DGX Spark

It might not have lived up to the hype but it gets the job done. People also underestimate how much noise, power and heat a system with duel full height graphic cards put out. The Mac Studio and DGX Sparks give you very capable systems in a small convenient package. AMD Strix Halo is also an option

Benchmarks speak louder than words: Dgx Spark: https://spark-arena.com/leaderboard Framework: https://kyuz0.github.io/amd-strix-halo-toolboxes/ (llamacpp) and https://kyuz0.github.io/amd-strix-halo-vllm-toolboxes/ (vllm) Apple Chips: https://omlx.ai/benchmarks The advantage of VLLM, which is NVIDIA-

Firstly you should understand that the Ryzen AI Max+ 395 chip is not intended to be an all-purpose AI chip. It comes with a lot of memory, but does not support CUDA, does not have a lot of compute, and most importantly does not have a lot of memory bandwidth. Not supporting CUDA means the options fo

Exactly. Steam on the Snapdragon 8 Elite gen 5 will be 30-75% faster than the Steam Frame. Duct tape the fucker to your head. Easy. I'm more excited for Steam on the next Nvidia-ARM device (like the DGX Spark). If they launched a high-end one that could run steam, they would clean up. Steam should p

It competes with things like Nvidia DGX Spark, which has 273 GBs (and a lot less cores). It's not a bad product, the worst thing is that AMD's ecosystem is not as large as Nvidia's.

I ran some benchmarks on Ollama with llama3.2:1b and it was ~ 3.5x faster on Nvidia than on AMD. I'm usually using my dgx spark for the LLM so I didn't benchmark a lot of models. For me, being able to run cuda-enabled things, faster local LLM, and gsync to my external monitor made it worthwhile. If

I've already got an M3 Ultra. I'm going to try this out to get M5 Ultra pp speeds: https://blog.exolabs.net/nvidia-dgx-spark/ Going to start with just one for Minimax 2.7 prefill, but if it works really well I'd consider a second. I somehow expect buying 2 GB10 systems is still going to be cheaper t

Unfortunately, that's about as far as my knowledge goes. I've only been using it for a few weeks, and I was late to the "AI party" in general. I was stubbornly resistant up until about a year ago, then mainly used GitHub Copilot for coding. I've started experimenting with RAG and automations, but I

they are linux only,dgx spark use the dgxOS based on ubuntu and preconfigured by nvidia,the problem is windows support. If nvidia wants to release the n1/n1x they can but it will be a linux only lineup,they are variants of the dgx spark after all. The vram is the ram on these products,call it igpu+c

If, for one reason or another, you only have these two options, then the Nvidia DGX Spark is undoubtedly the best choice to run LTX2.3 thanks to CUDA. However, as many say, it's not the best option: for the same price, you can build a complete PC around an RTX 5090 and get significantly better perfo