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A Sparse Summary Introducing Sparse Llama 3.1 8B Large language models (LLMs) are approaching their limits in terms of traditional scaling, with billions of parameters added for relatively small accuracy gains and advanced quantization techniques squeezing out the last possible bits before accuracy plummets. These dense architectures remain large, costly, and resource-intensive, making it challenging… Read More 2:4 Sparse Llama: Smaller Models for Efficient GPU Inference
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70% Sparsity, Full Accuracy Recovery—Unlock the power of smaller, faster LLMs with our latest foundational research, enabling up to 8.6X faster and cheaper deployments AI at a Cost: The Efficiency Challenge Large language models (LLMs) drive unprecedented innovation across content creation, customer support, content summarization, and more. Yet this powerful technology comes with significant costs—even… Read More Unlock Accurate, Affordable, and Sustainable LLMs by Removing Billions of Parameters
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Key Takeaways In the rapidly evolving landscape of large language model (LLM) inference, the quest for speed and efficiency on modern GPUs has become a critical challenge. Enter Marlin, a groundbreaking Mixed Auto-Regressive Linear kernel that unlocks unprecedented performance for FP16xINT4 matrix multiplications. Developed by Elias Frantar at IST-DASLab and named after one of the… Read More Pushing the Boundaries of Mixed-Precision LLM Inference With Marlin
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Key Takeaways This year has been an exceptionally exciting year for open-source large language models (LLMs). Just 11 months ago proprietary models, like GPT-3, were the only reasonable choice for companies to build generative AI applications. Now, there is a thriving ecosystem of high-quality open-source models, like Meta’s Llama family. In February, Meta released the… Read More Fast Llama 2 on CPUs With Sparse Fine-Tuning and DeepSparse
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The arrival of capable open-source large language models (LLMs) like MosaicML’s MPT and Meta’s Llama 2 has made it easier for enterprises to explore generative AI to address their business challenges. Yet, adoption of open-source models for commercial applications is still hampered by two key problems: In our recent paper, Sparse Fine-Tuning for Inference Acceleration… Read More Sparse Fine-Tuning for Accelerating Large Language Models with DeepSparse
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Neural Magic has added support for large language models (LLMs) in DeepSparse, enabling inference speed-ups from compression techniques like SparseGPT on commodity CPUs. SparseGPT: Prune and Quantize LLMs Quickly With One-Shot State-of-the art language models are very large with parameter counts in the billions. To deploy one is expensive and often requires multiple GPUs just… Read More Speed up your LLMs with SparseGPT and DeepSparse on CPUs
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This blog, originally posted in December 2022, has been edited in May 2023 to reflect updates made to the "Batch Deployment Flow" section and GitHub repo links. Leveraging the advantages of serverless computing, developers can deploy and manage AI-driven applications with unprecedented efficiency, scalability, and cost-effectiveness. With serverless deployments, machine learning inference can execute two… Read More Deploy Serverless Machine Learning Inference on AWS with DeepSparse
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Large language models (LLMs) solve natural language processing problems with astounding accuracy. However, these models are enormous and require a lot of space, cost, and computation power to deploy. For example, the GPT-175B model has 175 billion parameters requiring 320GB of storage and at least 5 A100 GPUs with 80GB of memory each for inference.… Read More SparseGPT: Remove 100 Billion Parameters for Free