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https://habana.ai/
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Habana Labs was founded in 2016 to create world-class AI Processors, developed from the ground-up and optimized for training deep neural networks and for inference deployment in production environments.
jeffboudier 
posted an update 4 days ago
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286
AMD summer hackathons are here!
A chance to get hands-on with MI300X GPUs and accelerate models.
🇫🇷 Paris - Station F - July 5-6
🇮🇳 Mumbai - July 12-13
🇮🇳 Bengaluru - July 19-20

Hugging Face and GPU Mode will be on site and on July 6 in Paris @ror will share lessons learned while building new kernels to accelerate Llama 3.1 405B on ROCm

Register to Paris event: https://lu.ma/fmvdjmur?tk=KeAbiP
All dates: https://lu.ma/calendar/cal-3sxhD5FdxWsMDIz
jeffboudier 
posted an update 17 days ago
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1641
Today we launched Training Cluster as a Service, to make the new DGX Cloud Lepton supercloud easily accessible to AI researchers.

Hugging Face will collaborate with NVIDIA to provision and set up GPU training clusters to make them available for the duration of training runs.

Hugging Face organizations can sign up here: https://huggingface.co/training-cluster
jeffboudier 
posted an update about 1 month ago
jeffboudier 
posted an update about 1 month ago
regisss 
posted an update about 1 month ago
jeffboudier 
posted an update about 2 months ago
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2589
Transcribing 1 hour of audio for less than $0.01 🤯

@mfuntowicz cooked with 8x faster Whisper speech recognition - whisper-large-v3-turbo transcribes at 100x real time on a $0.80/hr L4 GPU!

How they did it: https://huggingface.co/blog/fast-whisper-endpoints

1-click deploy with HF Inference Endpoints: https://endpoints.huggingface.co/new?repository=openai%2Fwhisper-large-v3-turbo&vendor=aws&region=us-east&accelerator=gpu&instance_id=aws-us-east-1-nvidia-l4-x1&task=automatic-speech-recognition&no_suggested_compute=true
jeffboudier 
posted an update about 2 months ago
jeffboudier 
posted an update 3 months ago
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2208
Llama4 is out and Scout is already on the Dell Enterprise Hub to deploy on Dell systems 👉 dell.huggingface.co
jeffboudier 
posted an update 3 months ago
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1572
Enterprise orgs now enable serverless Inference Providers for all members
- includes $2 free usage per org member (e.g. an Enterprise org with 1,000 members share $2,000 free credit each month)
- admins can set a monthly spend limit for the entire org
- works today with Together, fal, Novita, Cerebras and HF Inference.

Here's the doc to bill Inference Providers usage to your org: https://huggingface.co/docs/inference-providers/pricing#organization-billing
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regisss 
posted an update 4 months ago
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1758
Nice paper comparing the fp8 inference efficiency of Nvidia H100 and Intel Gaudi2: An Investigation of FP8 Across Accelerators for LLM Inference (2502.01070)

The conclusion is interesting: "Our findings highlight that the Gaudi 2, by leveraging FP8, achieves higher throughput-to-power efficiency during LLM inference"

One aspect of AI hardware accelerators that is often overlooked is how they consume less energy than GPUs. It's nice to see researchers starting carrying out experiments to measure this!

Gaudi3 results soon...
regisss 
in Habana/mamba 5 months ago

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#3 opened 5 months ago by
zzhang37
jeffboudier 
posted an update 6 months ago
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752
NVIDIA just announced the Cosmos World Foundation Models, available on the Hub: nvidia/cosmos-6751e884dc10e013a0a0d8e6

Cosmos is a family of pre-trained models purpose-built for generating physics-aware videos and world states to advance physical AI development.
The release includes Tokenizers nvidia/cosmos-tokenizer-672b93023add81b66a8ff8e6

Learn more in this great community article by @mingyuliutw and @PranjaliJoshi https://huggingface.co/blog/mingyuliutw/nvidia-cosmos
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regisss 
posted an update 6 months ago
regisss 
in Habana/mamba 7 months ago

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#2 opened 7 months ago by
zzhang37

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#1 opened 7 months ago by
zzhang37
regisss 
updated a model 7 months ago
jeffboudier 
posted an update 7 months ago
regisss 
posted an update 8 months ago
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1426
Interested in performing inference with an ONNX model?⚡️

The Optimum docs about model inference with ONNX Runtime is now much clearer and simpler!

You want to deploy your favorite model on the hub but you don't know how to export it to the ONNX format? You can do it in one line of code as follows:
from optimum.onnxruntime import ORTModelForSequenceClassification

# Load the model from the hub and export it to the ONNX format
model_id = "distilbert-base-uncased-finetuned-sst-2-english"
model = ORTModelForSequenceClassification.from_pretrained(model_id, export=True)

Check out the whole guide 👉 https://huggingface.co/docs/optimum/onnxruntime/usage_guides/models
jeffboudier 
posted an update 9 months ago
jeffboudier 
posted an update 9 months ago
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475
Inference Endpoints got a bunch of cool updates yesterday, this is my top 3