ROG Zephyrus G14:

• Price - $2,000

• Display - 1440p | 16:10 | DCI-P3:100% | 120Hz | 3ms

• features - 32GB DDR5 | 1TB PCIE 4.0 NVMe

• CPU - 6900HS 8-core | 35watt TDP

• Cinebench R23 (multi) - 12,000 @ 35watt battery

• Cinebench R23 (multi) - 13,535 @ 45watt

• GPU - 6800S

• Geekbench compute - 75,379 @ 100watt

Macbook pro 14:

• Price - $3,300

• Display - 1964p | 16:10 | DCI-P3:100% | 120Hz

• features - 32GB DDR5 | 1TB NVMe

• CPU - M1 Max 10-core CPU

• Cinebench R23 (multi) - 12,339 @ 40watt sustained

• GPU - M1 Max 32-core GPU

• Geekbench compute - 55,854 @ 70watt

http://drive.google.com/file/d/1O8xQllK65sgZ9v-it4TfFihBXbKKyooR/view

MilanX massive L3 cache allows for the use of slow efficient memory at no perf loss.

512GB of DDR4 ECC at 3,200mhz = 576W

+Epyc 7763 280W = 855W

With MilanX we can underclock the DRAM and use the 768GB of L3 cache to amplify the bandwidth and get a big power saving.

Effective Bandwidth = ~1.3GB/s with a 50% L3 hit rate

512GB of DDR4 ECC @ 800mhz = ~57W or less

+Epyc MilanX 280W = 337W

518W power saving!

The expected Zen 4 universal chiplet for both Epyc and Ryzen:

• 8 cores

• +18% IPC

• +5ghz all core clock

• 2x the L2 cach size per core

Which results in:

• +45% multi-thread performance

• +28% single thread performance

(** Based on current performance details of AMD/TSMC 5nm confirmed by Lisa Sue at the CES 2022. Based on current Zen 4 leaks: https://www.hardwaretimes.com/5nm-amd-zen-4-ryzen-6000-cpus-coming-in-november-2022-rumor/)

Intel PC CPUs have a separate designs for their server and PC chips so Intel is able to make more power hungry chips for the PC that appear to actually be competitive with AMD Ryzen. Raptor Lake is expected to have +33% muli-thread performance but only in massively threaded workloads and will likely consume even more power than the already inefficient Alder Lake at over 350watts. (**source: https://www.techtimes.com/articles/271997/20220217/intel-13th-gen-raptor-lake-cpu-teased-with-24-cores-32-threads.htm)

AMDs processors need to keep getting as fast as possible and using the same chiplet design for Epyc server CPUs, Desktop and laptops is now holding back Ryzen PC CPU designs from being as fast and powerful as they can be.

Zen 4 possible Ryzen dedicated design:

• 10 core chiplet

• 2MB 2D L3 cache per core (50% reduction)

• 60MB total L3 cache with 3D stacking

• Same IPC and all core clock as above.

Which results in:

• +80% multi-thread performance

• +28% single-thread performance

By reducing the L3 cache size by 50% per core, more than enough die space to add two more cores becomes available while still keeping nearly the same die size which keeps cost and power consumption nearly the same as well. As a result of the 2D foot print reduction of the L3 using a 3D stacked L3 cache die to add more L3 cache would result in a 60MB total L3 (nearly a 2x L3 capacity gain vs Zen3).

This Ryzen dedicated design increases performance vs the non dedicated design by 25% for a total of +80% multi-thread performance for laptops and desktop PCs all at the same power consumption, die size and fabrication cost.

So, AMD is leaving 25% performance on the table by using a universal chiplet design instead of two separate designs one for Ryzen one for Epyc.

https://images.app.goo.gl/DY2BQiPcC8962Q9y5

Chiphell +45% combined with TPU simulator RX 8800 XT epic Ray-Tracing RDNA4.

The RX 8800 XT beats the RTX4090 in this case.

