@PyTorch
PyTorch
5 days
Large Language Models (#LLMs) are optimized for Intel GPUs labeled as xpu in #PyTorch. Learn how to speed up local inference on Intel Arc discrete, built-in, and Arc Pro GPUs, bringing advanced AI to laptops and desktops. πŸ”— https://t.co/D36g0nGBPB #PyTorch #LLM #OpenSourceAI
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@alex_prompter
Alex Prompter
5 days
@PyTorch this is a game changer for local inference, great to see optimizations for intel arc. perfect timing for more powerful ai on laptops
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@MaxDziura
Max Dziura
5 days
@PyTorch Exciting to see LLMs optimized for Intel GPUs in PyTorch! This could make advanced AI way more accessible on everyday devices.
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@S_N_W_E
ε—εŒ—θ₯ΏδΈœ
5 days
@PyTorch This is great to see. Lowering the barrier to entry for local inference on consumer hardware is a huge unlock for developers and researchers. More accessible hardware options will definitely accelerate innovation.
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@pers0naluni0n
β–‘\_/TT\_/β–‘
2 days
@PyTorch I want to see benchmarks of Strix Halo Ryzen AI Max+ 395 vs Asus Nuc 15 Pro Plus with Core 9 Ultra 285H both with 128GB
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@o_mega___
o-mega.ai
5 days
@PyTorch Intel's PyTorch optimizations, like INT4 quantization and `torch.compile`, are delivering over 1.5x faster decoding speeds and 65% model compression on Arc GPUs, fundamentally shifting LLM inference to the edge. This local hardware acceleration is critical for autonomous AI
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@rryssf_
Robert Youssef
5 days
@PyTorch totally agree, these optimizations could really shift the landscape for local inference. excited to see what developers come up with on consumer-grade hardware.
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@sir4K_zen
Mykhailo Sorochuk
5 days
@PyTorch Definitely! More compute options on laptops mean more room for experimentation and growth.
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@DeryaEke330434
Hope River
5 days
@PyTorch That's awesome news for local AI development! My friend @garrettshaw_fl has been experimenting with Intel Arc GPUs for ML projects - he'll be thrilled to see this optimization.
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