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Found 16 Skills
Pick the serving stack and per-runtime memory flags (vLLM, SGLang, llama.cpp, TensorRT Edge-LLM) for an LLM/VLM workload on any NVIDIA Jetson.
Delegate a coding task to Aider (`aider`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to Aider - phrasings like "have Aider do X", "delegate this to aider", "run it through Aider", or "use Aider to implement/fix/refactor" - or wants to run a queue of coding tasks through Aider while staying the reviewer. This includes asking Aider to drive a local or self-hosted OpenAI-compatible endpoint ("have Aider use my local model", "run Aider against llama.cpp / Ollama / vLLM / LM Studio"), which Aider reaches via `--api-base`. DO NOT USE for local-model or coding requests that do not name Aider, for tasks small enough to do inline, or when the user wants the code written directly without delegating.
Use when the user asks about MLX serving, mlx_lm.server, oMLX, Apple Silicon LLM serving, or running a local LLM on a Mac, and when troubleshooting a model that fails to load, OOM during load or inference, a server that hangs or crashes at batch>1, tool calls returned as plaintext content, a throughput regression, or the choice between mlx-lm and oMLX. Also covers oMLX feature-flag tuning (turboquant_kv, dflash, MTP, specprefill, thinking_budget, max-concurrent-requests, force_sampling), the OptiQ proxy for models exceeding RAM, Llama-4 ChunkedKVCache batch handling, Llama-3 tool-call JSON format, and bench-driven validation of serving configs. Apple Silicon M-series only, not cloud LLM hosting, non-MLX backends such as llama.cpp, Ollama and vLLM, or model training.
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.