Is there a Quantized version of this?
DeepSeek v4
51–60 of 1001 posts
Re: DeepSeek v4
#52Earlier quoted context omitted.
The thing is, it doesnt need to beat 4.7. it just needs to do somewhat well against it. This is free... as in you can download it, run it on your systems and finetune it to be the way you want it to be.
... if you have 800 GB of VRAM free.
I remember reading about some new frameworks have been coming out to allow Macs to stream weights of huge models live from fast SSDs and produce quality output, albeit slowly. Apart from that...good luck finding that much available VRAM haha
Re: DeepSeek v4
#53[flagged]
Why did you bother to submit an AI comment?
Re: DeepSeek v4
#54History doesn't always repeat itself. But if it does, then in the following week we'll see DeepSeek4 floods every AI-related online space. Thousands of posts swearing how it's better than the latest models OpenAI/Anthropic/Google have but only costs pennies. Then a few weeks later it'll be forgotten by most.
It's difficult because even if the underlying model is very good, not having a pre-built harness like Claude Code makes it very un-sticky for most devs. Even at equal quality, the friction (or at least perceived friction) is higher than the mainstream models.
They have instructions right on their page on how to use claude code with it.
Re: DeepSeek v4
#55Re: DeepSeek v4
#56Earlier quoted context omitted.
How does it compare to Opus 4.7? I've been immersed in 4.7 all week participating in the Anthropic Opus 4.7 hackathon and it's pretty impressive even if it's ravenous from a token perspective compared to 4.6
It is more than good enough and has effectively caught up with Opus 4.6 and GPT 5.4 according to the benchmarks. It's about 2 months behind GPT 5.5 and Opus 4.7. As long as it is cheap to run for the hosting providers and it is frontier level, it is a very competitive model and impressive against the others. I give it 2 years maximum for consumer hardware to run models that are 500B - 800B quantized on their machines…
What's going to change in 2 years that would allow users to run 500B-800B parameter models on consumer hardware?
Re: DeepSeek v4
#57How long does it usually take for folks to make smaller distills of these models? I really want to see how this will do when brought down to a size that will run on a Macbook.
Weren't there some frameworks recently released to allow Macs to stream weights from fast SSDs and thus fit way more parameters than what would normally fit in RAM?
I have never tried one yet but I am considering trying that for a medium sized model.
Re: DeepSeek v4
#58The Flash version is 284B A13B in mixed FP8 / FP4 and the full native precision weights total approximately 154 GB. KV cache is said to take 10% as much space as V3. This looks very accessible for people running "large" local models. It's a nice follow up to the Gemma 4 and Qwen3.5 small local models.
Price is appealing to me. I have been using gemini 3 flash mainly for chat. I may give it a try.
input: $0.14/$0.28 (whereas gemini $0.5/$3)
Does anyone know why output prices have such a big gap?
Re: DeepSeek v4
#59Earlier quoted context omitted.
The thing is, it doesnt need to beat 4.7. it just needs to do somewhat well against it. This is free... as in you can download it, run it on your systems and finetune it to be the way you want it to be.
Do you think a lot of people have “systems” to run a 1.6T model?
No, but businesses do. Being able to run quality LLMs without your business, or business's private information, being held at the mercy of another corp has a lot of value.
Re: DeepSeek v4
#60Already on Openrouter. Pro version is $1.74/m/input, $3.48/m/output, while flash $0.14/m/input, 0.28/m/output.