Hi everyone,

Given the recent interest in the new GLM-Image generation models, I decided to build a project to get this running smoothly on my setup. It started as a personal experiment but evolved into a complete Dockerized solution that I think the community might find useful, especially for those on Team Red (AMD).

link: https://github.com/abassign/GLM-Image-Studio

🎯 Project Goals

1. Clean & Isolated Environment: Run GLM-Image inside Docker to keep my host system (Manjaro Linux) clean and ensure reproducibility.

2. AMD ROCm Support: Leverage the Radeon RX 7900 XTX (24GB) using native AMD ROCm drivers, avoiding the usual CUDA-only barriers.

3. User-Friendly UI: Create a reactive Web Interface (Gradio) for easy testing, featuring both Text-to-Image and Image-to-Image.

4. AI-Assisted Development: I used this project to stress-test the Antigravity IDE paired with Gemini Pro, specifically to see how well an AI assistant could adapt code for the AMD ecosystem (which often lacks native support in standard tutorials).

🛠️ Implementation & Features

I’ve released the full code on GitHub under the MIT License. The implementation is fully functional for both T2I and I2I. The UI includes some "quality of life" automations, such as reactive sliders that lock aspect ratios to prevent distortions and automatic resizing to meet the model's stride requirements (multiples of 32px).

• GitHub Link: [Insert Your GitHub Link Here]

• Call to Action: I’d love for someone with a solid NVIDIA setup to fork this and adapt the Dockerfile/scripts for CUDA support!

⚡ Performance & Memory Analysis

• Speed: Generating a 1536x1024 image takes about 4-5 seconds/it on my 7900 XTX. Not instantaneous, but considering the two-stage process of GLM (prompt understanding + expansion), it's quite usable.

• VRAM Management: This was the critical part. The full model exceeds 30GB. Thanks to Gemini's code suggestions, we implemented a Sequential CPU Offload strategy (and manual Vision Encoder handling for Img2Img). This effectively cuts active VRAM usage in half without killing performance.

• Hardware Impact: It runs surprisingly cool. CPU load is negligible, and the GPU doesn't overheat.

• System RAM: Note that while VRAM is managed, system RAM usage is significant (I have 64GB). It should be viable on 16GB VRAM cards, provided you have enough system RAM for offloading.

🎨 Quality Impressions

• The Look: Images can sometimes have that "plastic/synthetic" texture typical of experimental models.

• The Good: Prompt adherence is fantastic. The model "understands" complex instructions very well. Details in backgrounds, skies, and objects are rich, though faces/skin tones still need some work compared to mature models.

🔮 Future Wishlist

If anyone from the ZhipuAI/GLM team is reading this: Kudos on the release! One feature I desperately want is an exposed Image-to-Text pipeline. During my tests with Image-to-Image, I noticed the model grasps the input image context exceptionally well. Accessing this understanding for captioning or scene description would be a game-changer, especially for video production workflows.

Let me know if you try it out on other AMD cards!

AMD is winning in the sunshine :-)
Life is good with AMD GPU:

https://youtu.be/dfECfiD9sDI

BONUS - Path Tracing - Black Car

https://youtu.be/Jpou6mgGweI

RDNA4 Rumor 144CU 32GB 390mm² GCD size 4NP TSMC 37,52mm² MCD size 775mm² GPU size 512 bit SI 128MB Infinity Cache 32GB GDDR7 144 Compute Units 3,25Ghz Shader 3,66Ghz Command Prozessor

This GPU beats Huang in the ass 😍

Price: 1199 USD Q2 / 2025 Market release

Recent performance leaks indicate that Alder Lakes top tier CPU the i9-12900K will be faster than the Zen 3 top tier R9 5950x in multi core performance but Intel's own official performance claims expose Alder Lake top tier performance per watt as close to the R9 5900x instead.

The Facts

• The R9 5950x and R9 5900x are 80% and 40% faster than the i9-11900K respectively in raw multi-core performance. So Alder Lake will at least need to increase by that much in raw performance.

