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LocalAI/core/http/react-ui
LocalAI [bot] f88981cdce feat(ui): data-driven hardware model recommendations + gallery surfacing (#10500)
* feat(ui): make hardware starter models data-driven

The empty-state starter widget recommended from a hardcoded list, which
drifts as the gallery evolves. Add useRecommendedModels: it queries the
live gallery for chat-capable models (their natural curated order, since
the gallery exposes no popularity signal), estimates size/VRAM for the top
candidates via the existing estimate endpoint, and ranks by hardware fit -
smallest on CPU-only boxes, largest-that-fits on GPUs.

StarterModels now renders those live picks and keeps the curated static
list only as an offline/trimmed-gallery fallback.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]

* feat(ui): recommend models for your hardware in the gallery

Hardware-aware recommendations were only shown on the first-run empty
state. Surface them on the main Models gallery too: a dismissible
"Recommended for your hardware" strip at the top, sharing the
useRecommendedModels fit-ranking with the starter widget. CPU-only boxes
get small models; GPUs get the largest picks that fit VRAM, with size and
VRAM shown per card. One-click install; dismissal persists per browser.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]

* feat(ui): gpu-mid tier + NVIDIA NVFP4 model recommendations

Refine the hardware recommendation tiers and curated picks:

- Add a gpu-mid tier (8-24GB VRAM) between gpu-small and gpu-large, so
  ~27B-class models are suggested separately from the 30B+ large tier.
- Detect NVIDIA GPUs (resources.gpus[].vendor) and, on NVIDIA only, prefer
  NVFP4 + MTP variants (Blackwell-optimised); NVFP4 models are filtered out
  of recommendations on non-NVIDIA hardware where they can't run. This
  applies to both the live ranking and the static fallback, with an NVFP4
  badge shown on those picks.
- Refresh the curated fallback to current models: Gemma-4 QAT Q4 builds at
  every tier, low qwen3.5 (4B distilled / 9B) on CPU/small, qwen3.6-27b
  and MTP variants at mid, qwen3.6/qwen3.5 35B-A3B apex/distilled at large.
  All names verified against gallery/index.yaml.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]

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Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-25 00:22:45 +02:00
..
2026-05-08 16:25:45 +02:00