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Get a DGX Spark clone instead, if you want to do local LLM or train or whatever. It's worth the extra $.
No, you have a wrong idea. Go and read what that is for. That is especially targeted for AI developers to develop and optimize Int4 Quantize models, which Nvidia is promoting over Into or Int 16. That thing only works on CUDA. As an end user product, this one is far superior.
No, you have a wrong idea. Go and read what that is for. That is especially targeted for AI developers to develop and optimize Int4 Quantize models, which Nvidia is promoting over Into or Int 16. That thing only works on CUDA. As an end user product, this one is far superior.
Yes, a lot of YouTubers have said so. I'm sure they also stayed in a Holiday Inn Express.
Yes on YouTube, where the accredited tech bloggers exist, and reddit it where the really nerdy tech enthusiasts exists. typically when a bunch of intelligent people reach the same consensus, they are right.
NVIDIA DGX Spark
Best for: Training, fine-tuning, and inference with models specifically optimized for its unique NVFP4 format.
Strengths: 128GB of unified memory, compact form factor, and designed for a complete AI sandbox experience.
Weaknesses: Slower inference speeds compared to high-end GPUs for standard models, with benchmarks showing it being slower than a 5090 and significantly slower than a Pro 6000. The memory bandwidth is a bottleneck for large batch sizes
Last edited by ReturnToSender December 1, 2025 at 10:28 PM.
Yes on YouTube, where the accredited tech bloggers exist, and reddit it where the really nerdy tech enthusiasts exists. typically when a bunch of intelligent people reach the same consensus, they are right.
LOL! Accredited tech bloggers? Typically, reddit is an echo chamber for idiots.
LOL! Accredited tech bloggers? Typically, reddit is an echo chamber for idiots.
I can't believe you're here arguing with the Internet, lol
yeah fam the whole Internet is wrong and you're right. again that box is not for inference. It's for development two different things.
NVIDIA DGX Spark
Best for: Training, fine-tuning, and inference with models specifically optimized for its unique NVFP4 format.
Strengths: 128GB of unified memory, compact form factor, and designed for a complete AI sandbox experience.
Weaknesses: Slower inference speeds compared to high-end GPUs for standard models, with benchmarks showing it being slower than a 5090 and significantly slower than a Pro 6000. The memory bandwidth is a bottleneck for large batch sizes
I can't believe you're here arguing with the Internet, lol
yeah fam the whole Internet is wrong and you're right. again that box is not for inference. It's for development two different things.
NVIDIA DGX Spark
Best for: Training, fine-tuning, and inference with models specifically optimized for its unique NVFP4 format.
Strengths: 128GB of unified memory, compact form factor, and designed for a complete AI sandbox experience.
Weaknesses: Slower inference speeds compared to high-end GPUs for standard models, with benchmarks showing it being slower than a 5090 and significantly slower than a Pro 6000. The memory bandwidth is a bottleneck for large batch sizes
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And then get a 512gb ssd (+$20) and this is down to less than 1800 for the 395max
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NVIDIA DGX Spark
Best for: Training, fine-tuning, and inference with models specifically optimized for its unique NVFP4 format.
Strengths: 128GB of unified memory, compact form factor, and designed for a complete AI sandbox experience.
Weaknesses: Slower inference speeds compared to high-end GPUs for standard models, with benchmarks showing it being slower than a 5090 and significantly slower than a Pro 6000. The memory bandwidth is a bottleneck for large batch sizes
yeah fam the whole Internet is wrong and you're right. again that box is not for inference. It's for development two different things.
NVIDIA DGX Spark
Best for: Training, fine-tuning, and inference with models specifically optimized for its unique NVFP4 format.
Strengths: 128GB of unified memory, compact form factor, and designed for a complete AI sandbox experience.
Weaknesses: Slower inference speeds compared to high-end GPUs for standard models, with benchmarks showing it being slower than a 5090 and significantly slower than a Pro 6000. The memory bandwidth is a bottleneck for large batch sizes
yeah fam the whole Internet is wrong and you're right. again that box is not for inference. It's for development two different things.
NVIDIA DGX Spark
Best for: Training, fine-tuning, and inference with models specifically optimized for its unique NVFP4 format.
Strengths: 128GB of unified memory, compact form factor, and designed for a complete AI sandbox experience.
Weaknesses: Slower inference speeds compared to high-end GPUs for standard models, with benchmarks showing it being slower than a 5090 and significantly slower than a Pro 6000. The memory bandwidth is a bottleneck for large batch sizes
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Join The Conversation
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Share information with the community. Please follow our Community Guidelines and be kind!