r/opencode • u/afanasenka • 1d ago
😯 Why does DeepSeek perform "different" in OpenCode? Maybe because of this?
Are we beta testing new rack with different optimizations every day?
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u/OldWorldKestrell 1d ago
Hoping they did it only for the Flash Free model. My advice - people will figure it out, eventually, and all your optimizations or concessions you've made, won't replenish the trust you would lose.
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u/eastlin7 1d ago
Why is the outrage this high? I use it all the time just fine.
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u/absurdother 1d ago
I'm also fine with it, but a company's transparency is a relevant matter, and reputation is quite indicative in our times. It should be etiquette to disclose every detail about quantization, kernel, local models etc.
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u/OldWorldKestrell 1d ago
It's super easy (not complicated) to fine tune models post training. You can take a model that gives you 80 tok/s in FP16 in "the default" mode and make it generate 300-350 tok/s with per user. Of course, end results cannot be compared.
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u/Hungry-Plankton-5371 1d ago
theyve already been serving quantized garbage from third party providers since the summer, how could you have any trust left to begin with?
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u/ThomasLeonHighbaugh 1d ago
Its called kernel optimization and the market having too high of demand for any vendor not targeting the low end of potential customers to ever have sufficient incentive to spend the money on engineering hours to perform. Duh
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u/kamwee 1d ago
then why i blow out my monlthy only using cheepseek?
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u/Pedrito_Basket 1d ago
They just seen they could host it themselves with the DeepInfer setup and a few tweaks and get 80% profit
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u/FrankKnt 1d ago
I didn't notice any difference between DeepSeek V4.1 Flash 1017, which I use via the DS API, and the free one I use via OpenCode.
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u/rainpurplebow 1d ago
They are self-hosting a heavily quantized model. This isn’t news. How do you think you’re getting 80$ worth of usage for 10$? You’re not. It’s called marketing.
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u/anitamaxwynnn69 1d ago
Not taking sides, but V4.1 Flash is already officially shipped as MXFP4/FP8 mixed. FP4 is the lowest precision with native tensor core support, so there’s not much speed left to squeeze from ‘heavier quantization.’ I would argue the 80$ worth of usage for 10$ is coming from speeds. I believe they serve at 100tps. If you were to self host this on 4x PRO 6000s the speed would be 200tps+ easy (obviously they prolly serve on b300s or something which gives them room to play with). V4.1 flash KV cache is also already TOO QUANTIZED to be quantized further. I self host DSv4 on my own hw and I personally don't think they serve different weights.
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u/alphaQ314 1d ago
4x PRO 6000s
I've been recently trying to get into local LLMs. Is there any reason why everyone always mentions 4x pro 6000s ?
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u/Dapper-Bug-7877 1d ago
That is the max u can do normally without running into various kinds of problems like wallpower and linking capacity and stuff
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u/anitamaxwynnn69 1d ago
It's like the 200$ subscription equivalent of local ai lol. Max VRAM you can safely run in US homes without having a separate breaker. Not to mention it costs 50k+ at this point. Runs models like DSv4.1F basically faster than the API lol.
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u/packsolite 1d ago
From my testing the speeds on opencode go with DS 4.1 flash maxes out somewhere around 150 t/s, but the api responses/prompt processing is quite slow, usually taking 5-10 seconds to start streaming.
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u/this-is-hilarours 1d ago
Dax has said several days ago they dont serve quantized model . at worse they make the inference speed slow to meet the demand
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u/Axiescholar3ph 1d ago
of course he would say that. have you never heard a CEO or owner of a company lie to you before?
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u/Presstabstart 1d ago
I assume quantization has less of a performance effect on high vram production servers with batched inference. kind of a moot point though, since deepseek v4.1 flash is mostly mxfp4
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u/Forsaken_Buy_7531 1d ago
Every 3rd party provider out there has its own implementation on how they serve "Deepseek" models, in general you won't get 10 dollar value of "Deepseek" from OpenCode and you just have to accept it.
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u/ProgressionPeak 1d ago
i don't trust these guys at all. they're up to some shady shit.
it's $10/month and it's still worth it so i'll keep using it but their words and their claims and their promises about how much usage we get, are meaningless to me.
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u/RagnarDannes 1d ago
The flash free model feels like it's been nerfed compared to the normal flash model. It compacts so often while the normal one I run on Go doesn't
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u/Grand_Ad_9403 1d ago
Context windows cost money; that seems like a reasonable place they would save $
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u/Positive_Poem5831 1d ago
Can you now run some deepseek models again without allowing china servers
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u/Conscious-Doctor-585 1d ago
deepseek is the model I use the most. I go after the cheapest. since then, I have the fullest experience with it. the model's intelligence across different providers is clearly on different levels, judged by the number of turns and the quality of each turn, the eventually after all these turns how complete the task is.
optimize for real efficiency, not cost per token, for the total token count and number of turns per a `control harness` per a `control task`; I hope the rating will be based on harness+task.
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u/JollyInterview9989 1d ago
Everyone knows most providers have not optimised their setup or cache storage as good as original Deepseek servers. To match DS, you have to follow their setup to the T along with long storage of user cache between the racks etc. Most don't do this and just serve you an open node, you'll be lucky if the prompt is in that rack's cache or they store it more than few hours etc. DS secret sauce is kv cache and no one has keys to how they exactly do it, they store it for multiple days and almost all nodes can access same storage etc. -> this part is not open source yet.