Question Intent-Based Optimization Node ---- boosting performance without overclocking ?

[DRMIOmega]

Hey everyone,
I’ve been working on an optimization node I designed myself. Unlike traditional overclocking or BIOS tuning, this runs entirely in software — no voltage changes, no risk.


What makes it different?


🧠 It doesn't just optimize the CPU and GPU — it dynamically tunes memory in real time based on system intent.

For example, I’m running DDR4-2133, but during high-load scenarios, it behaves more like 2666+ due to intelligent memory access and prioritization.

⚡ No overclocking. No BIOS mods. Just smarter decisions during runtime.

I'm now looking for reliable stock benchmarks (CPU/GPU/memory) from similar hardware setups to compare my system against. If you’ve got 5700G or RTX 4070 systems (or even stock DDR4 data), I’d love to hear your scores or suggestions on where to test for apples-to-apples comparisons.

Appreciate the insight — let’s see how far optimization can take us without brute force.

 

[COLGeek]

Go here: https://www.cpubenchmark.net/

 

[DRMIOmega]

Thank you.

Ran PassMark + 3DMark with Brave (40 tabs), LibreOffice x2, Steam, HWInfo, Geekbench, Notepad++ all running.
PassMark CPU: 23,957 | 3D: 24,261
All stock, no thermal issues. My Optimization Node actively tunes CPU, GPU, and RAM on the fly — even DDR4-2133 performs like 2666+ when it matters.

Asus B450, BIOS 2/24/23 Ver 4002, 48 Gigs DDR 4 SDRAM Duel Channel, AMD Ryzen 7 5700G, RTX 4070.

I’m outperforming tweaked rigs with this kind of background load, I think I’m onto something. Trying to start a company around my tech any advice appreciated.

 

[COLGeek]

Advertising/self-promotion are clearly off limits on the site. Just making sure you are aware.

Your title suggests something that is network based (per the term Intent-Based Optimization Node). What you are describing is something else.

Describe your tool a bit more, for clarity's sake. Including a description of the number of platforms this has been applied to and whether they showed similar results.

 

[DRMIOmega]

Thank you, I'm not advertising, just new to all this. The nodes, are networked back to the Server AI, it optimizes from the top down, nodes from bottom up. Run a mesh network and nD encryption. The Nodes also have intent based security. The issue is finding the right framing. The optimization was the original and main function. All I have is this old PC and it runs the AI server, the Nodes, all the background programs, and still kills it in optimization. My RTX 4070 runs like a 4070 TI and depending on the use case 4080.

But thank you for your time I really appreciate it.

 

[COLGeek]

So you have a sample size of one, to date. Correct?

 

[DRMIOmega]

Yes, I just run a series of benchmarks. Cyberpunk 2077, Blender, and now Passmark.

Metric	No Node (Clean)	With Node (Clean)	With Node (Heavy Load)	% Change (Heavy Load vs. Baseline)	% Change (Heavy Load vs. Clean + Node)
CPU	24,328.0	24,523.0	24,297.8	-0.12%	-0.92%
3D	24,324.6	25,217.1	24,766.7	+1.82%	-1.79%
2D	1,018.6	1,035.6	1,021.2	+0.26%	-1.39%
Disk	1,037.3	1,039.0	993.4	-4.23%	-4.39%
Memory	2,512.4	2,498.1	2,466.9	-1.81%	-1.25%

Lilith AI + Node (My Tech)33%+ (e.g., 3D +1.82% vs. baseline, Cyberpunk 55.92 FPS vs. ~40) Intent-based firewall (green/yellow/red ethics coding) ZKCE (oblivious chains, origin-only decrypt) Limited by hardware scale (consumer-grade RTX 4070); needs enterprise testing.

 

[COLGeek]

I am not seeing huge gains, certainly within the margin of error between runs. I assume you have established a stable, consistent testing baseline?

Ask a friend or borrow something completely different and test again before assuming anything. You need more than a dataset of one.

 

[Ralston18]

This:

"For example, I’m running DDR4-2133, but during high-load scenarios, it behaves more like 2666+ due to intelligent memory access and prioritization".

Sounds more like potential "marketing speak" than reality to me.

I would really want to and would need to see quantitative proofs that the optimization mode works as described in a manner directly noticeable by the end user.

Just my thoughts on the matter.

 

[USAFRet]

even DDR4-2133 performs like 2666+ when it matters.

Personally, I'd need to see some tests with actual applications.
Before and after.

Marketingspeak and benchmarks are one thing....but does it really benefit the user behind the keyboard?

 

[DRMIOmega]

Thank you, I am going to try it on my Linux laptop. As to the benchmarks this is all new. I started making an AI that turned into an intent based system. It is optimizing the RAM, CPU, GPU, and OS on the fly. Not selling anything yet, just learning the right questions to ask.

