NVIDIA DGX Spark In-Depth Review: A New Standard for Local AI Inference
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Re: NVIDIA DGX Spark In-Depth Review: A New Standard for Local AI Inference
#2Re: NVIDIA DGX Spark In-Depth Review: A New Standard for Local AI Inference
#3Article doesn't seem to mention price which is $4,000 which makes it comparable to a 5090 but with 128GB of unified LPDDR5x vs the 5090's 32GB DDR7.
Re: NVIDIA DGX Spark In-Depth Review: A New Standard for Local AI Inference
#4Article doesn't seem to mention price which is $4,000 which makes it comparable to a 5090 but with 128GB of unified LPDDR5x vs the 5090's 32GB DDR7.
Re: NVIDIA DGX Spark In-Depth Review: A New Standard for Local AI Inference
#5Re: NVIDIA DGX Spark In-Depth Review: A New Standard for Local AI Inference
#6Article doesn't seem to mention price which is $4,000 which makes it comparable to a 5090 but with 128GB of unified LPDDR5x vs the 5090's 32GB DDR7.
Well, that’s disappointing since the Mac Studio 128GB is $3,499. If Apple happens to launch a Mac Mini with 128GB RAM it would eat Nvidia Sparks’ lunch every day.
Re: NVIDIA DGX Spark In-Depth Review: A New Standard for Local AI Inference
#7I wonder why they didn't test against the broadly available Strix Halo with 128GB of 256 GB/s memory bandwidth, 16 core full-fat Zen5 with AVX512 at $2k... it is a mystery...
Re: NVIDIA DGX Spark In-Depth Review: A New Standard for Local AI Inference
#8Earlier quoted context omitted.
Well, that’s disappointing since the Mac Studio 128GB is $3,499. If Apple happens to launch a Mac Mini with 128GB RAM it would eat Nvidia Sparks’ lunch every day.
Agreed. I also wonder why they chose to test against a Mac Studio with only 64GB instead of 128GB.
Re: NVIDIA DGX Spark In-Depth Review: A New Standard for Local AI Inference
#9I wonder why they didn't test against the broadly available Strix Halo with 128GB of 256 GB/s memory bandwidth, 16 core full-fat Zen5 with AVX512 at $2k... it is a mystery...
Re: NVIDIA DGX Spark In-Depth Review: A New Standard for Local AI Inference
#10Earlier quoted context omitted.
Agreed. I also wonder why they chose to test against a Mac Studio with only 64GB instead of 128GB.
Hi, author here. I crowd-sourced the devices for benchmarking from my friends. It just happened that one of my friend has this device.
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GB10, compute capability 12.1, VMM: yes
| model | size | params | backend | ngl | n_ubatch | fa | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | --------------: | -------------------: |
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | pp4096 | 3564.31 ± 9.91 |
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | tg32 | 53.93 ± 1.71 |
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | pp4096 | 1792.32 ± 34.74 |
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | tg32 | 38.54 ± 3.10 |