Comment by nejch | Hacker News Reader

Comment by nejch | Hacker News Reader

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My team runs open models for devs at our company, mostly on H200s, and I'd also say yes, if you want to always stay on the bleeding edge (and not use their model-specific images they publish before it lands in a release), then some amount of patching might be needed especially for new models.

The good thing is the models themselves are good enough to usually find the root cause if you give them read access to your deployment, logs and upstream issues/PRs to analyze.

If you do A/B deploys and E2E test them with popular harnesses (we do opencode/codex/claude), you'll catch most things. It'd be interesting to hear what the more nimble inference/neo-cloud providers do when they deploy models within days of them being released, as I know it definitely needs some patching.

But I think things have improved since the days when even chat templates/tool parsers were problematic, and their new flat model approach might help as well. I suspect some of the issues came from models inheriting config and parsers.

> if you want to always stay on the bleeding edge (and not use their model-specific images they publish before it lands in a release), then some amount of patching might be needed especially for new models.

Well, except this isn't exactly a bleeding edge architecture. DeepSeek-V4-Flash was released four months ago. I think it's not unreasonable to expect it to work well and be somewhat stable on mainstream hardware and not have critical bugs like e.g. garbage output?

Yes, agreed. I meant in general it's usually new models I'm seeing the biggest issues with. I hope the refactors on model arch/config will help with this so model changes have a smaller blast radius.

It's a lot of moving parts between reasoning parsers, tool parses, all kinds of MTP algorithms with different levels of support among popular etc. Even Kimi K3 saw more improvements in the latest release and it's essentially old news at this point.

On our side I've seen a lot of this garbled output in reasoning output but not in the output itself, though we did have to revert initially when we saw that a few versions ago.

Can I ask your setup? I’ve been tasked with taking a beefy machine (vague on details on purpose, the specs are legit) and trying to figure out how to run on-prem inference using open models. Gated in that I cannot use non-US models. I don’t think I have a handle on how to set it up for multiuser use without nuking TPS or context length. The current power users set and forget tasks overnight and expect results in the morning. Which… I have mixed feelings about, but that is the bar set by paying all the money for GitHub copilot enterprise.

Any tips?

With setup you mean HW or the SW stack?

We used to run GLM-5 class models but have now changed to smaller ones as we're able to serve more concurrent users with our limited hardware (DeepSeek-v4-Flash-0731, Qwen-3.8-27b). We run on the order of hundreds of parallel requests right now, Qwen with data parallelism and DSv4F with P/D disaggregation, but will probably continue to tweak this.

Qwen 3.8 27b in my experience is more than capable of churning out features overnight with the right tools (don't rely on its world knowledge, give it tools like playwright and github MCP for upstream context and search, and give it goals to work on). Bonus with another model like DSv4 as an adversarial reviewer.

> Gated in that I cannot use non-US models.

This is incredibly silly. First I would try to convince whoever has decreed this to reconsider. The only SOTA open-weight models are non-US, and rather than being afraid of potential Chinese backdoors secretly planted in open weight models you should be afraid of LLMs simply just doing something dumb. To give you an analogy: no one has ever demonstrated a backdoor in a popular model which maliciously runs 'rm -Rf *'; plenty of people have encountered a model running 'rm -Rf *' by mistake. You need to protect yourself against the model doing something stupid anyway, and, paradoxically, a US open-weight model makes this more likely due to them being weaker.

If you need a precedent then you can cite Mistral[1] hosting GLM-5.2 on their platform, or plenty of US-based providers on Open Router hosting Chinese models.

[1]: https://docs.mistral.ai/models/zai-glm-5-2

Yeah look, I don’t have a say in it. Also, the whole Chinese putting backdoors into routers hasn’t helped any.

https://www.reuters.com/world/asia-pacific/researchers-disco...

Did you actually try to convince them?

Again, this requirement is incredibly silly. Here, look at this (it's a link to AA; I shortened it since the link's huge):

https://tinyurl.com/2dhzdrt6

Right now, the best US-based open-weight model (Inkling) has 42 points and is 975B parameters. The best Chinese open-weight model has 60 points. Also, note, Qwen3.8-27B on low thinking beats Inkling here and gets 43 points.

Of course, AA scores are definitely not bulletproof and there is a certain amount of benchmaxxing going on, but the point stands -- it is *incredibly* silly to limit yourself to US-only models, just because of an imagined threat that doesn't exist. Let me state this in stronger terms: whoever decided this for you is clueless. They might have the authority to override you, but it's your job to tell them that the only thing this achieves is essentially pointing a gun at your own foot and pulling the trigger.

I think I’m just going to find the best model I’m authorized to use (of a few of them) and get on with it. You’re much more passionate than I am about Chinese models, I don’t really care about the topic much one way or the other. I show up to work to get paid and go home. I use qwen at my house, it’s pretty great.

Not gonna make problems for myself at work that I don’t need to make, and I sure don’t have any interest in sticking my neck out for models that come out of a country that doesn’t even hide the fact that they are a technological adversary.

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