What it does is optimize based on the intent of the user. If I am gaming, then it turns down any process that is not directly involved in that game. The more that is going on the better it does.

It is under 10 megs and has intent based security, a mesh network, encrypted work area, it is in a virtual desktop, and intent based optimization of the system not just one thing. So yes, hard to figure out a space it fits.

 

[DRMIOmega]

Hi all,


Thanks again for your feedback. I appreciate the push to stay objective — that’s exactly what I’m trying to do. My original intent wasn’t about performance gains, but about testing a system I built as part of a broader AI project. I ended up with something unique: a node that optimizes based on intent. Explaining what it's doing can be hard, because it behaves in ways I didn’t fully anticipate, and I’m still learning how to properly benchmark it.


The project has grown into something beyond typical system tuning — it actively detects and corrects inefficient behavior in software, especially games, even when the underlying code is poorly written. And it does so from the system level without requiring any game modifications.


Here’s a summary of how it works, and why I’m looking for better benchmarking strategies to evaluate it fairly:


1. Dynamic VRAM Coalescing

• Addresses memory fragmentation from inefficient game assets
• Reclaims VRAM in long play sessions with memory leaks
• Prevents over-allocation slowdowns

2. Precision-Aware Task Weighting

• Identifies unnecessary use of high-precision math (e.g., FP64 for simple effects)
• Reallocates GPU compute more efficiently
• Improves frame pacing and thermal stability

3. Intent-Driven Thermal Governor

• Detects wasteful GPU load in non-interactive scenes (menus, cutscenes)
• Reduces thermal buildup and power draw
• Maintains consistent performance without throttling

4. Node-Aware Task Spillover

• Rebalances CPU load when games overuse a single thread
• Improves core utilization
• Enhances performance in CPU-bound games

Here are some simple examples from testing:

• A game that loads 8GB of VRAM for low-res textures: the node reclaims 2GB and improves streaming.
• A title that maxes GPU in a pause menu: thermal governor cuts temps by 30%.
• A physics engine using one CPU thread: task spillover improves responsiveness.
• A game using FP64 lighting: precision weighting reclaims GPU bandwidth.

It also includes features like draw call batching, memory access pattern detection, and performance scaling — but everything is system-level and doesn’t interfere with the game directly. It works without modifying or injecting anything.


At this point, I’m not selling anything — just trying to figure out the best way to test what I’ve built and understand whether it’s viable as a long-term project. The insights from this community help a lot. If you’ve got suggestions on how to isolate and measure system-wide effects like this more clearly, I’d welcome the advice.


Thanks again,
Mike

 

[COLGeek]

How are you instructing the OS to make any changes?

How are you addressing the security aspects of such controls?

 

[DRMIOmega]

My system doesn’t override the OS in the traditional sense or inject anything into user-space processes. The Node operates as a low-level runtime assistant — it observes intent through system behavior (usage patterns, priority changes, memory pressure, instruction throughput) and responds by suggesting re-weighted priorities and resource usage patterns, using existing APIs and kernel-level hooks, not exploits or modifications.

It’s more like an orchestration layer that speaks the OS’s language — not forcing control, but nudging. It operates in a sandboxed service context and uses telemetry, thermal curves, instruction stall analysis, and memory behavior to model what's happening in the system. Then it reacts by coordinating:

• Thread priority weighting via QoS-aware hints
• GPU and memory timing influence using runtime scheduler directives
• Thermal behavior adaptation using sensor feedback and fan profile management
• Application-level behavior profiling, but only via metadata (process names, usage stats — not inspecting or injecting into app code)

---

Security:
Security is a major focus. I use a Zero-Knowledge Contextual Encryption (ZKCE) model I developed, which means:

• All decisions are made without revealing internal system/user data to external services.
• Contextual decisions are logged locally for audit, but not transmitted unless the user opts in.
• It never modifies or reads game binaries or config files — the behavior is purely at the system layer (OS scheduler, VRAM, I/O timing, CPU affinity, etc).
• There’s intent tagging — apps that try to execute outside their typical profile (e.g., a media player requesting GPU compute) get flagged and blocked.

No kernel patches. No DLL injection. Just intelligent system guidance through the same interfaces available to trusted system services.

I'm essentially creating the next generation of system optimization tools since my intent based AI works so well - like how Task Manager evolved into Resource Monitor, I am building the next step: intent-aware system orchestration.


I’m still experimenting with how far this model can go without stepping outside safe, OS-compliant behavior — and feedback like yours is exactly what helps shape the testing scope.

 

[DRMIOmega]

A comment and a question. After testing Heaven 4 is looking my RTX 4070 to 4095 MB of VRAM. Any ideas about how to fix this?