Replying to @⁨panda_abyss@lemmy.ca⁩

The efficiency of Chinese models really is impressive. I generated sooo much code yesterday with Qwen3.6 35B-A3B running on an RTX 5060 Ti 16GB (+ a little CPU offloading). It got the jobs done at ~50 tokens/sec.

(It’s not super complex code, just some scripts that I would not have taken to time to write manually.)

I’d love to upgrade to something with more VRAM, but even my current card has doubled in price since I bought it last year 😬

Replying to @⁨isVeryLoud@lemmy.ca⁩

There’s not really anything interesting to show. It’s just a home server in a 13 year old desktop ATX case.

There’s no desk, monitor, keyboard, or mouse… But also no cool server rack.

Function over form, and it sits in a spare bedroom out of sight.

EDIT: I found the receipt for the case. It’s a Cougar Volant Black Steel mid tower, purchased in 2013. So my server just looks like this:

Replying to @⁨percent@infosec.pub⁩

I meant your LLM stack lol. I just have an RX 6800 XT in my main Linux PC for inference, but it has to share VRAM with the DE. Maybe I’ll set it up for remote development from my laptop instead to free up VRAM.

What are you using? vLLM? llama.cpp? Which params? How much CPU offloading? Do you use draft models? Is it a MoE model? Have you tried llama-swap? Which agentic front-end are you using? I presume you set it up to access it without SSH’ing into the machine, did you do anything special or is it just a raw unsecured open port on the machine to the LAN?

Replying to @⁨isVeryLoud@lemmy.ca⁩

Ohhh lol. Yeah it’s Llama-swap, running llama.cpp for now, but might add vLLM to the llama-swap config to experiment with NVFP4.

I mainly use MoE models so I can get decent speed while using a 150-200k context window. My go-to model has been Qwen3.6 35B-A3B for a while. I tried Qwen3.8 27B, but it was too slow.

Gemma4 26B-A4B also runs nice and fast, but I generally get better results from Qwen3.6. I don’t remember exactly how much CPU offloading is happening, but it’s not much. As long as I can get like 40-50 tokens/sec, I’m usually satisfied enough.

For the coding harness, I’ve been running Pi in an Apple Container (sort of like Podman, but better isolation in a microvm). Though, I recently configured VS Code to use LLMs on my server, and it was actually pretty decent. Still need to explore a bit more, but so far VS Code’s AI capabilities seem much better than they were a year ago (they seemed way behind, back then).

Also, I don’t connect any harness directly to llama-swap. I have another container running Caddy, which acts as a gateway to AI providers. For other services (e.g. OpenRouter), the API key is injected in the Caddy container. I don’t like having API keys or secrets anywhere where LLMs can read them. It’s not so bad for my own self-hosted LLMs, but not cool to send secrets to a server owned by someone else.

Replying to @⁨percent@infosec.pub⁩

How has tool use been for you? I struggled a lot with tool use with Gemma and Qwen, to the point where I needed to build a healing layer.

Regarding the coding harness, I was looking for something CLI-based or JetBrains-based, and I haven’t had much luck getting my local llama.cpp models playing ball with OpenCode. They keep losing context and misusing tools.

I’m not too familiar with Apple containers as I’m running a full Linux stack, but I’ll give Pi a try, seems interesting! Does it work for coding tasks or is it strictly an “orchestrator”?

Replying to @⁨isVeryLoud@lemmy.ca⁩

Tool use with Gemma has been hit or miss. I wouldn’t rely on it for anything unsupervised.

Tool use for Qwen3.6 has been great lately, but I do remember seeing some issues with it too, a while back. I don’t remember when/why the issues cleared up (I have tweaked configs a bit over time), but switching to Pi definitely helped.

I do remember having a lot more problems in OpenCode and it was practically unusable (which is why my recent experience with VS Code was surprising). I’d definitely recommend trying Pi.

A fresh Pi install is very minimal by design. The system prompt is tiny, so it’s a pretty good fit for small LLMs like these. It’s sort of like Neovim: Nothing fancy out of the box, but you can add lots of fancy things to it. I containerize it because I don’t like giving LLMs (especially these small ones) unrestricted access to my host computer – though, I have not seen any signs of it accidentally doing something destructive, which is surprising.

There are similar alternatives to Apple Container for Linux (e.g. Docker Sandboxes, muvm, Firecracker). There’s also this thing made specifically for Pi called Gondolin. I haven’t tried it yet, but I may end up switching to that if it could simplify my stack.

