51–60 of 496 posts
I hope to see something like this, but in a small form factor like the NVIDIA spark. I want a super fast LLM that is Opus 4.6+, like, in ability.
One thing I don't like about California based companies is how cringe the names always are. "Jalapeño" is such a bad name, having an "ñ" already makes it difficult and annoying to deal with in so many little ways. Good luck with that. But also, theres the sort of "yes lets use Mexican related things because we're California" thought that I just really hate. I don't know, its like corporate Memphis to me. You see a pr…
>designed for initial deployment by the end of 2026 and expanding in the years ahead, So after the IPO and will be featured heavily in the IPO sales brochure as a future promise? I'm sceptical over any pre-IPO announcements.
Who's IPO? Broadcom and Google are already listed, obviously.
I mean I'd love to be able to buy something like the 17k tps taalas chip as a pcie or m.2. Imagine when we can roar along at that speed, low power. Can just have the model reason for a while about anything and everything. It reminds me of the "race to idle" for mcus etc.
> 17k tps taalas chip It's odd to me that I haven't heard anything about this approach (baking LLMs/weights into silicon directly) since. It seems almost common-sense that we're going to end up there eventually . And it feels like that point is drawing ever closer now that model capabilities, if not quite plateauing out, are at least getting to a "good enough" point for a LOT of use cases. I wonder if it's being work…
Taalas has a running demo here: https://chatjimmy.ai/
It's eye opening: generated an AVX-512 optimized Mersenne Twister in C in 0.076s, 13,706 tok/s. Too fast for the tok/s to be terribly accurate.
We’ve entered the “if you care about software, build hardware” phase of AI
I have been eyeing what Taalas is doing [1] by making pure hardware models. The speed is absurd. [1] https://taalas.com/products/
I know, it's nick picking, but when people can just reach in and take services away, like Fable/Mythos, hardware is the only thing worth buying.
I hope to see something like this, but in a small form factor like the NVIDIA spark. I want a super fast LLM that is Opus 4.6+, like, in ability.
Memory bandwidth is the bottleneck in the Spark. If you replace the SoC with an optimized ASIC but keep the same 256-bit LPDDR5 the performance will be the same. You can increase performance by using wider memory but that's also more expensive.
>designed for initial deployment by the end of 2026 and expanding in the years ahead, So after the IPO and will be featured heavily in the IPO sales brochure as a future promise? I'm sceptical over any pre-IPO announcements.
Who's IPO? Broadcom and Google are already listed, obviously.
Cerebras stock is down nearly 20% today.
Not only is approach overlapping, OpenAI is also Cerebras's only major customer.