DGX Spark

I’ve got a Strix Halo and 2x GB10 (Nvidia DGX Spark, but the Asus version). For pure AI workloads, I’d go with the GB10. For example, on GPT-OSS-120B I’m hitting ~6000 pp and 50–60 t/s with vLLM, and I can easily serve 3 or 4 parallel requests while still outperforming my Strix Halo, which struggles

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-

Unfortunately, you dropped $4k for an inference server with anemic bandwidth (273 GB/s). To put that in perspective, that's 24 GB/s less than Apple's M5 Pro chip. If you run a dense model like gemma4:31b with that bandwidth, you're gonna have a bad time. You need an MoE model with a quant optimized

I'd love to show the entire desk, speakers, etc. but I'm afraid everything surrounding is a bit of a mess 'PC' is an Nvidia DGX Spark. Needed something with plenty of memory, strong CUDA support, & something small I can take with me for travel. It is a hair better than a 9950x in CPU performance & a

Honestly it's fairly niche. It's meant for local LLM development/testing when working with large datasets (that's why it comes with 200Gb/s networking). But it's actually pretty damn bad at actually running LLMs (as in, you can load a large model but it's really slow), you want a beefier workstation

If you want long context processing, Mac is not a good choice. Actually, the worst among other choices, because of extremely slow reading speed. Note that this is different from the TG speed you mentioned. But for your use case, it is far more important, as others have mentioned. So Prefill speed (P

DGX is not for anything other than AI, and big models at that. It's a 5070ti speed wise. The 5070ti has 896GB/s memory bandwidth while the DGX Spark and the Minisforum S1 Max have 256GB/s memory bandwidth so it's literally 3.5x slower. You don't buy it for that. You buy it to run 200b parameter mode

Training using the full Nvidia professional stack absolutely justifies it for a certain demographic. You're not it. You aren't going to train anything on a 5070ti with 256GB/s memory bandwidth. Also training is done with Fp16 so 128GB RAM limits you to about 60B parameters. People doing training use

Actualmente lo unico que vale la pena son mac minis, el dgx spark de nvidia tiene una mierda de ancho de banda cualquier modelo te corre a 20 tokes por segundo

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