Here’s my current llama-swap/llama.cpp config for Qwen3.6 35B-A3B:

qwen3.6-35b-a3b:
    name: "Qwen3.6 35B-A3B (Coding)"
    proxy: "http://127.0.0.1/:${PORT}" # If you're seeing a `/` after `127.0.0.1` here, don't include it. I think something in Lemmy is trying to "sanitize" this input by adding the `/`.
    cmd: |
      llama-server
      --port ${PORT}
      --no-webui
      -hf unsloth/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL
      --jinja
      --parallel 1
      --flash-attn on
      --no-mmproj
      --load-mode none
      --reasoning-preserve
      --ctx-size 190000
      --temp 0.6
      --top-p 0.95
      --top-k 20
      --min-p 0.0
      --presence-penalty 0.0
      --repeat-penalty 1.01

A few notes about this config:

  • Now that I think of it, –reasoning-preserve might be another thing that helped with tool calls.
  • Note the -MTP part of the -hf param. MTP helps speed things up. Here’s the Huggingface page for this model
  • You can also omit –no-mmproj if you need vision, but it might mean sacrificing speed or context size, so I usually just enable vision in a separate llama-swap model entry to use as needed.
  • Unsloth recommends –repeat-penalty 1.0, but I saw the LLM enter a thinking loop in VS Code, so I bumped it up just a tiny bit to 1.01. I have since seen it do something that resembled the same thought loop, but it was able to recover on its own. Not sure if it’s a coincidence or if 1.01 was actually the solution, so worth some experimentation.
There are many agent harnesses, but this one is yours.pi.devPi Coding AgentA terminal-based coding agent

Replying to @⁨Damage@feddit.it⁩

I actually did exactly that previously! I had both an RX 6800 XT and an RX 6600 in my system and I used the 6600 for video output. Unfortunately, this cuts my RX 6800 XT from PCIe 4 16x to PCIe 4 8x and severely slows down model loading for llama-swap. Joys of the X570!

And yes, I do have it running right now with a bit of occupied VRAM, but I need to limit my model to 14 GB to leave 2 GB free for GNOME Shell. I really want one of those 64 GB UMA Mac Mini, I heard they work really well because the GPU has direct access to system RAM.

Replying to @⁨isVeryLoud@lemmy.ca⁩

Yeah I was lucky to buy a 6900XT when it was near the lowest price, so I sold that and added a couple hundred for the 7900, seemed like a good future-proofing move, given the times we’re living in.

I think Apple silicon is faster than my generation of Ryzen, but the newest (Strix something?) with the LPDDRGGFASEWARGH5 memory should be faster. Of course buying all that memory right now would be quite painful.

Replying to @⁨devfuuu@lemmy.world⁩

Not true. I’ve been involved in litigation with both AWS and Google over unsecured comms that were defined as being TLS secured in SLA/SLO contracts and found not to be. Not that anything nefarious was happening necessarily, but the expectation is clear.

Whether these asshats even check for such requirements with a Musk run company right now 🤷

It COULD possibly be that they are logging every exchange happening at the network fabric between the service layers, but nobody knows unless they intentionally take steps to investigate or accidentally prove it.

Replying to @⁨expr@programming.dev⁩

You’re missing the point, nobody is talking about trusting these companies.

You can tell if a connection is encrypted end to end or not. And if your paying for that service you can sue if you aren’t receiving what your paying for.

In digital security nobody relies on trust if they can help it. And security matters if you want to keep a competitive edge on your competition, so even shitty companies care about that.

Replying to @⁨Cocodapuf@lemmy.world⁩

I was not talking about your specific lawsuit, or TLS.

Companies trust other companies all the time, and it’s foundational to most all SLA/SLOs. Any time I’ve voiced concerns around how AI companies are using the data we are giving them (like giving them access to our codebase), it’s brushed off as “we have an agreement with them”. It’s just a load of hogwash. They can and will abuse all data they have access to, just as they have done thus far.

In this particular case, we are talking about a data center, and it is not at all reasonable to assume that the data that flows to said data center is in any way protected, especially one run by Musk.

