FILES & 30GB STORAGE
NCC Storage Volume
30.0 GB PostgreSQL
NOTEBOOK OUTLINE
1. NCC CUDA Hardware Verification
2. Multi-GPU Tensor Matmul Benchmark
3. Neuroblyx Research Performance Table
4. Real-Time Distributed Loss Curves
| Name | Type | Shape | Value |
|---|
Neuroblyx AI Assistant
Ask questions, generate optimized PyTorch Hopper kernels, or diagnose training errors.
[1]
CUDA Available: True
Active NCC GPU: NVIDIA H100 80GB SXM5
Allocated VRAM: 79.6 GB HBM3 (NVLink 4.0 900 GB/s)
Device Architecture: Hopper (Compute Capability 9.0)
[2]
Matrix Multiplication Finished: Tensor shape torch.Size([8192, 8192])
Allocated VRAM: 128.0 MiB
Kernel Latency: 0.14 ms | Performance: 7,850 TFLOPS (FP16 Tensor Core)
[3]
| Model Variant | Cluster Partition | Throughput (Tokens/s) | Val Accuracy | Status |
|---|---|---|---|---|
| Neuroblyx-Transformer-7B | ncc_h100 (8x SXM5) | 142,500 | 98.8% | CONVERGED |
| AlphaFold-JAX-v2 | ncc_h100 (4x SXM5) | 89,200 | 94.6% | CONVERGED |
| Vision-Pathology-ResNet | ncc_a100 (2x PCIe) | 34,100 | 99.2% | IN-TRAINING |
[4]
Neuroblyx H100 Training Convergence (Loss vs Epoch)