You don't need the internet to run research - document 05 ? local ai inference performance. We made sure of it.

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You don't need the internet to run research - document 05 ? local ai inference performance. We made sure of it.

research - Document 05 ? Local AI Inference Performance

The Problem

This document presents a comprehensive benchmark analysis of local AI inference performance for the Inte11ect platform, with a focus on CPU-based deployment scenarios. We evaluate inference latency, throughput, memory utilization, and energy consumption across four hardware configurations: consumer desktop , workstation , laptop (Apple M3 Pro), and edge device (Raspberry Pi 5).

What We Built

The Qwen2-VL-2B model under the Inte11ect optimization pipeline achieves 42.3 tokens per second on desktop CPU with INT4 quantization, representing a 3.8? improvement over the unoptimized baseline. We identify memory bandwidth as the primary bottleneck, with L2 cache hit rate and SIMD vectorization width as the strongest predictors of inference throughput.

The Research

This document presents a comprehensive benchmark analysis of local AI inference performance for the Inte11ect platform, with a focus on CPU-based deployment scenarios.

We evaluate inference latency, throughput, memory utilization, and energy consumption across four hardware configurations: consumer desktop , workstation , laptop (Apple M3 Pro), and edge device (Raspberry Pi 5).

The Qwen2-VL-2B model under the Inte11ect optimization pipeline achieves 42.3 tokens per second on desktop CPU with INT4 quantization, representing a 3.8? improvement over the unoptimized baseline.

We identify memory bandwidth as the primary bottleneck, with L2 cache hit rate and SIMD vectorization width as the strongest predictors of inference throughput.

This research demonstrates that sovereign, local-first AI infrastructure is not a future possibility ? it is a present reality.

Full citation: Alpasan, L.-K. (2026). research - Document 05 ? Local AI Inference Performance. The Anticloud Research Corpus.

Read the full paper

Why The Anticloud

Every AI company today will try to sell you inference as a service. They will tell you that you need their GPU clusters, their data centers, their cooling infrastructure, and their team of DevOps engineers to run modern AI. They are either lying to you or they have not seen what we built.

The Anticloud runs on any GPU or CPU with equal competence. There is no silicon vendor lock-in. There is no hardware partnership requirement. There is no planned obsolescence built into the stack. If you have a computer, you have enough hardware to run it.

The entire system ships as a single binary. There is no orchestration layer to configure. There is no Kubernetes cluster to maintain. There are no containers to deploy. There is no DevOps team required to keep it running. One file. One execution. That is the entire infrastructure.

There is no bloat anywhere in the stack. No Electron wrapper adding hundreds of megabytes of overhead. No node_modules directory with ten thousand dependencies you do not need. No container layers abstracting away from the hardware. Everything in the binary is there because it serves a purpose.

The system requires no internet connection to function. It does not need to phone home for model updates. It does not need to call out to third-party APIs for inference. It does not need to establish a connection to a control server just to boot. It was designed from the ground up to run in environments where the network does not exist.

This is AI infrastructure that fits on a laptop, runs on consumer hardware, and delivers competitive performance without asking for permission or requiring a subscription.

The Anticloud requires one machine, one binary, and zero trust in anyone.

About the Author

My name is Lois-Kleinner Alpasan. I'm 23 years old. I built The Anticloud.

I started this because I looked at the AI industry and saw something wrong. Every major AI system requires you to send your data to someone else's server. Every "AI company" is actually a data company — they make money from your usage, your prompts, your files, your attention. They call it a service. I call it extraction.

I spent the last two years building an alternative. Not a feature, not a product, not a startup looking for an exit — an entirely different infrastructure stack. One where AI runs on your machine, for you, and never needs to phone home. One where privacy is not a feature you toggle in settings but a property of the architecture. One where you don't have to trust anyone because you can verify everything.

The project is near production-ready. Every component is open. Every claim is backed by published research. The code is documented. The ledger is verifiable. The binary fits on a laptop.

I'm not asking for trust. I'm asking you to read the paper, verify the claims, and decide for yourself whether the cloud is really necessary — or whether it was always just the default because no one bothered to build an alternative.

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