These are going to be so awesome to buy in 5-8 years!
Right now I’m running 2x 3090 and 128 gb ram on 3.8 flash-next180b (the quant is 97gb) and it hums at120t/s. It’s really a surprisingly good model for general tasks, and I’m soon to see how it does with more agentic kit. For agentic work it does slow down quite a bit when the context fills up… but what do you want for running a 180b model on consumer hardware?
Right now I have it set up as an administrator for our asterisk PBX and radius server, and it does higher level judgment on our cctv footage for notifications to security. It’s been really good so far. Agents are the new UI, everything just works over SSH now.
> (...) it hums at120t/s. It’s really a surprisingly good model for general tasks, and I’m soon to see how it does with more agentic kit.
This is exactly why I interpret the aggressive price cutting by OpenAI on their Luna model line as a clear sign they are scared shitless of local models, and how they can trigger a "the king has no clothes" moment for AI companies like OpenAI.
> as a clear sign they are scared shitless of local models,
The number of people with hardware to run a model like that is very small.
It's going to be many years before hardware to run medium size local models is widely available. Running mid-size local models doesn't even register as a factor in pricing decisions right now.
It's such a small part of the market and will be for many years, until RAM and other hardware prices come down.
> The number of people with hardware to run a model like that is very small.
True, but the number of people who pay the non-subsidized API price is also very small, and it's all in big companies who would have no issue buying or renting hardware for that purpose.
> The number of people with hardware to run a model like that is very small.
That's not the problem anyone is facing, and analysis anyone is doing. At all.
The problem is the huge AI-related budget that companies who dove head first onto AI are facing.
Therefore the problem everyone is faced with boils down to whether they keep paying the likes of OpenAI a de-facto business tax, or they replace that tax with a one-off CapEx invoice followed by a utilities bill.
And you don't need a frontier model to do most things. In fact, the biggest challenge in using local models in agentic coding is the models supporting these agent-based tools.
> It's going to be many years before hardware to run medium size local models is widely available.
Nonsense. Speaking for myself, my employer already introduced a hard cap on AI credits while rolling out hardware support for local models, and even went to the extents of rolling out workstations with 64GB of RAM as a baseline. Consequently, we are using the likes of gemma4 for a fair share of everyday tasks. This has a transformative effect on your workflows because suddenly you don't ration tokens, and agent orchestration doesn't become a cash guzzler. So far the only constraints are tool support and token throughput, but that's not a concern.
I'm working off a 128gb Strix system on mostly hydro and it's worth it for me. Qwen 3.8 27B and Flash Next, Gemma 4 and MOE models. It Keeps my thinking and methods local while moving at a speed I can actually read and think about.
It can be annoying when you're running something long and realize there is an issue. But hey, I'm not wasting paid tokens. Reset and try again. I've probably thought of a better way by then too.
The large amount of memory means you can serve larger models, or less quantized, but that also means they're going to be slower due to the low bandwidth.
Its bandwidth is around that of 32 DDR5 DIMMS (50ish GB/s). These boards should have built in NICs but they don't because Intel also wants to sell more servers.
The marketing department doesn't have to lift a finger in this market. They could quietly drop it for sale without a single announcement and it would be backordered for a year.
You can tell by the shape of the board that it's meant to go into a server chassis behind a stack of 80mm blower fans. It's not meant for consumers, but you will be able to buy it used for $200 off ebay in 7 years to put in your home lab.
Wonder if there will be consumer video drivers for these GPUs once the AI hardware shortage is over. Hope to buy a gaming rig one day that doesn't cost a fortune.
Intel ai gpu division is so aggressively anti “small” consumer. I think it has something to do with low volume and a small sales/distribution network. The data center cards I have from amd and nvidia are grey market at best. I don’t think intel moved enough units to establish alternate channels.
What I don't understand is: when you only plan for your product to have 10 customers, why even make a marketing page like this for it? At that point it's essentially the hardware equivalent of consultingware, something you sell to your existing contacts through outbound calls and by shoving ads through the B2B equivalent of a post-consumer sales channel (i.e. proactive outreach by your org's internal champion for that customer, to said customer.)
1.5 TB/s 1.3 PFLOPS with no fast multi-GPU, it's a consumer card that pretends to be a server card.
It would work great on Qwen3.8-Flash-Next or GLM-5.3 as single user decode only hits a few experts, anything with concurrent users and you'd be sitting for a while.
That's the point I believe fast local 2-4 user service for cheap on very large models.
