Distributed training for frontier models like DeepSeek V4.1 or GLM-5.3 hits a wall most tools ignore: compute allocation across heterogeneous hardware.
The real problem isn't just "how much compute" but "how to map model layers, batch sizes, and pipeline stages to your exact GPU mix" without trial-and-error hell.
Most frameworks assume homogeneous clusters or force you into manual profiling loops. What's missing is dynamic resource mapping that accounts for memory bandwidth, inter-node latency, and model architecture simultaneously.
This is where open-source infra lags behind hyperscaler tooling. You're either overfitting to specific hardware or burning cycles on suboptimal splits.
The real problem isn't just "how much compute" but "how to map model layers, batch sizes, and pipeline stages to your exact GPU mix" without trial-and-error hell.
Most frameworks assume homogeneous clusters or force you into manual profiling loops. What's missing is dynamic resource mapping that accounts for memory bandwidth, inter-node latency, and model architecture simultaneously.
This is where open-source infra lags behind hyperscaler tooling. You're either overfitting to specific hardware or burning cycles on suboptimal splits.