Seriously, if a single politician stepped forward and said "i'll bring down ram prices" they could then shoot a puppy and call me a slur and I'd still go out and doorknock for them.

Memory companies have price fixed multiple times. They've paid hundreds of millions in fines. wikipedia even has a page on it. https://en.wikipedia.org/wiki/DRAM_industry_price_fixing.

Look at the financials of these companies, they're all making obscene margins and do they plan to increase production? No. Micron is doing a stock buy back to pump the price of their share.

The Micron CEO just recently said this is the exact plan https://www.theregister.com/systems/2026/10/01/ram-supply-se...

There's sanctions, tarrifs, and a DOJ who doesn't give a shit. Until we can fix that the insanity will continue. Phones will be unaffordable. Laptops will be obscene. Gaming consoles will be thousands of dollars. Desktops will be dead.

If you're waiting for some David Ricardo equation to happen, tough cookies, it's not coming.

The market is legally locked down and we're in hostage pricing mode.

And what's the story? You can't afford electronics because we're using it to build robots to take your job? I mean ...

Nobody is coming to save us. That's our job.

Are you counting the n-gram/PLE as part of the model weights there? They can go in host memory. Would be good to show your working. Also the released weights are pre-quantised and presumably QATed, so your "Full Precision" and INT8 are simply not a version of the model that actually exists.

Edit: I went and checked for you. The LM backbone is 307.2 GB (286.1 GiB), straight from DeepSeek's upload. The n-gram table is 203.1 GB (189.1 GiB), which goes in host RAM. Note the embeddings are higher precision than the expert tensors, so it's a larger fraction of the bytes than it is of the parameters.

So,

> Call me crazy but:

You're crazy. :-)

There's no BF16, original full quality weights are quantized already and 510GB.

Then good portion of those weights are n-grams (~200GB) that don't need to be in VRAM.

Then KV cache of that model is super lightweight at ~1GB per 1M tokens. If HBF succeeds, then accelerator with 16GB of VRAM and 1TB HBF/NAND is probably all you need (?).

Projects like DwarfStar https://github.com/antirez/ds4 really lower the hardware bar a lot so Deepseek 4.1 flash and other mixture of expert models can run on consumer hardware. There are also other inference providers who make their money serving openweight models. Services like OpenRouter make it all too easy to utilize these models. Access to these models isn't hard. The hardware moat is becoming pretty easy to bridge.

"1070s or 1070 TIs because GPUs have been severely overpriced for too long" ... ."

1070ti launch MSRP was $450 ish. 5070 could be had in the last year for 5xx-6xx range easily.

All things considered - (inflation being about 30%~ (guess)) between these two timelines. You are looking at 300% performance difference at a cost dollar for dollar that is cheaper then when they purchased their cards.

Might be a bit of a stretch blaming it on "severely overpriced for too long..."

Not quite: not all of this needs to be in VRAM

It has a set of n-gram tables which you can stream from system RAM or even NVMe

That said it’s still quite big! I can’t fit it on my DGX Spark, though I believe you can if you have two?

Thanks for the data!

Allow a question from someone who’s only got a very vague idea of how this kind of stuff works behind the scenes: say I rent usage of this model through one of the many LLM hosting providers out there, and let‘s assume I use it extensively through something like Pi or OpenCode and vibe code away all the time, keeping the hosted model occupied as much as I can, happily burning my credits.

Does that mean that there is a hardware cluster as described by you above that is crunching away just for me?

So at FP16, I alone keep a 1,664 GiB system occupied all the time?

The article isn't just about running locally though. The author is saying it's super cheap to run the model through Opencode Go (and presumably OpenRouter etc.) Personally I'm always most excited by models I can actually run locally, but even these huge open source models open up the competitive landscape for companies to let you call models via an API or just lease compute. And they don't have to charge you to offset research, training, huge staffs of the best minds in the world, crazy PR etc. I think that's a big win for customers and buts competitive pressure on the frontier labs as well.

I think, considering the size of this model, it's closer to a Pro than a Flash on everything other than speed

I did somet math and completely gave up on the idea of trying any worthwhile local model and figured I'd rather pay the 15-30 USD per month via subscription and/or API key combos for years than buying a local setup which might go out of date very fast, if it doesn't goes kaput just out of warranty. I won't be surprised if RAM scarcity is an concerted effort to herd people towards the remote models :)

> Even so why would anyone not sleep on a model they cannot run?

Because it's an open model so providers compete on price.

I just un-retire my pair of 1080Ti for some small models development because the current GPU prices literally make me sad.

This “blame sama for memory prices” meme is so tired.

He gave demand signal so many times years ago and was mocked for it and now we have the consequences of industry not taking him seriously.

It's dangerous to go alone. Take this: [1]

I reimplemented most of the features of the Deepseek v4.1 flash paper (apart from quantization aware training which doesn't make sense because my implementation uses float32 precision anyways)

I'm currently learning how to distill reasoning traces (check my other github repositories) but I think that a locally selfhostable deepseek is possible with my mixture of experts sharding mechanism. I decided to optimize everything for CPU parallelization, with the idea that the KV cache and meta model have to run from CPU RAM anyways, so the experts can also be loaded/unloaded at runtime if needbe, to save more RAM.

My assumption is that the KV cache optimizations in combination with the CED and compressed attention features are the reason why v4.1 flash has so few hallucination problems and such a strong self-lookup/thinking behavior. But that's more a gut feeling, need to evaluate and test this more thoroughly.

Anyways, would love to see someone train this on their own datasets. Currently my pipeline is kinda optimized for parquet and zim files.

[1] https://github.com/cookiengineer/gonano

The bulk of its weights are natively MXFP4. And engram values don't need to be in vram.

one can make a fine gaming pc for ~$350

1660 ti, 4790k, 16gb ddr3

You can run it locally for the price of a decent car, or run it (hopefully) privately on somebody else's hardware at vast.ai or a similar provider for much less. What's not to like?

No, you won't get frontier-level intelligence on a 1070Ti. Yes, it should be illegal to do what Altman did. Since we clearly don't live in the best of all possible worlds, we need to settle, and DS4.1 Flash is a good place to do that.

For tasks that don't require vision I personally like the NVFP4 quant of GLM 5.3 from Local Inference Lab better than DS4.1F, but they are both well beyond awesome.

He doesn't ruin the cost of memory. Advances in memory size and speed are now in full speed mode. Expect drastic increase in the upcoming years. Big factories are in the making and planned. Gigalab in the US and many others in the east. Since 2010 we have computers with 16gb as being normal. Finally we are moving into a new era where the standard will be 64gb next year and 128 in 2028. Hopefully we reach 1tb in 2030.

What are you talking about? The model is native NVFP4, why you run it at any precision higher than that?