Local LLM ran out of VRAM or context during a coding task
Local models are powerful, private, and cheap, but they hit practical limits: VRAM, context length, and weaker reasoning on messy state.
Why this hurts.
When a local agent fails, the next model still needs the current repo state, not a half-remembered chat.
How ShardStitch solves it.
Preserve the project checkpoint
Save the diff, active goal, failed attempt, and next check before retrying with a smaller context.
Record the runtime conditions
Record the model, context setting, available VRAM, and exact out-of-memory or context error.
Reduce the next unit of work
Split the next action into a smaller testable step; use a larger model only when configured and needed.
Retry only after checking resources
Check free memory and settings before retrying; if the local model still fails, carry the packet to a configured alternative.
What the next AI receives.
- Small context packet
- Current files
- Risks
- Next action
What stays out.
- Oversized context
- Full transcript
- Irrelevant files
- Large blobs unless needed
Separate model pressure from lost work
FAQ.
Can ShardStitch fix a local model VRAM error?
No. It can help carry project context into a smaller or later run, but GPU memory allocation is controlled by the model runtime and hardware.
Will a shorter handoff guarantee the model fits?
No. Memory use also depends on model size, runtime, context configuration, and other processes. A smaller task may help, but verify it in the local runtime.