You have to pretend it's for servers to keep investors happy, it would be banks and similar with in house servers that have to run local large models buying these for experiments and cybersecurity. Likely.
The cost of a 480gb ram video card seems preposterous to be even marketing at this point.
Intel is going to make a card that costs $1 million dollars.. this is like industrial newsletters bleeding into a public forum
> Commercial Scale Pricing: In the industrial data center sector, battery storage systems are priced per kilowatt-hour (kWh) or megawatt-hour (MWh). Industrial battery storage installations typically cost $300,000 to $450,000+ per MWh of capacity depending on software integration, cooling systems, and structural framing.
Slop and placeholder text on the page, including this one. They should have reviewed their webpage.
"What software frameworks are relevant to Intel GPU D270?
Intel GPU D270 messaging should reference familiar AI software and serving frameworks such as PyTorch, vLLM, SGLang, and OpenAI Triton, where approved. This helps developers and platform teams understand how the product fits into existing AI workflows."
This is a brand new class of GPU that I am excited for, especially because it's rated for air cooling. I hope to see other manufacturers release GPU's with multiple of 100 GB's that can be utilized even in an at-home system's with PCIe 5 support. I probably can't afford this but knowing the engineering is sound at this scale means a similar accelerator maybe attainable.
Looks like Intel is choosing to title its new crescent island cards with the D prefix despite being Crescent Island.
Kind of disappointed that Intel is (probably) not going to release consumer class GPUs. Intel's driver stack was seriously getting mature—both on Linux and Windows. It's always nice to have a third option available.
Agreed. I was really rooting for them to deliver that third iteration to consumers, and retain them as a competitive option in the low to mid range. Pretty sad to see.
"Both" requires more engineering staff on payroll, more wafers to fab/dice/package/test, more boards to manufacture, more inventory to manage, among other things. If their math says it isn't worth it (granted that they don't always get it right), then they don't do it.
> "Both" requires more engineering staff on payroll, more wafers to fab/dice/package/test, more boards to manufacture, more inventory to manage, among other things.
Aren't you describing how a business operates?
I mean, doesn't Intel already hire people for those tasks? Unless you are worried about a specific bottleneck, increasing headcount and production output is what businesses do.
For now. A limited run of Celestials until 2028 when Serpent Lake switches to Nvidia tiles, and a bit longer for the LTS combo (AMD style) IO+GPU die in lower end/embedded products.
Druid is still dead, consumer DGPUs are still, and workstation DGPUs also seems to be dead, and most of the Xe driver team for Windows was laid off, as well as most of the Xe chip design team.
They originally said it was going to be 120GB but that partners could ship bigger capacities. So much for that!
I do hope this has a price point that makes it somewhat competitive with a Mac! Consumers have had nearly no choices available for mem capacity, and 20 channels of lpddr5 is one way to get that capacity! Go Intel!
Both this GPU and the Mac are constrained by a lack of GPU compute. There's basically no rack-scale GPUs with 480gb on-die that have a sub-400W compute element. It's going to be incredibly prefill-constrained on denser model inference just like the Apple Silicon iGPUs are.
> But I am surprised that they were able to pack so much flops into 300W.
I'm not. When you're doing inference you typically reduce power limits anyway if you're optimizing for efficiency. Even if you don't, it doesn't spike to the max of the card.
My 3090 I power it down to 175W or so for single-digit reductions in performance for like, 60% of the wattage, something like that.
> Efficient token serving
Dense, air-cooled, standard PCIe infrastructure helps maximize tokens per rack and watt, scaling serving capacity and keeping costs in check.
Next the tokens will be asking for tequila and caviar. Bow down to the new token overloads.
Right now I’m running 2x 3090 and 128 gb ram on 3.8 flash-next180b (the quant is 97gb) and it hums at120t/s. It’s really a surprisingly good model for general tasks, and I’m soon to see how it does with more agentic kit. For agentic work it does slow down quite a bit when the context fills up… but what do you want for running a 180b model on consumer hardware?
Right now I have it set up as an administrator for our asterisk PBX and radius server, and it does higher level judgment on our cctv footage for notifications to security. It’s been really good so far. Agents are the new UI, everything just works over SSH now.
This is exactly why I interpret the aggressive price cutting by OpenAI on their Luna model line as a clear sign they are scared shitless of local models, and how they can trigger a "the king has no clothes" moment for AI companies like OpenAI.
The number of people with hardware to run a model like that is very small.
It's going to be many years before hardware to run medium size local models is widely available. Running mid-size local models doesn't even register as a factor in pricing decisions right now.