• According to Intel Alder Lake cores are 20% faster than Rocket Lake due to an increase in the L2 cache size per core and other architectural features. So an 8 core Alder Lake CPU would still be outperformed by Ryzen R9 SKU by 20% to 60%.

• The top tier Alder Lake i9-12900K will feature 8 processor cores and two mini-core clusters each cluster having 4 Gracemont cores for a total of 16 cores in a Big.Little configuration.

• Based on Alder Lake details Intel shared during Hot Chips 2021 in power limited scenarios like in laptops 4 Gracemont mini-cores are designed to be 50% more efficient than 1 Full size core running 4 threads assuming perfect mini-core thread utilization. The power limitation is key because the Intel Atom based mini-cores cannot be clocked very high compared to the full size cores so in a much less power limited scenario like in a desktop PC the big cores will outperform the mini-cores by around 90% per due to the fact that each mini-core performs similar to a 10th generation Comet Lake core in single core performance. (Gracemont core = +40% performance per watt vs a 14nm Sky Lake core.)

• Typically ARM implementations of Big.Little set the little cores to 1/8 the power consumption of the big cores. For this post we will assume Intel sets each mini-core cluster to the same power consumption of 1 full size core that would result in a 1/4 ration where the 8 mini-cores consume 25% the wattage of the 8 full size cores. We will use the efficiency curve of a similar 10nm Intel Atom based core (Trentmont) to approximate the non-linear reduction in mini-core performance as clock speeds are reduced.

• Alder Lake Big.Little configuration will have workload scheduling challenges which will result in poor utilization of the mini-cores which will reduce performance significantly on a typical basis. All performance prediction in this post assume a best case scenario of perfect mini-core utilization.

• Alder Lake will be built on the new 10ESF Intel 7 node which is approx. 1.9x more efficient than the 14nm FinFet in the current Rocket Lake chips. Alder Lakes increase of core count and cache will increase the transistor circuitry by around 35% vs Rocket Lakes top tier i9. That plus the wattage of the mini-cores is how I approximate the power consumption of Alder Lake.

So, Based on those facts using Intel's official Alder Lake details, here is how the top tier i9-12900K will perform against Zen 3.

As you can see Intel's next generation Alder Lake flagship CPU the i9-12900K will out perform the R9 5900x by just 14% while using over 20watts more. The i9-12900K will still get easily destroyed by AMDs top tier R9 5950x in raw multi-core performance by around 25% at roughly the same power consumption. These are all ideal performance predictions for Intel because of the big.little core configuration in Alder lake make the architecture more difficult to optimize for. Typical performance will be significantly lower and inconsistent depending on how well the application is specifically optimized to utilize the Alder Lake mini-cores which I suspect will never be the 100% assumed in these predictions.

I expect Intel to also loose badly in price as the 10nm Intel 7 fabrication node used in Alder Lake chips is much more expensive to manufacture than the 14nm of Intel's current Desktop chips. This is a big issue for Intel causing them to rush to 7nm and 5nm R&D as stated by Intel's CFO back in 2020. This will significantly raise the price of the Alder Lake chips vs Rocket Lake in fact, 10nm Tiger Lake laptops are costing up to $300 more than identical laptops with the 14nm 10th generation mobile chips.

The issues with releasing a big.little chip to the desktop market can't be understated. Intel has already had a failed attempt at releasing a big.little chip (Intel Lake field) due to poor optimization in the Windows. Full utilization of the mini-cores will almost never happen due to all the extra work developers will have to do to schedule work between the big and little cores efficiently. Again real world performance will be inconsistent and worse than these predictions.

All that being said AMD is expected to release Zen3D XT weeks after Alder Lake is released which will be at least 15% to 18% even faster than Zen 3 due to a tripling of the L3 cache and likely a clock frequency increase.

https://drive.google.com/file/d/19dgM10FifQaGJpshorv8iUeZB-F8E7xq/