Buy ASRock B70 Intel Arc Pro B70 B70 CT 32GB 256-bit GDDR6 PCI Express 5.0 x16 2-slot Graphics Card with fast shipping and top-rated customer service. Once you know, you Newegg!
Product SKU:
N82E16814930149
ai_features:
AI Accelerated
auxiliary_power_connector:
12V/26-pin, Form Factor & Dimensions
brand:
ASRock
chipset_manufacturer:
Intel
cooler:
Blower Design
core_clock:
2540 MHz, Memory
digital_resolution:
7680 x 4320
dimensions_l_x_h:
271 x 112 x 39 mm, , Additional Information
directx:
DirectX 12 Ultimate, Ports
displayport:
4, Details
first_listed_on_newegg:
March 25, 2026
form_factor:
2-slot
gpu:
Intel Arc Pro B70
hdcp_ready:
Yes
interface:
PCI Express 5.0 x16, Chipset
memory_clock:
19 Gbps
memory_interface:
256-bit
memory_size:
32GB
memory_type:
GDDR6, 3D API
model:
B70 CT
part_number:
B70 CT, Interface
series:
B70
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Quote
from EthanH5438
:
Is it closer to a 3090?
In gaming performance, probably not. This is targeted as a Workstation/LLM Interencing Graphics card an on that regards, it is better than most cards at this price bracket.
In gaming performance, probably not. This is targeted as a Workstation/LLM Interencing Graphics card an on that regards, it is better than most cards at this price bracket.
Out of curiosity, what type of scale would you need where moving to a co-located or cloud (AWS, Azure, Oracle, etc) is cheaper than on-prem systems for LLMs?
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Quote
from cheshirecat
:
Does it support tensor parallelism? Full Linux support? (B60 was weirdly favoring Windows)
Yes it does, and especially excels in AI Inferencing. Check this Video: - https://www.youtube.com/watch?v=DTJr2msyqGY&t=2s
You can literally stack multiple of this GPU in a Server and they can work together to handle multiple prompts coming from users. And Linux Support is full. However, keep in mind, Intel has been updating their stack regularly but it is still far behind CUDA and even AMD ROCm. The model supports are not the lastest one most of the times, you need to work with slightly older version which is compatible with Intel Software stack.
Out of curiosity, what type of scale would you need where moving to a co-located or cloud (AWS, Azure, Oracle, etc) is cheaper than on-prem systems for LLMs?
I'm no expert on this but if you can clarify your ask in a bit details then I can try to answer.
Correct me if im wrong, but NVIDIA is still much better and easier for all Local model stuff right?
Last I checked, there's a reason Nvidia cards cost so much more at the moment.
I don't know about 'easier', but this card is supposed to be equal performance of nVidia cards that cost twice as much. So if reports are true, this is 1/2 the price vs Nvidia.
Correct me if im wrong, but NVIDIA is still much better and easier for all Local model stuff right?
Last I checked, there's a reason Nvidia cards cost so much more at the moment.
NVIDIA has much more community support.
Think of it like right vs left-handed. Neither is inherently better than the other. But, since most people are right-handed, a lot of things are designed specifically for right-handed people. Aside from specialized cases like baseball or feeling pride in being unique, life is easier if your are right handed.
NVIDIA has a much higher percentage of the GPU market. A lot more people are experts with NVIDIA hardware and software than AMD or Intel. This means more software is specifically designed around NVIDIA hardware. And since there is more software support for NVIDIA hardware, NVIDIA hardware becomes more valuable.
Is NVIDIA hardware actually more advanced or more capable? I'd guess "no". Or at least, it isn't a clear "yes". Not because I know a lot about GPUs. Just because it normally happens that when a company gets that much of a lead in the market. They tend to get much more complacent.
I'm no expert on this but if you can clarify your ask in a bit details then I can try to answer.
Probably too complex of a question of this forum but if someone is training LLMs where is the inflection point when it's cheaper to train your LLM in the cloud (using AWS, etc.) vs buying your own equipment to train the LLM?
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Probably too complex of a question of this forum but if someone is training LLMs where is the inflection point when it's cheaper to train your LLM in the cloud (using AWS, etc.) vs buying your own equipment to train the LLM?
What you mean is fine tuning your LLM, by chooisng a pretrained model and then fine tuning it with your data and thatis always going to be cheaper locally but you will be lomited by what you have. 32GB Video memory allows this Graphics Card to use pretty big models , 30B with 8 bit Integer. However, fine tuning needs much higher VAM for same model. Using LoRA / QLoRA Fine-Tuning, you can easily train 13B parameter models with 32GB memory. You can even go higher, nearly 30B but in that case, you need to use 4 Bit Quantization and lot of optimization techniques.
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Our community has rated this post as helpful. If you agree, why not thank Suryasis
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https://www.youtube.com/watch?v=DTJr2ms
You can literally stack multiple of this GPU in a Server and they can work together to handle multiple prompts coming from users. And Linux Support is full. However, keep in mind, Intel has been updating their stack regularly but it is still far behind CUDA and even AMD ROCm. The model supports are not the lastest one most of the times, you need to work with slightly older version which is compatible with Intel Software stack.
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Last I checked, there's a reason Nvidia cards cost so much more at the moment.
Last I checked, there's a reason Nvidia cards cost so much more at the moment.
Last I checked, there's a reason Nvidia cards cost so much more at the moment.
Think of it like right vs left-handed. Neither is inherently better than the other. But, since most people are right-handed, a lot of things are designed specifically for right-handed people. Aside from specialized cases like baseball or feeling pride in being unique, life is easier if your are right handed.
NVIDIA has a much higher percentage of the GPU market. A lot more people are experts with NVIDIA hardware and software than AMD or Intel. This means more software is specifically designed around NVIDIA hardware. And since there is more software support for NVIDIA hardware, NVIDIA hardware becomes more valuable.
Is NVIDIA hardware actually more advanced or more capable? I'd guess "no". Or at least, it isn't a clear "yes". Not because I know a lot about GPUs. Just because it normally happens that when a company gets that much of a lead in the market. They tend to get much more complacent.
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Join The Conversation
Share your experience with the Slickdeals community
Share information with the community. Please follow our Community Guidelines and be kind!