It's such a small part of the market and will be for many years, until RAM and other hardware prices come down.
True, but the number of people who pay the non-subsidized API price is also very small, and it's all in big companies who would have no issue buying or renting hardware for that purpose.
That's not the problem anyone is facing, and analysis anyone is doing. At all.
The problem is the huge AI-related budget that companies who dove head first onto AI are facing.
Therefore the problem everyone is faced with boils down to whether they keep paying the likes of OpenAI a de-facto business tax, or they replace that tax with a one-off CapEx invoice followed by a utilities bill.
And you don't need a frontier model to do most things. In fact, the biggest challenge in using local models in agentic coding is the models supporting these agent-based tools.
> It's going to be many years before hardware to run medium size local models is widely available.
Nonsense. Speaking for myself, my employer already introduced a hard cap on AI credits while rolling out hardware support for local models, and even went to the extents of rolling out workstations with 64GB of RAM as a baseline. Consequently, we are using the likes of gemma4 for a fair share of everyday tasks. This has a transformative effect on your workflows because suddenly you don't ration tokens, and agent orchestration doesn't become a cash guzzler. So far the only constraints are tool support and token throughput, but that's not a concern.
Your electrical costs are likely less than mine though
It can be annoying when you're running something long and realize there is an issue. But hey, I'm not wasting paid tokens. Reset and try again. I've probably thought of a better way by then too.
Literally my first thought!
Marketing department asleep at the wheel.
A single B300 has almost 8 TB/s.
AMD MI300x from 2023 is 5.3 TB/s
Edit: my bad you are saying that this is an impressive number precisely because it is more or less normal RAM, not VRAM
So yeah on its own not very impressive.
The large amount of memory means you can serve larger models, or less quantized, but that also means they're going to be slower due to the low bandwidth.
https://www.techpowerup.com/gpu-specs/geforce-rtx-5090.c4216
The marketing department doesn't have to lift a finger in this market. They could quietly drop it for sale without a single announcement and it would be backordered for a year.
Not targeting me
No. Certainly not now, maybe not ever. That is the least consumer-focused product page I've seen today.
You can work with 10 players that will buy all of them with minimal effort.
It would work great on Qwen3.8-Flash-Next or GLM-5.3 as single user decode only hits a few experts, anything with concurrent users and you'd be sitting for a while.
You have to pretend it's for servers to keep investors happy, it would be banks and similar with in house servers that have to run local large models buying these for experiments and cybersecurity. Likely.
The cost of a 480gb ram video card seems preposterous to be even marketing at this point.
Intel is going to make a card that costs $1 million dollars.. this is like industrial newsletters bleeding into a public forum
> Commercial Scale Pricing: In the industrial data center sector, battery storage systems are priced per kilowatt-hour (kWh) or megawatt-hour (MWh). Industrial battery storage installations typically cost $300,000 to $450,000+ per MWh of capacity depending on software integration, cooling systems, and structural framing.
Let's all get excited
"What software frameworks are relevant to Intel GPU D270?
Intel GPU D270 messaging should reference familiar AI software and serving frameworks such as PyTorch, vLLM, SGLang, and OpenAI Triton, where approved. This helps developers and platform teams understand how the product fits into existing AI workflows."
Kind of disappointed that Intel is (probably) not going to release consumer class GPUs. Intel's driver stack was seriously getting mature—both on Linux and Windows. It's always nice to have a third option available.
Theres so much money to be made
You can make a nice profit off of the enterprise space, but the big winners are the guys who put the work into servicing the consumer market.
Aren't you describing how a business operates?
I mean, doesn't Intel already hire people for those tasks? Unless you are worried about a specific bottleneck, increasing headcount and production output is what businesses do.
Druid is still dead, consumer DGPUs are still, and workstation DGPUs also seems to be dead, and most of the Xe driver team for Windows was laid off, as well as most of the Xe chip design team.
I do hope this has a price point that makes it somewhat competitive with a Mac! Consumers have had nearly no choices available for mem capacity, and 20 channels of lpddr5 is one way to get that capacity! Go Intel!
This is almost twice better than RTX 5090. This GPU will not be prefill constrained, I feel.
But I am surprised that they were able to pack so much flops into 300W.
I'm not. When you're doing inference you typically reduce power limits anyway if you're optimizing for efficiency. Even if you don't, it doesn't spike to the max of the card.
My 3090 I power it down to 175W or so for single-digit reductions in performance for like, 60% of the wattage, something like that.
Next the tokens will be asking for tequila and caviar. Bow down to the new token overloads.