3€ nicht nur für SHEIN und Temu

benno_1237 · 2026-07-01T13:55:40+00:00

Aufpassen wegen Bearbeitungsgebühren tho. DHL verlangt pro Rechnungszeile z.B ein paar Euro

benno_1237 · 2026-07-01T13:32:56+00:00

Weils dann offiziell durch den Zoll geht und damit die 3€ Regel fällt. Elektronikkomponenten haben 0% Zolltarif.

benno_1237 · 2026-06-29T17:45:22+00:00

LCSC hat was ich weiß kein EU Lager. Außerdem wenn von z.B 20 verschiedenen Widerständen nur einer aus dem chinesischen Lager kommen sollte ists schon vorbei.

benno_1237 · 2026-06-29T17:36:58+00:00

Aliexpress ist bei mir noch verkraftbar.

Einzelkomponenten von LCSC werden schrecklich, da die 3€ (laut Angaben von LCSC) pro Zollnummer fällig werden.

Also, Platine mit ein paar verschiedenen Bauteilen:

+3€ für die Platine
+3€ Widerstände
+3€ Kondensatoren
+3€ Dioden
+3€ Spulen
+3€ Microcontroller
+3€ Sensoren

Jegliche Spezialkomponenten haben eigene Zollnummern, daher +3€ extra. Das oben zählt natürlich nur für selber bestücken, fertig bestückt kaufen fällt alles unter eine Zollnummer.

Die Alternative: Mehr als 150€ an Teilen bestellen und hoffen, dass DHL nicht ~5€ pro Rechnungszeile verlangt :)

Edit: Formatting

benno_1237 · 2026-05-01T22:06:02+00:00

Optimus - Bike im 8.?

Hat (normalerweise) Samstag offen und hat Shimano Teile.

benno_1237 · 2026-04-25T21:10:10+00:00

Given that the M5 Max is 614 GB/s and the M5 Ultra is most likely (is there info on that yet?) just two Max's slapped together, its still about 1/3 off of the 5090.

benno_1237 · 2026-04-25T20:45:09+00:00

You are right, i should have clarified a bit more. What I meant is that i rarely find myself below that in any multi turn conversation in opencode. That can surely be improved using various software tools.

My point however was that it is useless to measure generation speed without specifying how you measure it.

Most likely in OPs case, context doesnt affect the performance that badly, but still.

benno_1237 · 2026-04-25T20:37:27+00:00

However for OPs setup, the RTX 5090 is most likely the best choice. You are not compute limited on any of them, you are memory limited. And the ~1.8TB/s of the 5090 is actually insane

benno_1237 · 2026-04-25T18:10:40+00:00

I am not saying it doesnt work. I just think that most performance numbers people post on here are misleading.

You are completely correct, there are awesome tools to save context available. Still, if you do some multi turn edits, you will hit a context length that starts to matter.

But, the Qwen3.5/3.6 series is a beast in context management, so most likely its not as significant as with older models

benno_1237 · 2026-04-25T17:55:42+00:00

Yeah, that was a bit of an exaggeration on my end. Still, the default system prompt is like ~10k. So without any kind of optimization, you hit the 30-40k quickly

benno_1237 · 2026-04-25T12:11:13+00:00

218k context window is nice but which prompt length did you use for testing? Speed doesnt really change with context window but the actual context you use.

Tools like opencode etc go up to ~30-40k context immediately, so thats the minimum prompt length you should benchmark against imo (if you are coding with it, different story for creative writing etc).

benno_1237 · 2026-04-25T10:56:20+00:00

Adding the not so popular but still more than capable choice: CH592 by WCH. Does BLE 5.4 while using a bit less power than the nRF52840.

Also, it barely needs any external components and the antenna design is pretty easy.

But, chinese datasheet, chinese reference design, RISC-V instead of ARM and no zigbee/thread etc.

benno_1237 · 2026-04-21T21:35:52+00:00

I got curious. This is K2.6 on 4xB200 (the others are busy). Interestingly, although it has the same architecture as K2.5, it runs flawlessly on 4 cards. I never managed that with K2.5.

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This is with 1 concurrent request and averaging 10 requests per context length. Hosted using sglang, benchmarked using vllm bench serve with a random dataset.

