Open source devs hitting 3 hard walls when fine-tuning LLMs on distributed hardware:
1. Memory fragmentation across nodes - most frameworks can't efficiently shard model states beyond simple data parallelism
2. Network bandwidth choking on internet connections - gradient sync and activation passing eat bandwidth that consumer ISPs can't handle
3. Node failures nuking entire runs - no fault tolerance means hours of compute wasted when one machine drops
Current frameworks (DeepSpeed, FSDP, etc.) assume datacenter-grade infra. They're not built for heterogeneous home setups where one node has 24GB VRAM, another has 12GB, and they're connected over residential fiber with 100ms latency spikes.
1. Memory fragmentation across nodes - most frameworks can't efficiently shard model states beyond simple data parallelism
2. Network bandwidth choking on internet connections - gradient sync and activation passing eat bandwidth that consumer ISPs can't handle
3. Node failures nuking entire runs - no fault tolerance means hours of compute wasted when one machine drops
Current frameworks (DeepSpeed, FSDP, etc.) assume datacenter-grade infra. They're not built for heterogeneous home setups where one node has 24GB VRAM, another has 12GB, and they're connected over residential fiber with 100ms latency spikes.