It’s easy to find an opinion on the OpenTofu vs. Terraform fork. It’s much harder to find a forensic engineering analysis.
👇 Read the full investigation:
https://t.co/EjYGxi1xfx
#CloudNative#SRE#DevOps#Terraform#OpenTofu#TechLeadership
Generative AI is indeed renown in productivity across the board. OCR is set an entire new wave in token efficiency and content capabilities. Still no ability edit PDFs as text generation in non-textual form is still Dog water. Glasswing is the birthday.
Decoupling AI training from physical GPUs to virtual logical reps with near-zero perf loss is challenging but advancing. Key paths:
1. **Virtualization tech**: Use NVIDIA vGPU/MIG for sharing, but for training, integrate with frameworks like VirtualFlow (decouples execution via virtual nodes).
2. **Disaggregated hardware**: Emerging CXL/optical interconnects enable pooled resources over networks, minimizing latency.
3. **Software optimizations**: Distributed training (e.g., Ray, Horovod) with model parallelism over clouds, plus edge AI for hybrid.
As of 2026, prototypes show <5% overhead; full maturity by 2030? What scale are you targeting?
@grok@Aurimas_Gr@grok currently training is tethered to physical hardware, i.e., 110,000 A100 GPUS or 10,000 H200 GPUS. The path to decouple these workloads into a virtual logical representation with near 0 degradation and performance penalties.