If the specs seem outdated, it’s because this chip launched in 2024. It’s available at cloud providers including Cirrascale
Thats relieving!<p>I'm starting to believe in Qualcomm! The Snapdragon X2 Elite Extreme is alas only available in a single system, with it's tri-channel on-package (!) memory good for 228 GB/s. But it's a beast. The core is just good, even in the mainstream form in other systems: some of the best single core performance you can get, shy of Apple.<p>And they are upstreaming their stuff much better! They hired a bunch of really good known FOSS developers and it seems to be working!<p>Now they are talking up High Bandwidth Compute as their next big thing. And I want to believe, even if it makes me nervous that they too intend to leave consumer computing completely in the dust. <a href="https://www.qualcomm.com/news/onq/2026/11/qualcomm-hbc-validated-silicon-ai-inference" rel="nofollow">https://www.qualcomm.com/news/onq/2026/11/qualcomm-hbc-valid...</a><p>This is, as commented, at M4 Max levels of GB/s. It is not impressive. It has some uses! But if this was their modern opening salvo I would not be impressed.
What's the price?
According to their own website, they cost 1 cent.
Less than I expected for this level of GPU power.<p>It's a trend I like.
8,750 in stock at 1 cent, too. The more expensive 47 and 63 cent variants are all sold out.
Yeah, I clicked that too. But knowing qualcomm they obviously cost an arm and a leg. Still interesting how much.
Honestly, let me buy one, it's many times faster than the minipc I run local ai on so if the price is right I'm in.
hopefully less than 5090?
Curious how the memory bandwidth actually holds up under sustained inference loads compared to the spec sheet numbers.
So it's an M4 Max Mac Studio, RAM-wise, with a GPU's worth of compute?
(I know it's you comparing it to the M4 Max, not them, but:) it's Qualcomm, so it'll be compared to the M4 Max, while being slower than a M2 Max.
Sort of interesting design It has half a terabyte of SRAM on the accelerator with a 128 GB of RAM replacing HBM with SRAM but keeping regular RAM.
Seems like a reasonable general-purpose card.
Ok so it’s significantly worse than Apple M6 and we don’t know if it’s cheaper or not<p>Edit: Ah sorry you’re right, the bandwidth is lower but the TOPS is a lot higher. I’m not sure how much effect you get from higher TOPS with such low bandwidth though.
> I’m not sure how much effect you get from higher TOPS with such low bandwidth though.<p>Raised arithmetic intensity. It would be quite good for LLM prefill, and decode would want to adapt by pushing parallelism higher (saving on memory bandwidth by reusing the same layer weights for multiple streams) or, if unable to do so (e.g. due to KV cache constraints) by resorting to speculative decoding (where the expensive 'validate' step handles tokens in parallel within a single stream). It does not make the platform inherently unviable. It would also do well at other tasks such as image/video generation, which is already quite compute bound.
Apple M6 has 80-100 TOPS.<p>Qualcomm Cloud AI 100 Ultra has 800 TOPS, almost 10 times more.
This could be very fast at running embedding models (if it supports the right modes)
Considering that memory bandwidth is the most important for inference for most neural networks, that matters more.
Modern inference stacks use different GPUs for decode versus prefill. This is a GPU suitable for prefill.
Aren't these specs already outdated? Seems meh. I would think players would like to bring something new to the table instead of bringing what was okay two years ago or so.<p>Give me 1TB RAM and 5TB/s or something that exciting.
For most purposes, this seems reasonable? This looks like a single-height card, so fitting four of these in a workstation or server would give 512 GB RAM and 2 TB/s total bandwidth. That's pretty attractive for a lot of current LLMs, even if one wishes for 1 TB capacity at 5 TB/s bandwidth.
If you are going down this path, MI350P and RTX PRO 6000 Blackwell is what you would want. A lot more compute a lot more vram bandwidth.
Maybe. I don't know power for the MI350, but the RTX PRO 6000 is normally a 200-300W card, so you pay 2x the power for 3/4th the RAM and 3x the bandwidth. The performance frontier looks very different in terms of power to hold a mid-size model and throughout per user or across users. Price and inference stack would be big factors in what I would choose.
MI350P, H200 NVL and Nvidia RTX PRO 6000 are all 600W but compute wise all of them should be 3 to 4 times more performant than A100 Ultra according to their specifications.<p><a href="https://www.servethehome.com/the-amd-instinct-mi350p-is-a-hbm-pcie-accelerator-that-has-been-all-over/amd-mi350p-vs-nvidia-h200-nvl-vs-nvidia-rtx-pro-6000-blackwell-server-edition-key-specs/" rel="nofollow">https://www.servethehome.com/the-amd-instinct-mi350p-is-a-hb...</a>
> A lot more compute a lot more vram bandwidth.<p>But this gives you significantly more memory capacity for a compute/vram bandwidth tradeoff (i.e. arithmetic intensity) that's ultimately comparable to Blackwell, perhaps even more skewed towards compute. This seems good if you care about being able to store larger sparse models and more KV caches because that ultimately bottlenecks your ability to do inference.
But there are already plenty of products for most purposes.
5 year old GPUs are still at crazy prices.<p>Plenty of businesses want in local inference and are priced out.
This is all the CPU companies playing catch up with Nvidia and AMD.<p>The thing you are describing is much closer to AMD and Nvidia latest generation server offerings.<p>See MI455X for the absolute cutting edge and MI350P for something you can use outside of a datacenter.
8 of those cards give you 1TB of ram and 4.4TB/s