Generative AI system

1 . A method for running a multimodal generative artificial intelligence (AI) model, the method comprising:

transforming model parameters of a trained multimodal generative AI network from floating-point precision to selected bit-depth representations including 16-bit, 8-bit or 4-bit integers, wherein quantization preserves semantic features across text, image, video, audio, or sensor modalities to reduce memory footprint and computational complexity;

packaging quantized model parameters and a network architecture into a pruned deployment bundle;

retraining the pruned model and updating the relevance threshold by continuously collecting telemetry including layer-level flop counts, parameter sparsity patterns, communication volumes, and end-to-end iteration latency, supplying said telemetry to a learning-based or heuristic optimizer to maintain model sparsity and accuracy over successive pruning iterations;

transmitting said bundle to one or more edge devices, wherein model compatibility and runtime configuration for heterogeneous device hardware are validated prior to installation; and

conducting AI inference operations on deployed edge devices with the bundle having modular neural network layers, on-device caching of intermediate results, and batched or streaming inference, wherein inference on multimodal inputs is completed without exceeding predetermined device memory or compute constraints while maintaining generative accuracy.

2 . The method of claim 1 , comprising enhancing data quality and consistency by:

receiving a dataset comprising inputs from two or more modalities, including at least text, image, audio, or video data, and applying modality-specific preprocessing operations selected from normalization, resizing of images or videos, tokenization, and spectral filtering to convert each raw input into a machine-processable format;

analyzing the preprocessed data for statistical outliers and noise artifacts by calculating summary statistics for each modality, applying denoising algorithms and removing or correcting data samples that fall outside modality-specific quality thresholds or contain missing, corrupted, or anomalous features; and

evaluating each modality's cleaned input data against a schema tailored to the expected format and distribution for the modality, such that validation rules enforce conformity to required structures); and, excluding data points that fail schema validation to ensure only compliant samples are propagated to downstream training routines.

3 . The method of claim 1 , comprising reducing memory-efficient architecture transformer layers size less than a floating-point precision transformer layers size.

4 . The method of claim 1 , further comprising distilling the model from the deployment bundle prior to deployment.

5 . The method of claim 1 , wherein the AI inference operations are performed asynchronously.

6 . The method of claim 1 , comprising quantizing weights and activations.

7 . The method of claim 1 , further comprising caching inputs.

8 . The method of claim 1 , wherein bundle compression is performed using principal component analysis.

9 . The method of claim 1 , further comprising fallback to cloud-based inference if edge capacity is exceeded.

10 . The method of claim 1 , comprising setting quantization parameters based on modality type.

11 . The method of claim 1 , comprising distilling knowledge from a teacher model into the memory-efficient architecture.

12 . The method of claim 1 , further comprising dynamic loading of model modules based on task.

13 . The method of claim 1 , comprising:

evaluating the importance of model parameters using a relevance metric selected from parameter magnitude, activation statistics, or contribution to output accuracy, and removing or zeroing model parameters falling below a predefined relevance threshold during or after a training process, wherein a pruning comprises both structured pruning by removing neurons, filters, or layers and unstructured pruning by removing individual weights.

14 . The method of claim 1 , comprising segmenting a neural network model computation into multiple partitions, each partition assigned to a distinct compute node within a distributed computing environment, wherein the segmentation is performed based on layer structure, data modality, or computational demand.

15 . A method, comprising:

segmenting a neural network model computation into multiple partitions, each partition assigned to a distinct compute node within a distributed computing environment, wherein the segmentation is performed based on layer structure, data modality, or computational demand;

monitoring real-time workload characteristics at each compute node, and allocating computational resources including processor cores, memory, and network bandwidth to each compute node in response to detected changes in training workload during model training for optimizing throughput and resource utilization;

evaluating the importance of model parameters using a relevance metric selected from parameter magnitude, activation statistics, or contribution to output accuracy, and removing or zeroing model parameters falling below a predefined relevance threshold during or after a training process, wherein a pruning comprises both structured pruning by removing neurons, filters, or layers and unstructured pruning by removing individual weights; and

retraining the pruned model for a plurality of epochs to recover performance degraded by parameter removal, and updating the relevance threshold in subsequent pruning iterations to balance model sparsity and accuracy, further comprising continuously collecting telemetry including layer-level flop counts, parameter sparsity patterns, communication volumes, and end-to-end iteration latency, supplying said telemetry to a learning-based or heuristic optimizer, and pruning schedules to maintain model sparsity and accuracy over successive pruning iterations.

16 . The method of claim 15 , wherein segmenting the neural network model computation into multiple partitions comprises partitioning a model graph into subgraphs that are mapped to individual compute nodes or groups of nodes using layer-wise partitioning, operator-wise partitioning, or tensor-slicing, and assigning memory-bound operators to nodes with larger memory capacity and compute-bound operators to nodes with greater accelerator capability so as to balance load and minimize cross-node data transfer within the distributed computing environment.

17 . The method of claim 15 , wherein monitoring real-time workload characteristics at each compute node and allocating computational resources includes tracking per-node metrics comprising compute utilization, memory usage, queue lengths, and network traffic using a runtime monitor, and adjusting autoscaling policies to instantiate or retire nodes, migrate tasks, or rescale resource shares based on queue depth, latency targets, or cost constraints while maintaining stable operation of the distributed training workload.

18 . The method of claim 15 , wherein allocating computational resources further comprises coordinating with an orchestration layer configured to allocate GPUs and host resources via a cluster scheduler, place workloads on heterogeneous GPU types selected according to tensor-core availability or memory capacity, and apply backpressure, deadline-aware admission control, and task prioritization so that latency targets and throughput objectives for the model training are satisfied.

19 . The method of claim 15 , wherein evaluating the importance of model parameters using the relevance metric and removing or zeroing parameters falling below the predefined relevance threshold is performed in pruning rounds scheduled after a burn-in training phase, each pruning round followed by fine-tuning the model for a plurality of epochs to recover accuracy, and wherein the relevance threshold is progressively tightened or determined via automated hyperparameter search to trade off model size, throughput, and accuracy.

20 . The method of claim 15 , wherein the pruning comprises representing pruned parameters in a sparse format, encoding the sparse parameters using compressed representations, and communicating sparsified tensors across compute nodes using sparse all-reduce protocols so as to reduce memory footprint, network bandwidth consumption, and inter-node transfer overhead in the distributed computing environment.