Expose the new `mmproj_offload` option in the model settings UI and include it in the `ModelPlan` type. The component now collects the offload flag (`result.offloadMmproj`) and queues it with other setting updates to ensure a single atomic change, preventing race conditions when toggling this feature. This enables users to control MMProj offloading directly from the app.
* feat: Smart model management
* **New UI option** – `memory_util` added to `settings.json` with a dropdown (high / medium / low) to let users control how aggressively the engine uses system memory.
* **Configuration updates** – `LlamacppConfig` now includes `memory_util`; the extension class stores it in a new `memoryMode` property and handles updates through `updateConfig`.
* **System memory handling**
* Introduced `SystemMemory` interface and `getTotalSystemMemory()` to report combined VRAM + RAM.
* Added helper methods `getKVCachePerToken`, `getLayerSize`, and a new `ModelPlan` type.
* **Smart model‑load planner** – `planModelLoad()` computes:
* Number of GPU layers that can fit in usable VRAM.
* Maximum context length based on KV‑cache size and the selected memory utilization mode (high/medium/low).
* Whether KV‑cache must be off‑loaded to CPU and the overall loading mode (GPU, Hybrid, CPU, Unsupported).
* Detailed logging of the planning decision.
* **Improved support check** – `isModelSupported()` now:
* Uses the combined VRAM/RAM totals from `getTotalSystemMemory()`.
* Applies an 80% usable‑memory heuristic.
* Returns **GREEN** only when both weights and KV‑cache fit in VRAM, **YELLOW** when they fit only in total memory or require CPU off‑load, and **RED** when the model cannot fit at all.
* **Cleanup** – Removed unused `GgufMetadata` import; updated imports and type definitions accordingly.
* **Documentation/comments** – Added explanatory JSDoc comments for the new methods and clarified the return semantics of `isModelSupported`.
* chore: migrate no_kv_offload from llamacpp setting to model setting
* chore: add UI auto optimize model setting
* feat: improve model loading planner with mmproj support and smarter memory budgeting
* Extend `ModelPlan` with optional `noOffloadMmproj` flag to indicate when a multimodal projector can stay in VRAM.
* Add `mmprojPath` parameter to `planModelLoad` and calculate its size, attempting to keep it on GPU when possible.
* Refactor system memory detection:
* Use `used_memory` (actual free RAM) instead of total RAM for budgeting.
* Introduced `usableRAM` placeholder for future use.
* Rewrite KV‑cache size calculation:
* Properly handle GQA models via `attention.head_count_kv`.
* Compute bytes per token as `nHeadKV * headDim * 2 * 2 * nLayer`.
* Replace the old 70 % VRAM heuristic with a more flexible budget:
* Reserve a fixed VRAM amount and apply an overhead factor.
* Derive usable system RAM from total memory minus VRAM.
* Implement a robust allocation algorithm:
* Prioritize placing the mmproj in VRAM.
* Search for the best balance of GPU layers and context length.
* Fallback strategies for hybrid and pure‑CPU modes with detailed safety checks.
* Add extensive validation of model size, KV‑cache size, layer size, and memory mode.
* Improve logging throughout the planning process for easier debugging.
* Adjust final plan return shape to include the new `noOffloadMmproj` field.
* remove unused variable
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Co-authored-by: Faisal Amir <urmauur@gmail.com>