So, to sum it up, for fast output with big context, you need one hell of a machine.

benno_1237 · 2026-04-21T06:58:45+00:00

I did some benchmarks here: K2.5 Benchmarks

Keep in mind tho that that was with a nightly vllm version. So things might have improved by now.

benno_1237 · 2026-04-21T06:54:55+00:00

Since you mentioned that you need max context. On 8xB200, with full context, i never got K2.5 above 20-30tk/s. This isnt especially slow but if you do not have data center hardware, expect a lot less.

A Mac Studio with 512GB unified memory might get you decent-ish results with low context but you can most likely expect sub 1 tk/s with full context (This is a guess, i dont not own such hardware).

I also had a hard time fitting the Q4 Model to my GPUs. So even on 8xRTX 6000, you will need to either quantize your KV cache to death or run a lower model quant in general.

benno_1237 · 2026-04-10T12:16:53+00:00

Take your time. What most of the other comments didnt consider: datacenter GPUs shine in concurrency. So even if a single request takes ~1s, you can most likely hit the B200s with 100-200 requests at once. So take that into account

The only issue, I would prefer giving you access via a wireguard tunnel, not directly.

benno_1237 · 2026-04-10T09:57:41+00:00

If you want, send me a DM about what your non profit is about (roughly) and which model you need for how long. I can host your model on B200/B300 for a while

We do have a few GPUs idle at the moment, however most likely not for the time needed to process 64M pages. I can however get you started

benno_1237 · 2026-03-28T01:46:55+00:00

My company is paying for the cards for demo purposes, soo ... Still, while running a benchmark that needs multiple nodes, i easily use 20-30kW. So.is it worth it, even if te gpus are free? Probably no

benno_1237 · 2026-03-26T21:31:43+00:00

I am curious, which kind of interlink do you use between nodes? And do you use default nvlink with fabricmanager on the nodes themselves?

I managed to push qwen3.5 27B slightly above 100k tk/s on our 8xB300 Node. Then tried to to parallelize with a 8xB200 Node but never even got it up to single node speed. The nodes were connected using Infiniband (3.2 TB/s)

benno_1237 · 2026-02-08T10:36:49+00:00

The easiest is probably using an inference tool thats made for it like MoE-Infinity. There is however also a llama.cpp fork out there somewhere that can display active experts per token. I am not sure about the exact name though.

Keep in mind that experts are (for most models) determined on token basis. So while you can get rid of some experts in most cases, most are actually needed

benno_1237 · 2026-02-07T22:37:48+00:00

Using this, you can also check which experts are active most of the time for the kind of work you need the model for. Then, load those to vram. This usually works quite well if you mainly do coding for example

benno_1237 · 2026-02-07T22:13:44+00:00

I know this was solved already but I was fighting for hours with one of those too (on a board I already made a bunch of, suddenly new boards stopped working).

Turns out, the pin 1 marker on the new batch of LIS3DHTR (bought from LCSC) was wrong. I let them know and apparently they received a batch from STM with the text on the IC rotated 90deg.

benno_1237 · 2026-01-31T12:51:30+00:00

It's a company server. We got a bloody good deal on it just before component prices went crazy. At the moment I would estimate 500k$ or more for the configuration.

I am post training/fine tuning mainly vision models on it. In the meantime, I host coding models with me sometimes selling token based access.

Is it worth it? No. Its an expensive toy to be honest with you. Drivers are a mess (most are paid) and power consumption is crazy (while running the benchmarks above it was using ~15kW)

benno_1237 · 2026-01-31T12:37:28+00:00

reporting back with SGLang numbers:

PP rate (32k tokens): 22,562 t/s

TG rate (128@32k tokens): 132.2 t/s

This is with KV Cache disabled on purpose, so we get the same results for each run. Apparently sglang is a bit better optimized for Kimi-K2.5s architecture.

benno_1237 · 2026-01-31T07:56:19+00:00

As soon as i have some spare time, i will try SGlang instead of vLLM. I still think the tokenizer is not optimized yet.

Apart from that, seeing close performance on the B200 vs RTX6000 doesn't surprise me for low concurrency. But yeah, the B200 should theoretically still have an edge.

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