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DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence

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11–20 of 23 posts

Re: DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence

#11

From this thread [0] I can assume that because, while 1.6T, it is A49B, it can run (theoretically, very slow maybe) locally on consumer hardeware, or is that wrong? [0] https://news.ycombinator.com/item?id=47864835

Theoretically with streaming, any model that fit the disk can run on consumer hardware, just terribly slow.

Re: DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence

#16

The quality of this model vs the price is an insane value deal.

Models like Deepseek is the only reason we are able to categorize and measure quality of thousands of MCP servers (https://glama.ai/blog/2026-04-03-tool-definition-quality-sco...). That's billions of tokens – an expense that would be otherwise very hard to swallow.

Re: DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence

#17
post #2

Hmm. Looks like DeepSeek is just about 2 months behind the leaders now.

If that is really so, it would be now be good enough to replace claude for us; we use sonnet only; with our setup, use cases and tooling it works as well as opus 4.6, 4.7 so far. We won't replace sonnet as long as they have subscriptions but it is good to have alternatives for when they force pay per use eventually.

Yep, it should be better and more efficient then sonnet.

Re: DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence

#18

From this thread [0] I can assume that because, while 1.6T, it is A49B, it can run (theoretically, very slow maybe) locally on consumer hardeware, or is that wrong? [0] https://news.ycombinator.com/item?id=47864835

If 5090 has 32GB, and let's say somehow a 1-bit quantization is possible and you don't need more VRAM for anything else (forget KV cache etc), it would be able to fit a 256B 1-bit model. Just to picture it in extremes how unlikely this is.

And the active parameters come from the experts. For each token the model picks some experts to run the pass (usually 2 to 4, I haven't read V4's papers). It's not always the same experts.

OTOH, being DeepSeek, I foresee a bunch of V4 distilled FP8 models fitting in a 5090 with tiny batches and with performance close from 75 to 85% of V4. And this might be good enough for many everyday tasks.

Today is a good day for open models. Thank god for DeepSeek.

Re: DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence

#19

From this thread [0] I can assume that because, while 1.6T, it is A49B, it can run (theoretically, very slow maybe) locally on consumer hardeware, or is that wrong? [0] https://news.ycombinator.com/item?id=47864835

It will be Seconds Per Token instead of Tokens Per Second.

Re: DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence

#20
I used the flash version on a tricky Common Lisp coding problem this morning. The first cut of the new library had a runtime error. I was running in a simple REPL using:

ollama run deepseek-v4-flash:cloud

so I had to feed the generated code and the error back into the REPL manually, but it nailed it the second time, and the Common Lisp code was very good.

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