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* feat(config): node-aware hardware defaults — larger physical batch on Blackwell A larger physical batch (n_batch/n_ubatch) materially lifts MoE prefill on NVIDIA Blackwell consumer GPUs (sm_120/121, incl. GB10 / DGX Spark) — measured on a GB10 with Qwen3-Coder-30B-A3B, the prefill ceiling rises (ub512 ~2994 -> ub2048 ~3316 t/s) and saturates around 2048. The heuristic lives in core/config alongside the other config overriders (ApplyInferenceDefaults, guessDefaultsFromFile/NGPULayers) — they all fill the ModelConfig from heuristics, so hardware tuning is the same domain and stays in one place. It is parameterized on a GPU descriptor (not direct detection) so it works in both deployment shapes: - Single host: SetDefaults applies it with the LocalGPU. - Distributed: only the worker sees the GPU, so the worker reports its compute capability on registration (gpu_compute_capability -> BackendNode), and the router re-applies the SAME core/config heuristic for the SELECTED node before loading — fixing the case where the frontend has no GPU at all. Explicit `batch:` always wins (only managed default values are touched). xsysinfo gains NVIDIAComputeCapability() (detection only); all interpretation lives in core/config. Tests: core/config, pkg/xsysinfo, core/services/nodes. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(config): injectable local-GPU seam + single-instance coverage Make local GPU detection an injectable package var (localGPU) so the single-instance path (SetDefaults -> ApplyHardwareDefaults) is deterministically testable without a real GPU, mirroring the distributed override's coverage. Adds specs asserting SetDefaults sets the Blackwell physical batch, leaves it unset on non-Blackwell, and never overrides an explicit batch. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(config): default concurrent serving (n_parallel) by GPU VRAM The llama.cpp backend defaults n_parallel=1, which serializes multi-user requests and leaves continuous batching off (it auto-enables only at n_parallel>1). Fold a VRAM-scaled parallel-slot default into the hardware-config path so multi-user serving works out of the box: >=32GiB->8, >=8GiB->4, >=4GiB->2, else unchanged. With the backend's unified KV the slots SHARE the context budget, so this adds concurrency without multiplying KV memory. Explicit parallel/n_parallel always wins. EnsureParallelOption is shared by the single-host path (ApplyHardwareDefaults with the local GPU) and the distributed router (per selected node's reported VRAM, since the frontend may have no GPU). LocalGPU now also reports VRAM. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io>