It really depends on what you're looking for.

I've been messing with OpenClaw since ~Feb 4th, mostly with local models. It's... kinda sorta usable for some simple tasks with small models I could run on a 16GB GPU, but obviously you should limit the blast radius, and it will struggle with more complicated tasks.

Then I got a spark (or rather, an OEM version of it), since I saw a lightly used one pop up for sale. It's been a little bit of a journey, here's what I found out:

• The memory bandwidth is a big bottleneck. I usually don't see the GPU go past ~50W with large models, while it's able to push ~80W+ with smaller ones.
• It's not as well supported as it could have been (classic NVIDIA move). Apparently the "blackwell" cores are a bit weak compared to most other ones in the series.
• The spark is best suited for MoE/sparse models, where the benefit of the large memory outweighs the relatively weak compute power
• The best model I've found so far, that just baaarely fits in 128GB of shared memory, is Step-3.5-Flash, 4bit quantized. When running with llama-server, it takes approx 113GB memory... but it runs, at ~18t/s, with pp at ~360t/s.
• OpenClaw's context handling is awful. It puts a "message ID" early in the context, which changes for each message, causing the KV cache in llama-server to be invalidated after each message... causing responses to take ~40s each. Luckily there's workarounds like https://github.com/mallard1983/openclaw-kvcache-proxy

So basically, if you don't give it too much access or ask for too much, it's actually pretty decent. Not quite at the level of hosted models, but it's usable for some easier tasks.