agents heavily in the development process.

I am not presenting project size, generated code, or AI output as evidence. What I am asking reviewers to evaluate is whether I can defend the architecture, failure mechanisms, experimental design, physical evidence, benchmark methodology, and promotion/rejection decisions.

Mechanism-level criticism is exactly what I am looking for.

Central review thread:
https://github.com/danielforface/Aether ... e/issues/3
Repository:
https://github.com/danielforface/AetherOS-Showcase

[Octocontrabass]

What is your justification for requesting this level of academic rigor? Are you planning on publishing a research paper with your results? Are you aiming for commercialization and trying to avoid signing a contract you can't uphold? Or is this just a hobby project where nobody actually cares if your claims are perfectly accurate?

The fact that you present your data as if you were looking for a LLM to review it instead of a human does not help your case.

[danielforface]

What is your justification for requesting this level of academic rigor? Are you planning on publishing a research paper with your results? Are you aiming for commercialization and trying to avoid signing a contract you can't uphold? Or is this just a hobby project where nobody actually cares if your claims are perfectly accurate?

The fact that you present your data as if you were looking for a LLM to review it instead of a human does not help your case.

Fair point. I'm not writing a paper or selling a product. It's a research/hobby project, but I also use it as an engineering portfolio, so I want the claims to be defensible and not exaggerated — especially because AI is part of the workflow.

What I'm really looking for is a sanity check from experienced low-level developers: does the evidence actually support what I think it does?

I also agree the post was too audit-like. I'll simplify the way I present it.

[Octocontrabass]

does the evidence actually support what I think it does?

Why do you think the evidence supports your conclusions?

[danielforface]

does the evidence actually support what I think it does?

Why do you think the evidence supports your conclusions?

Mostly because the BY case gave me a falsifiable prediction before I changed the code.

The problematic write was effectively:

Code: Select all

start: r109.3
SIMD8 footprint -> r109.3 ... r110.2
                         ^
                         r110.0 was still live

So if that was really the bug, I expected r110.0 to be corrupted.

In the unsafe test it was overwritten in all 2048 cases. After narrowing that write, all 2048 sentinels survived, and the repaired full kernel reached 8192/8192 exact outputs.

That's why I think that specific causal conclusion is supported. It doesn't prove the whole system is correct.

HAR is a different kind of claim: it only says the corrected measurement protocol repeatedly produced ~7.08 tok/s on that machine/model. And with DV I deliberately did not infer an end-to-end speedup because the full graph didn't support it.