LLM Serving
AI and machine learning skill, available on Zeplik
LLM Serving is a ready-to-run AI and machine learning skill on Zeplik. High-throughput LLM inference serving — vLLM PagedAttention, SGLang, TensorRT-LLM, and llama.cpp for CPU/Apple Silicon. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
The LLM Serving skill loads automatically when your request matches it, or you can invoke it directly by typing /llm-serving in any chat. It works with attachments, connectors, and any model that supports the task, so you get the same expert method every time without setting anything up.
What the LLM Serving skill can do
- Recommend an LLM serving stack based on hardware and throughput targets
- Generate vLLM PagedAttention configs for high throughput batching
- Configure SGLang deployments with RadixAttention prefix caching
- Build TensorRT-LLM or llama.cpp setups for GPU-less or consumer hardware
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Pick a prompt to open it in the Zeplik app. If you are not signed in yet, your prompt is waiting for you the moment you do.
How the LLM Serving skill works
/llm-serving
Umbrella for LLM inference serving. The user wants to serve a model; establish hardware, throughput/latency targets, and batching needs, then pick the server and deliver a deployment config. For compressing the model first route to llm-quantization.
Dispatch table
Pick the reference file(s) that match the request, read them, then answer. Read at most 2-3 files per turn.
| Topic | Read |
|---|---|
| Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. | references/llama-cpp.md |
| Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. | references/serving-llms-vllm.md |
| Fast structured generation and serving for LLMs with RadixAttention prefix caching. | references/sglang.md |
| Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. | references/tensorrt-llm.md |
How to work
- Identify which leaf topic the request maps to from the dispatch table above; establish the concrete inputs (language, dataset, framework, file format) and the goal. Ask for a missing detail rather than guessing.
- Read the matching reference file(s) before answering. Read at most 2-3 per turn.
- Deliver runnable artifacts — code, configs, specs — with a short rationale, matching the user's existing conventions when they paste code.
- Confirm any decision the source flags (versions, thresholds, tradeoffs) with the user instead of guessing.
Usage
/llm-serving $ARGUMENTS
How to use the LLM Serving skill
Sign in to Zeplik
Create a free Zeplik account or sign in. New accounts start with free credits, so you can try the LLM Serving skill right away.
Describe your AI and machine learning task
Ask in plain language, or type /llm-serving to invoke the skill directly. Zeplik recognizes the LLM Serving skill and applies its method.
Review and refine the result
Zeplik returns a clear, structured answer. Ask follow-ups in the same chat to refine it or take the next step.
Source and credit
- Author
- davila7 (D7 umbrella-consolidation)
- License
- MIT
Adapted from the open-source davila7/claude-code-templates project and tuned to run natively on Zeplik. View source on GitHub.
Frequently asked questions
- What is the LLM Serving skill?
- LLM Serving is a ready-to-run AI and machine learning skill on Zeplik. High-throughput LLM inference serving — vLLM PagedAttention, SGLang, TensorRT-LLM, and llama.cpp for CPU/Apple Silicon. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
- How do I use LLM Serving on Zeplik?
- Sign in to Zeplik and ask in plain language, or type /llm-serving in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.
- Which AI model does the LLM Serving skill use?
- Any model you choose. Zeplik works across every model in one chat, so the LLM Serving skill runs on your preferred model for the task.
- Where does the LLM Serving skill come from?
- The LLM Serving skill is adapted from the open-source davila7/claude-code-templates project (MIT) and tuned to run natively on Zeplik. The original source is linked on this page.
- How much does the LLM Serving skill cost?
- Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.
Related ai and machine learning skills
- Agent MemoryMemory and context management for LLM agents — short/long-term memory stores, conversation persistence, context-window strategies (summarization, trimming, retrieval) and prompt caching. Use for "give my agent memory / manage context"; for vector stores see vector-databases.
- AI Safety GuardrailsSafety and moderation for LLM apps — Constitutional AI, Llama Guard input/output moderation, and NeMo Guardrails runtime rails. Use for "add safety/moderation/guardrails to my LLM app"; for evaluating safety see llm-evaluation-harnesses.
- Autonomous Agent PatternsFramework-agnostic design patterns for autonomous coding agents — goal decomposition, planning, parallel/multi-agent orchestration and operational modes. Use for "how should I architect an autonomous agent"; for concrete frameworks see ai-agent-frameworks.
- Gemini CLIRun Gemini CLI (Gemini 3 Pro) for code/plan review and huge >200k-token context analysis in the terminal
- InterpretabilityMechanistic interpretability of neural networks — TransformerLens, NNsight remote access, sparse autoencoders (SAELens), and causal interventions (pyvene). Use for "probe/intervene on model internals" or "train an SAE"; for architecture basics see llm-architectures.
- LLM App PatternsProduction patterns for LLM applications — RAG architecture, embeddings, LLMOps, and end-to-end app design. Use for "architect a production LLM/RAG app"; for the vector store see vector-databases, for agents see ai-agent-frameworks.
More on Zeplik
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