Replying to an earlier post

https://epoch.ai/data/ai-data-centers/directory/colossus-2 says “Memphis, Tennessee, United States” but also provides satellite imagery

Epoch AIColossus 2 | AI Data CentersColossus 2 is an operational AI data center with AI chips owned by SpaceXAI and used by Anthropic, Cursor, and SpaceXAI. It hosts an estimated 1,112k H100-equivalents of compute, supported by 946 MW o

Replying to @⁨GolfFoxtrotLima@sh.itjust.works⁩

They can’t stay operational even when they don’t have to power/water…

Another possible culprit could be SpaceXAI. The company formerly known as xAI before being rolled into Musk’s rocket company SpaceX boasts a huge glut of computing power with its massive Colossus data centers near Memphis, and selling this to its direct rivals has become a multibillion dollar stream of revenue. Anthropic is one of those customers; the two companies announced a “compute partnership” in May.

The same afternoon that the major AI models went down, SpaceXAI issued a statement on social media that made passing reference to being partially responsible for the widely experienced outage.

“We are sorry for the issues you may have experienced with Grok following an outage at our Memphis compute center this morning,” SpaceXAI wrote on Thursday. “We’d also like to apologize to our impacted compute partners.”

Technical difficulties at its data centers would explain why SpaceXAI’s own chatbot, Grok, and Anthropic’s Claude went down together. But that leaves out OpenAI’s ChatGPT. Is it merely a coincidence that it suffered a “routing error” at the same time as its competitors on a different platform? We still can’t say.

They’re straight up lying about the actual costs of current chatbots, and for it to be productive it will cost a shit ton more resources.

There’s zero reason to be scaling it up right now. It’s gambling that if enough money is dumped in it will solve itself somehow.

Replying to @⁨GolfFoxtrotLima@sh.itjust.works⁩

It was a good time to call customer service for a lot of products. Instantly got a human when I called newegg and she was so scattered she gave me a full refund and they paid for my replacement which was a total of $900. They were probably so overloaded with calls they’ll never notice what happened. They even did the fastest delivery option on a Sunday, it’s already on its way!

Replying to @⁨DarrinBrunner@lemmy.world⁩

Well it was instantly in the sense of immediately picking up, putting me on hold for 2 minutes, getting some info, putting me on hold again for 2 minutes, asking what issue was, waiting 2 minutes, heard them put me on hold briefly again, then about 5 minutes of apologizing while saying it will be refunded and replacement shipped. I could hear a call center like atmosphere in the background with quite a few people talking so I’m guessing it was all hands on deck.

Next time this happens I’m going to call a few places and see about getting some free shit. Screw these companies, I paid enough with my time and their stupid bot answering system.

Replying to @⁨Archer@lemmy.world⁩

Yep. The college I attend got hacked twice in a year. First time they went with “it’s maintenance” on a Monday at peak hours, you know like you would maybe do that at I don’t know? When everyone’s sleeping? The second time they fessed up and sent out text’s that they “fixed” the data breach. So yeah cool all the school email data is out there now; sucks for those that use that as their Microsoft computer login.

Replying to @⁨Sam_Bass@lemmy.world⁩

Just out of curiosity do you actually have an analysis across the board that your statement is correct? For sure if someone is using a model like chatGPT 3.5 it’s going to be mostly trash output so not all models are made equally. My experience of the current generation is models are pretty accurate although still require supervision. Like you can see the path they’re going down and they need some corrections here and there. It’s a far cry from the rampant hallucination just a year ago.

Replying to @⁨M0oP0o@mander.xyz⁩

Nothing in that image hints even remotely at computer tech though. The cables are coaxial. No one uses those for computer networks since decades.
The cables look like those used for cable TV or satellite receivers. The cascade on the left looks like some HF tech. Wonder what’s actually shown in the image. It can’t be AI because AI can’t do that many cables properly.

Replying to @⁨Kaligalis@lemmy.world⁩

Yeah, and this is how I know you never worked in the random standard production to miss the humor. Its a picture of a tech bro type in a clearly decommissioned closet stressed out about what’s going on, a common thing and always funny. I can just picture the tech on site in the next room replacing a fan on a much less “cool” looking appliance while that guy stresses out on a conference call with the “stakeholders” on his coax nest.

Now that being said I have worked on places still using coax (might still be) and tape libraries, and all sorts of museum pieces well after you would think they would. But I can tell that picture is after decommission since it is clean. If that was in use it would be filthy and filled with so many things that may or may not be important/in use. Fans all clogged running at max rpm, and the smell… I can still smell it now. But then again I was the guy they called to fix shit after the local (often tech bro) it guy gives up so I think I often saw the worst places.