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Asus Ascent GX10

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131–140 of 203 posts

Re: Asus Ascent GX10

#131

Earlier quoted context omitted.

Performance wise it was able to spit out about half of a buggy version of Space Invaders as a single HTML file in roughly a minute.

I’m pretty sure I could spit out something that doesn’t work in half a minute.

Don't undersell it. The game is playable in a browser. The graphics are just blocks, the aliens don't return fire. There are no bunkers. The aliens change colors when they descend to a new level (whoops). But for less than 60 seconds of effort it does include the aliens (who do properly go all the way to the edges, so the strategy of shooting the sides off of the formation still works--not every implementation gets that part right), and it does detect when you have won the game. The tank and the bullets work, and it even maintains the limit on the number of bullets you can have in the air at once. However, the bullets are not destroyed by the aliens so a single shot can wipe out half of a column. It also doesn't have the formation speed up as you destroy the aliens.

So it is severely underbaked but the base gameplay is there. Roughly what you would expect out of a LLM given only the high level objective. I would expect an hour or so of vibe coding would probably result in something reasonably complete before you started bumping up into the context window. I'm honestly kind of impressed that it worked at all given the minuscule amount of human input that went into that prompt.

Re: Asus Ascent GX10

#132

My hope was to find a system which does ASR, then LLM processing with MCP use and finally TTS: "Put X on my todo list" / "Mark X as done" -> LLM thinks, reads the todo list, edits the todo list, and tells me "I added X to your todo list", ... "Turn all the lights off" -> llm thinks and uses MCP to turn off the lights -> "Lights have been turned off". "Send me an email at 8pm reminding me to do" .... "Email has been s…

Energy efficient LLM inference (for now) is as realistic as existence of perpetual motion.

Re: Asus Ascent GX10

#135

My hope was to find a system which does ASR, then LLM processing with MCP use and finally TTS: "Put X on my todo list" / "Mark X as done" -> LLM thinks, reads the todo list, edits the todo list, and tells me "I added X to your todo list", ... "Turn all the lights off" -> llm thinks and uses MCP to turn off the lights -> "Lights have been turned off". "Send me an email at 8pm reminding me to do" .... "Email has been s…

Energy efficient LLM inference (for now) is as realistic as existence of perpetual motion.

I wouldn't mind if it burns 200 watts while it does the task, as long as it idles at below 30W

Re: Asus Ascent GX10

#136

Earlier quoted context omitted.

Energy efficient LLM inference (for now) is as realistic as existence of perpetual motion.

I wouldn't mind if it burns 200 watts while it does the task, as long as it idles at below 30W

NVIDIA H200 idles at 75 watts. I'm not keeping my hopes high on that, either.

Re: Asus Ascent GX10

#137

My hope was to find a system which does ASR, then LLM processing with MCP use and finally TTS: "Put X on my todo list" / "Mark X as done" -> LLM thinks, reads the todo list, edits the todo list, and tells me "I added X to your todo list", ... "Turn all the lights off" -> llm thinks and uses MCP to turn off the lights -> "Lights have been turned off". "Send me an email at 8pm reminding me to do" .... "Email has been s…

You can already do that on most desktop GPU's (even going as far as prev gen Nv 1050/1060/1070 for example).

You'll need a model able to work with tools, like llama 3.2 (https://huggingface.co/meta-llama), serve it, hook up MCPs, include a STT interface, and you're cooking.

Re: Asus Ascent GX10

#138

My hope was to find a system which does ASR, then LLM processing with MCP use and finally TTS: "Put X on my todo list" / "Mark X as done" -> LLM thinks, reads the todo list, edits the todo list, and tells me "I added X to your todo list", ... "Turn all the lights off" -> llm thinks and uses MCP to turn off the lights -> "Lights have been turned off". "Send me an email at 8pm reminding me to do" .... "Email has been s…

You can already do that on most desktop GPU's (even going as far as prev gen Nv 1050/1060/1070 for example). You'll need a model able to work with tools, like llama 3.2 ( https://huggingface.co/meta-llama ), serve it, hook up MCPs, include a STT interface, and you're cooking.

Even a bottom of the barrel N95 has audio acceleration features helping with speech to text, but the LLM inference part still will be far from being efficient.

Plus, you need to keep the card at "ready" state, you can't idle/standby it completely.

Re: Asus Ascent GX10

#139

ServeTheHome has already benchmarked the DGX Spark architecture against the (very obvious) Ryzen AI Max 395+ with 128G RAM: https://www.servethehome.com/nvidia-dgx-spark-review-the-gb1... If (and in case of Nvidia that's a big if at the moment) they get their software straight on Linux for once this piece of hardware seems to be something to keep an eye on.

GMKtec, maker of the EVO-X2 mini-PC that uses a Ryzen AI Max 395+, posted a blog post with a comparison between the DGX Spark and their EVO-X2 miniPC.

from https://www.gmktec.com/blog/evo-x2-vs-nvidia-dgx-spark-redef... (text taken from https://wccftech.com/forget-nvidia-dgx-spark-amd-strix-halo-... since the GMKtec table was an image, but wccftech converted to an HTML table - EDIT-reformatted to make table look nicer in monospace font w/o tabs)

  Test Model    Metric                          EVO – X2        NVIDIA GB10     Winner
  Llama 3.3 70B Generation Speed (tok/sec)       4.90            4.67           AMD
                First Token Response Time (s)    0.86            0.53           NVIDIA
  Qwen3 Coder   Generation Speed (tok/sec)      35.13           38.03           NVIDIA
                First Token Response Time (s)    0.13            0.42           AMD
  GPT-OSS 20B   Generation Speed (tok/sec)      64.69           60.33           AMD
                First Token Response Time (s)    0.19            0.44           AMD
  Qwen3 0.6B Model Generation Speed (tok/sec)  163.78          174.29           NVIDIA
                First Token Response Time (s)    0.02            0.03           AMD

Re: Asus Ascent GX10

#140

This is a tangent, but the little pop up example for their ai chat bot to try and entice me to use it was something along the lines of “what are the specs?” How great would it be if instead of shoving these bots to help decipher the marketing speak they just had the specs right up front?

I find all these Popup Assistant Bots as bad User Experience.

No, I don't want to use your assistant and your are forcing me to pointlessly click on the close button. Some times they event hide viable information during their popup.

They seem to be the reincarnation of 2000s popups; there to satisfy a business manager versus actually being a useful tool.

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