Are there clear data on the differences in resource requirements (such as peak GPU memory usage, number of CPU cores needed, memory IO frequency) during deployment for different AI models (such as deep learning models / traditional machine learning models, image-based / NLP models)? What are the specific ratios of resource "surplus" to "deficit": for example, is there detailed data showing that the average GPU utilisation for a certain type of model is only 30% (surplus), while another type of model experiences deployment delays exceeding expectations by 200% due to insufficient GPUs (deficit)? Are there fluctuations over time: is there a situation where "peak periods (such as simultaneous deployment of multiple models) experience more severe resource competition, while off-peak periods have a higher rate of resource idleness"?
yes its train and test possible after quantify the data , if data is real instance which make another prediction to do and so ..
Deep models (especially LLMs) are dominated by GPU memory footprint and memory bandwidth constraints. Large language models often require tens to hundreds of GB of GPU memory, which makes them memory-bound at inference and forces special serving strategies (model sharding, offloading, quantization). This is shown repeatedly in recent LLM inference performance papers
Dear Evan Tsang ,
The core concept of unequal resource distribution refers to the uneven allocation and access to essential elements necessary for human well-beingMeaning → Well-being encompasses holistic health and happiness, supported by sustainable lifestyle choices that benefit both individuals and the environment. and societal functioning. At its most basic level, this describes situations where certain individuals, communities, or nations possess significantly more of a particular resource than others. This disparity manifests across various critical domains, including but not limited to clean water, nutritious food, habitable land, educational opportunities, healthcare services, and financial capital..
https://pollution.sustainability-directory.com/term/unequal-resource-distribution/
Regards,
Shafagat
SolutionsOptimising model architectures and algorithms: Select more efficient model architectures, reduce the number of model parameters, and decrease resource demands. For example, using quantisation techniques, such as 4-bit quantisation, can reduce memory requirements by 50%-75%.Dynamic resource scheduling: Use containerisation technologies and orchestration tools, such as Docker and K8s, to dynamically allocate resources based on business load, improving resource utilisation.Hardware upgrades and optimisation: Choose appropriate hardware devices, such as GPUs with high memory bandwidth, according to the resource demands of the models to enhance computational performance.Inference engine optimisation: Select appropriate inference engines, such as TensorRT or ONNX Runtime, to optimise models and improve inference speed.
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