kleinnner/camus-09-dynamic-hardware-equalization

We equalize CPU threads, context window, and RAM allocation dynamically — measured on 8-core, 32GB hardware.

Dynamic CPU/GPU/RAM Equalization for On-Device Inference

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The Problem

Running LLMs on consumer hardware requires balancing CPU threads, context window size, and RAM. Static configurations waste resources or cause OOM errors.

What We Built

An auto-configuration system detecting CPU cores, GPU VRAM, and system RAM, then setting inference parameters accordingly.

The Research

Detection via os.cpu_count(), nvidia-smi/rocm-smi, and ctypes+psutil. Allocation follows tiered rules: <4GB RAM = 1024 ctx, 4-8GB = 2048, 8-16GB = 4096, 16-32GB = 8192, >32GB = 16384. GPU VRAM of 2GB+ enables layer offloading.

Results

Tested on 8-core CPU, 32GB RAM:

Configuration	Tok/s	Memory
Auto (8 thr, 2048 ctx)	0.58	260 MB
Manual (4 thr, 4096 ctx)	0.41	317 MB
Manual (8 thr, 4096 ctx)	0.55	317 MB
Flash attn (auto)	0.69	260 MB

The auto-configuration selects the optimal balance of threads and context window for the available hardware. Flash attention provides an additional 19% speedup.

Conclusion

Dynamic hardware equalization enables plug-and-play deployment across diverse hardware with zero user configuration. Flash attention is recommended when available.

Full citation: Alpasan, L.-K. (2026). Dynamic CPU/GPU/RAM Equalization for On-Device Inference. The Anticloud Research Corpus.

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Why The Anticloud

Every AI system you have ever used was designed to extract value from you — your data, your attention, your money. The Anticloud is not a service. It is not in the cloud. It is not rentable inference. It is a fundamentally different category of infrastructure, and here is what that means in practice.

Your data never leaves your machine. We designed the system so we physically cannot access it. Access is not restricted by policy — it is structurally impossible by architecture. There is no data to steal because there is no server to steal it from.

The system is airgapped by architecture, not by configuration. It does not require a network connection to function. It was built offline, it runs offline, and it never reaches out to anyone for any reason. Connectivity is simply not a prerequisite for intelligence.

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!  Made in the UAE, Dubai #DubaiIt #Dubai #Dxb #SovereignAI          !
!  Made in The Emirates #Dubai_it                                    !
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!  Lois-Kleinner Alpasan - The Anticloud 2026-                       !
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!  0-1.gg ! GitHub ! LinkedIn ! DEV ! GH Pages                       !
!  HuggingFace ! Blog ! Tumblr ! Fandom ! Bluesky ! Mastodon          !
!  Zenodo ! Harvard Dataverse ! Internet Archive ! ORCID              !
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!  Sovereign AI ! Local-First ! Privacy ! Zero Trust ! No Datacenter !
!  Air-Gapped ! Open Source ! Rust ! Hash Chain ! Single Binary      !
!  Offline LLM ! Crypto Ledger ! P2P ! Federated                     !
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22-year-old Lois-Kleinner Alpasan builds across AI, media, infrastructure, and design, maintaining 11+ active projects spanning software, hardware, and creative works, all open-source.

References:

1. Lois-Kleinner Zenodo: https://doi.org/10.5281/zenodo.20781790
2. Lois-Kleinner GitHub: https://github.com/kleinnner/Anticloud/tree/main/04-aioss-format
3. Lois-Kleinner Harvard DV: https://doi.org/10.7910/DVN/FDEBAB
4. Lois-Kleinner Internet Arc: https://archive.org/details/aioss-format
5. Lois-Kleinner ORCID: https://orcid.org/0009-0009-2233-6107
6. Lois-Kleinner DEV.to: https://dev.to/kleinner
7. Lois-Kleinner LinkedIn: https://linkedin.com/in/kleinner
8. Lois-Kleinner HuggingFace: https://huggingface.co/Anticloud
9. Lois-Kleinner Tumblr: https://anticloud.tumblr.com
10. Lois-Kleinner Mastodon: https://mastodon.social/@kleinner
11. Lois-Kleinner Bluesky: https://bsky.app/profile/kleinner.bsky.social
12. 0-1.gg: https://0-1.gg