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Lowest price ever!
eCoupon at checkout: GMKVX2
SPECS:
https://www.gmktec.com/products/a...header_1.1
eCoupon at checkout: GMKVX2
SPECS:
- AMD Ryzen AI Max+ 395 3.0GHz Processor
- 128GB LPDDR5X-8000 Onboard RAM
- 2TB PCIe 4.0 M.2 2280 SSD; Dual M.2 2280 slots
- AMD Radeon 8060S Graphics
- Microsoft Windows 11 Pro
- SD Memory Card Reader
- 2.5GbE LAN
- Wi-Fi 7: RZ717(MT7925) + BT 5.4
- Ports:
- Front Panel I/O:
- 2× USB-A 3.2 Gen2
- 1× Audio Combo (3.5mm jack)
- 1× USB Type-C (USB4.0)
- 1× SD Card Reader (SD4.0, supports SDXC)
- Buttons: Power, System Fan Lighting Control, Performance Mode Switch
- Rear Panel I/O:
- 1× DisplayPort 1.4 HBR3 (8.1 Gbps)
- 1× HDMI 2.1 (FRL at 8 Gbps)
- 1× USB-A 3.2 Gen2
- 2× USB-A 2.0
- 1× USB Type-C (USB4.0)
- 1× Audio (3.5mm CTIA)
- 1× DC IN (5525 interface)
- 1× RJ45 Gigabit LAN (2.5G, 8125BG)
https://www.gmktec.com/products/a...header_1.1
Community Notes
About the Poster
Deal Details
Community Notes
About the Poster
Lowest price ever!
eCoupon at checkout: GMKVX2
SPECS:
https://www.gmktec.com/products/a...header_1.1
eCoupon at checkout: GMKVX2
SPECS:
- AMD Ryzen AI Max+ 395 3.0GHz Processor
- 128GB LPDDR5X-8000 Onboard RAM
- 2TB PCIe 4.0 M.2 2280 SSD; Dual M.2 2280 slots
- AMD Radeon 8060S Graphics
- Microsoft Windows 11 Pro
- SD Memory Card Reader
- 2.5GbE LAN
- Wi-Fi 7: RZ717(MT7925) + BT 5.4
- Ports:
- Front Panel I/O:
- 2× USB-A 3.2 Gen2
- 1× Audio Combo (3.5mm jack)
- 1× USB Type-C (USB4.0)
- 1× SD Card Reader (SD4.0, supports SDXC)
- Buttons: Power, System Fan Lighting Control, Performance Mode Switch
- Rear Panel I/O:
- 1× DisplayPort 1.4 HBR3 (8.1 Gbps)
- 1× HDMI 2.1 (FRL at 8 Gbps)
- 1× USB-A 3.2 Gen2
- 2× USB-A 2.0
- 1× USB Type-C (USB4.0)
- 1× Audio (3.5mm CTIA)
- 1× DC IN (5525 interface)
- 1× RJ45 Gigabit LAN (2.5G, 8125BG)
https://www.gmktec.com/products/a...header_1.1
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38 Comments
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This was never $1699 pre-order. The price was $ 1,799 for the 64GB or 96GB RAM variant. Search it up in the SD search bar and see for yourself:
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The high RAM is for running AI locally. You can download LLMs (Large Language Models) and have your own "ChatGPT" and run it on your computer. The way LLM's work is accessing data thousands of times per second.
Memory bandwidth speed for SSD's = 3-7 GB/s
Memory bandwidth speed for RAM or VRAM = 500-1,500 GB/s
So thats why they need to be stored in RAM (regular RAM or VRAM from a GPU which is even better)
Sorry if this is TMI or you don't care, just thought i'd answer your question in case you didn't know!
The high RAM is for running AI locally. You can download LLMs (Large Language Models) and have your own "ChatGPT" and run it on your computer. The way LLM's work is accessing data thousands of times per second.
Memory bandwidth speed for SSD's = 3-7 GB/s
Memory bandwidth speed for RAM or VRAM = 500-1,500 GB/s
So thats why they need to be stored in RAM (regular RAM or VRAM from a GPU which is even better)
Sorry if this is TMI or you don't care, just thought i'd answer your question in case you didn't know!
It would probably be considered a mini desktop, yes, as it doesn't have a display. But it's size is a big advantage for mounting it out of the way, even to a back of a monitor, putting it in a rack, just being portable in general.
The high RAM is for running AI locally. You can download LLMs (Large Language Models) and have your own "ChatGPT" and run it on your computer. The way LLM's work is accessing data thousands of times per second.
Memory bandwidth speed for SSD's = 3-7 GB/s
Memory bandwidth speed for RAM or VRAM = 500-1,500 GB/s
So thats why they need to be stored in RAM (regular RAM or VRAM from a GPU which is even better)
Sorry if this is TMI or you don't care, just thought i'd answer your question in case you didn't know!
No, that's a great answer thank you. So would this be good for also running something like stable diffusion or would that be more GPU side than ram since it's images/video? As for chat gpt type, how would you train it on your desktop? Or do you not even need to?
Small caveat worth noting: while it is normal system RAM in the case of this computer, you would allocate some/most of it to the iGPU so technically it would be considered VRAM
For LLMs you have model size and context length
For SD, you have resolution
As far as your other question, I'm sure someone around SD will see this and give a better answer, as I am a bit of a noob.
As I understand it, LLM's & SD and similar models are already "trained." That's essentially what you're downloading when you go here and download a model:
https://ollama.com/library
But there's a caveat to this, LLM's can also leverage "RAG" which is kind of a 2nd layer of "training" that might be like your personal docs you feed it. Think of that as like a personalization layer, but it can be leveraged to make the model "Smarter" or more "specific"
Small caveat worth noting: while it is normal system RAM in the case of this computer, you would allocate some/most of it to the iGPU so technically it would be considered VRAM
For LLMs you have model size and context length
For SD, you have resolution
As far as your other question, I'm sure someone around SD will see this and give a better answer, as I am a bit of a noob.
As I understand it, LLM's & SD and similar models are already "trained." That's essentially what you're downloading when you go here and download a model:
https://ollama.com/library
But there's a caveat to this, LLM's can also leverage "RAG" which is kind of a 2nd layer of "training" that might be like your personal docs you feed it. Think of that as like a personalization layer, but it can be leveraged to make the model "Smarter" or more "specific"
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