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-

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

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

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

I have the exact model and ran extensive benchmark on it. Bad news: Qwen 27B is a no go. Token Generation (TG) 12 tk/s is best speed. With more context you will hit another wall with prefilled prompt (PP) speed and waiting for over a minute for each response due to long Time to First Token (TTFT). T

Specs are real, I’m not arguing 819 GB/s. But I went through this exact decision a few months back and ended up on a DGX Spark for my solo agent stack instead of an M3U. Three things killed the Mac for me:1. CUDA ecosystem lockout. No Unsloth QLoRA, no TensorRT-LLM, no vLLM, no FlashAttention-2, no

Personally I am interested in the DGX Spark. This table doesn't assess it correctly on Bandwidth (GB/s) as NVIDIA advertises "one petaFLOP of FP4 AI performance" and multi-device plug & play; but for pure local inference & retrieval it seems to be the superior product.