a100

Using the famous cnn model in Pytorch, we run benchmarks on various gpu.

Remove Backgroud from a video 🧤🎥

A kubernetes operator for managing nvidia MIG instances.

DeepSeek-V4.1-Flash inference on NVIDIA Ampere with vLLM, SM80 patches, reproducible throughput and coding-agent benchmarks. Tested on 8x A800 80GB.

LLM benchmarking, GPU workload orchestration backend server | Deepseek-R1, Qwen2.5, Llama3.1 | 4xRTX-5090 inside PRU2500, 2xH100 inside PRU2500, 8xMI210 in SuperMicro

Dataset and code for "Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning"

ClusterOps is an enterprise-grade Python library developed and maintained by the Swarms Team to help you manage and execute agents on specific CPUs and GPUs across clusters. This tool enables advanced CPU and GPU selection, dynamic task allocation, and resource monitoring, making it ideal for high-performance distributed computing environments.

Offline DeepSeek-V4.1-Flash deployment for 8x A100: DSpark k5, 256K context, local APIs and measured benchmarks.

CLI tool to check Oracle Cloud compute shape availability - find capacity across regions

DeepSeek-V4-Flash-Vision-Exp multimodal serving on 4× CMP 170HX (unlocked GA100, sm_80, PP4, 512k ctx, DSpark)

Faithful from-scratch reimplementation of DeepSeek-V3 (MLA + aux-loss-free MoE + MTP + speculative decoding), ~412M params, Chinchilla-optimal 8.4B-token training on a single A100 80GB.

Transformer implementation in pytorch trained on NVIDIA A100 in fp16

Reference Flash Attention implementation in PyTorch with V1/V2, GQA/MQA, Triton kernels, benchmark and docs.

GPU profiling suite & CUDA kernels on A100 80GB — ResNet-50 benchmarks, Nsight Systems profiling, tiled matrix multiplication with shared memory

Read-only first-pass NVIDIA GPU evidence scan.

[2024Fall CSE291-DSC253 Advanced Data-Driven Text Mining] You are asked to design models to predict the restaurant type using the observed variables.

Production-grade health monitoring and predictive fault management system for NVIDIA A100/H100 GPU fleets

Hardware-aware CNN training performance predictor for CIFAR-10 on NVIDIA A100—learns sec/epoch from config features and derives throughput (images/sec) from predicted time.

Develop an NLP model driven by ML to accurately predict students’ potential math misconceptions based on student explanations in open-ended responses.