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I did try some finance analysis earlier this year. I was using a DGX Spark, so I could run some relatively large models, but the results were pretty mixed at th

I did try some finance analysis earlier this year. I was using a DGX Spark, so I could run some relatively large models, but the results were pretty mixed at the time. I honestly can't remember which models I used anymore. Might be worth trying again now though.

jacquesm 1mo ago
I'm having a really hard time doing on twin DGX spark what I could do on my quad 3090 rig (which is a scaled down version of what I was using before, the power requirements and the noise were really an issue but I loved the speed and the amount of VRAM). The results tend to be inconsistent, there is lots of looping, far more tokens generated for the same job and lower quality output. I suspect there is some kind of regression in the B12X kernels or something to that effect because none of that should happen, the exact same model on both machines gives wildly different results. Probably this will sort itself out over time. If I may ask, what model / software combo were you using?
- Alephinitesimal 1mo ago
  I was only using a single DGX Spark, and this was earlier in the year, so I was running some pretty aggressively quantized models — probably in the 1–3 bit range. My main issue at the time was that my financial data had lots of messy notes, comments, and irregular annotations. The quantized models often failed to process all of that context consistently and would miss things. So I ended up generating a fake dataset with the same structure, asking Claude Code to work out the analysis on that, and then bringing the result back to the local model for the final pass. I was mainly using llama.cpp at the time, before B12X support was integrated into vLLM, so I think I wasn't using it then.
  - jacquesm 1mo ago
    Thank you, interesting info! I think the Sparks are an interesting platform, the power consumption / memory bandwidth / memory amount trade-off is completely different from the regular cards and it will take a while for the software to really take advantage of them.

jacquesm 1mo ago

I'm having a really hard time doing on twin DGX spark what I could do on my quad 3090 rig (which is a scaled down version of what I was using before, the power requirements and the noise were really an issue but I loved the speed and the amount of VRAM). The results tend to be inconsistent, there is lots of looping, far more tokens generated for the same job and lower quality output. I suspect there is some kind of regression in the B12X kernels or something to that effect because none of that should happen, the exact same model on both machines gives wildly different results. Probably this will sort itself out over time. If I may ask, what model / software combo were you using?

Alephinitesimal 1mo ago
I was only using a single DGX Spark, and this was earlier in the year, so I was running some pretty aggressively quantized models — probably in the 1–3 bit range. My main issue at the time was that my financial data had lots of messy notes, comments, and irregular annotations. The quantized models often failed to process all of that context consistently and would miss things. So I ended up generating a fake dataset with the same structure, asking Claude Code to work out the analysis on that, and then bringing the result back to the local model for the final pass. I was mainly using llama.cpp at the time, before B12X support was integrated into vLLM, so I think I wasn't using it then.
- jacquesm 1mo ago
  Thank you, interesting info! I think the Sparks are an interesting platform, the power consumption / memory bandwidth / memory amount trade-off is completely different from the regular cards and it will take a while for the software to really take advantage of them.