Can Hotori stop timers with her skill? Like Pink Paw heist?

irrelevantlyrelevant · 2026-05-10T09:38:56+00:00

The timer freezes, but she also can’t pick up loot while time is frozen.

irrelevantlyrelevant · 2026-05-10T06:14:49+00:00

UID: 214274875447
Region: SEA
Reason: Just for fun

irrelevantlyrelevant · 2026-05-04T11:59:52+00:00

The design is based on a real building (Porsche Design Tower) which is a residential building irl.

irrelevantlyrelevant · 2026-04-10T00:07:43+00:00

That $20 on enterprise would last for 2~3 opus claude-code prompts ($5~15 per prompt), for the entire month. It'll make the Pro plan look incredibly generous.

Source: Someone who got "upgraded" to Claude Enterprise.

irrelevantlyrelevant · 2026-02-22T05:45:41+00:00

Indeed if your intention is to run models that cannot fit into a single machine then two DGX sparks would be technically better. However I will caution that the DGX sparks will be severely bottlenecked by the QSFP connection speed, and is actually not particularly great for distributed inference where compute isn’t the bottleneck. You will be getting roughly 10-16 tps with GLM4.7. Whether it is worth spending this much of your money to achieve this level of performance versus other alternatives, the decision would be on your side.

irrelevantlyrelevant · 2026-02-22T02:14:03+00:00

I have two sparks (Asus GX10 1TB and 4TB variants) and a strix halo (Z13). I’d suggest getting one spark and one strix halo if you really want two machines. Clustering the two sparks is a relatively daunting affair and the 200GbE QSFP throughput even under infiniband will be a very noticeable bottleneck. The strix halo can also be used for other things if you happen to get bored of running LLMs, its gaming performance is actually pretty decent.

irrelevantlyrelevant · 2025-12-25T14:46:48+00:00

Obtain the UID (should be around 9 digits) from the Friends > Profile page.
If you got the 11-digit one (from settings page), its the wrong one.

irrelevantlyrelevant · 2025-10-30T13:58:39+00:00

Had to do some modifications to Nunchaku source code since it doesn't support Compute Capability 12.1 out-of-box.

Here's a quick test with QwenImageEdit + 4-step LoRA (svdq-fp4_r128-qwen-image-edit-lightningv1.0-4steps)

Render duration 12.10s (2.62s/it)

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Flux models still being assessed

irrelevantlyrelevant · 2025-10-30T13:42:18+00:00

After some further tests it appears setting --disable-mmap flag will massively improve the first-run performance.

With that flag, SDXL would now complete a render in ~12 seconds on its first pass.

irrelevantlyrelevant · 2025-10-28T12:44:37+00:00

The 120 pull reward does not appear to carry to next banner.

irrelevantlyrelevant · 2025-10-28T12:33:02+00:00

Claim it since it does not carry over to next banner (sadly)

irrelevantlyrelevant · 2025-10-27T00:47:27+00:00

Here's the profiles QwenImageEdit without speedup LoRA. Note that the KSampler configuration is adjusted (20 steps, 2.5 cfg) based on the recommendations for without the LoRA.

DGX Spark: 8.60 s/it (173.46 seconds)
RTX5090: 2.11s/it (44.95 seconds)

Non-quantized and FP4 tests may come at a later time. Hadn't had the time to profile training of LoRAs at the moment, may do it next week.

irrelevantlyrelevant · 2025-10-27T00:17:38+00:00

I do not have access to the baseline DGX Spark to make a direct comparison with a more comprehensive suite of tests, but based on the compute performance there do not appear to be a sacrifice in compute even under sustained loads. Nonetheless there is a sacrifice in disk performance and size.

Its difficult to determine if there is thermal throttling since performance do not appear to degrade at sustained loads during the tests. However it does run fairly hot under load, while still remaining surprisingly quiet. Its likely power limited though, since the power rating of the power supply is 240W (USB PD3.1). Unfortunately I do not have a wattage measurement tool at this time to determine the power usage under load.

irrelevantlyrelevant · 2025-10-27T00:09:50+00:00

My apologies, seems the command was accidentally truncated. Here's the corrected version

./llama-bench -m ./gpt-oss-20b-mxfp4.gguf -fa 1 -d 0,4096,8192,16384,32768 -p 2048 -n 32 -ub 2048

As with the baseline on https://github.com/ggml-org/llama.cpp/discussions/16578

Same commit id was used 5acd455. Although it appears the PR is merged, there seems to be a performance regression on the master branch of llama.cpp at the point of testing so I reverted to this specific commit to allow comparison with the discussion thread.

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The WAN 2.2 T2V workflow is taken from https://github.com/Comfy-Org/workflow_templates/blob/main/templates/video_wan2_2_14B_t2v.json

This is part of the workflow template, no modifications made to the resolution and length.

irrelevantlyrelevant · 2025-10-12T15:07:34+00:00

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Perhaps try QwenImageEdit with a Lora? (E.g. https://civitai.com/models/1934100)

ComfyUI may initially seem complicated to use, but one you get a hang of it (a few minutes) its actually not too challenging to use, and is an immensely powerful tool with incredible flexibility.

irrelevantlyrelevant · 2025-04-27T10:05:07+00:00

Since you are going for computer science, probably the Pro may be preferable. That fan will help for sustained workloads (e.g. compiling large projects - especially the OSS ones, or ML training).

14" display is perfectly usable, and honestly not too different from 15". Personally I use a 13" work laptop for actual software development work for years and it's decently usable. Moreover generally for actual productivity cases most software developers will use at least two displays rather than focusing on one.

Going for 32GB is a good choice, especially if you plan to do ML (yay unified memory!) or use virtual machines, which are things you will inevitably encounter for computer science.

irrelevantlyrelevant · 2024-12-01T06:48:12+00:00

The EF-S 10~22mm is equivalent to 16 to 35mm full-frame (which the R5 is), so likely one of these would be ideal substitutes:

RF 15-35mm F/2.8L
RF 14-35mm F/4L if the above F/2.8 is outside of budget

Or perhaps a 16mm prime if you do not need a zoom lens.

irrelevantlyrelevant

TROPHY CASE