Local Model Operations

Use this skill to choose and operate local or self-hosted LLM workflows when privacy, offline access, or low-friction experimentation matter more than large-cluster serving.

This skill covers:

• local runtime choice for laptops, workstations, and small self-hosted setups
• team-facing local or private chat surfaces
• single-binary or minimal-dependency model packaging
• lightweight adaptation paths before full training or cluster-scale serving
• evaluation and escalation rules before a local stack becomes a product dependency

ASCII Flow

local/private model need
  |
  v
constraint
  privacy | offline | cost | hardware | demo portability | team chat
  |
  v
runtime selection
  Ollama | LM Studio | MLX | Microsoft Foundry Local | Open WebUI | llamafile | lightweight adapter workflow
  |
  v
local operating contract
  pinned model + quantization + eval set + storage/privacy boundary
  + optimization levers: KV-cache quant | speculative decoding | NPU tier
  |
  v
use or escalate
  local workflow OR hand off to inference/MLOps for production serving

Quick Reference

Need	Default path	Notes
Run a local model quickly	Ollama	Lowest-friction day-0 local runtime for experiments and private workflows
Share a self-hosted chat UI	Open WebUI	Best fit when a team needs a ChatGPT-like local or private interface
Ship a no-install demo or portable binary	llamafile	Useful for single-file distribution and low-ops delivery
Apple Silicon on-device inference at framework level	MLX (mlx-lm)	Primary path for Metal-native inference and LoRA fine-tune on Mac; verify at https://github.com/ml-explore/mlx-lm
GUI model browser and switcher (non-technical users)	LM Studio	Supports GGUF and MLX; good for rapid model comparisons
Windows / enterprise SDK-first local inference	Microsoft Foundry Local	Curated Microsoft catalog; SDK + REST; verify at https://learn.microsoft.com/en-us/ai/foundry-local
Fine-tune or adapt cheaply	Unsloth + ../ai-llm/SKILL.md	Good for lightweight adaptation, not a substitute for full training ops
Optimize throughput or production serving	../ai-llm-inference/SKILL.md	Use this skill for local ops; use ai-llm-inference for deeper serving engineering

Runtime Selection

Situation	Best fit
Solo developer or analyst on one machine	Ollama
Internal team chat with local or self-hosted models	Open WebUI
Portable model demo or offline executable distribution	llamafile
Fast adapter and fine-tuning iteration on limited hardware	Unsloth
Apple Silicon, framework-level inference or LoRA fine-tune	MLX (mlx-lm)
Non-technical user, GUI model management	LM Studio
Windows-primary, SDK-first, Microsoft model catalog	Microsoft Foundry Local

See references/desktop-runtime-landscape.md for a detailed Ollama / LM Studio / Foundry Local comparison.

Local vs Hosted API: Judgment, Not Reflex

Do not default to "local" just because privacy or cost was mentioned once. Decide with a real eval set and a real cost model:

• Local tends to win on data residency/offline requirements, steady high-volume traffic that amortizes hardware cost, sub-100ms latency needs, and narrow tasks where a well-evaluated 7–32B open model already matches frontier quality.
• Hosted API tends to win on frontier-tier reasoning or long-context needs that no locally-runnable model size covers yet, spiky/low-volume traffic, or when local ops overhead (drivers, quant regressions, capacity planning) would cost more engineering time than the API bill.
• Full frontier-scale open weights (DeepSeek-V3/R1-class, Mistral Large 3, Llama 4 Maverick) do not fit on a single consumer GPU or single H100 at usable quant — "open-weight" does not mean "runs on your laptop." Check the model-sizing matrix before promising local feasibility.
• Local inference feasibility does not imply local training or fine-tuning feasibility. Training amplifies memory, bandwidth, and especially energy per sample well beyond inference, so the binding constraint often shifts from VRAM capacity to thermal/power budget. See references/model-sizing-matrix.md before promising on-device adaptation.

See references/model-sizing-matrix.md for the full tradeoff and current family-by-family sizing (Llama, Qwen, DeepSeek, Mistral, Gemma, GPT-OSS).

Default Workflow

1. Define the real constraint first: privacy, offline use, cost ceiling, hardware ceiling, or demo portability.
2. Pick the runtime or UI layer that matches that constraint.
3. Pin model IDs, quantization choice, and prompt/eval set before broader rollout.
4. Decide whether the stack is only for local use or will become part of a product or team workflow.
5. If it needs stronger serving, routing, or monitoring, hand off to the adjacent skills instead of stretching a local-first setup too far.

Operational Rules

• Keep model IDs and quantization choices explicit and versioned.
• Treat local and self-hosted endpoints as sensitive services, not casual defaults for internet exposure.
• Measure quality on a small real eval set before swapping local models into a user-facing workflow.
• Separate runtime selection from product integration. Running a model locally is not the same thing as shipping a good AI feature.

Known Traps

• Treating a laptop prototype as proof that a workflow is production-ready. Latency, uptime, auth, and observability requirements change immediately once real users appear.
• Leaving model IDs, quant levels, and system prompts implicit. Local stacks drift quickly when operators rely on tags like latest.
• Exposing Ollama, Open WebUI, or ad hoc reverse proxies without an explicit threat model and access controls.
• Assuming a polished chat UI solves governance. UI convenience does not replace logging, retention policy, or approval paths.
• Using lightweight local adaptation as a substitute for evaluation discipline. Faster iteration is useful only if the eval loop is real.
• Using vLLM V0 features — V0 is fully deprecated as of 2026. Use the V1 engine (see https://docs.vllm.ai/en/stable/usage/v1_guide/).
• Citing vendor speedup figures (e.g., Microsoft Foundry Local vs cloud) as neutral benchmarks — always measure on your own workload.
• Assuming speculative decoding helps at high concurrency — the benefit is concentrated at batch size 1.
• Running NPU inference without confirming the accelerator is being used — fallback to CPU/GPU is silent in some runtimes.

Common Anti-Patterns

• Installing several local runtimes at once before deciding which constraint actually matters: privacy, portability, cost ceiling, or offline access.
• Treating local models as drop-in replacements for hosted models without rechecking tool use, structured outputs, and long-context behavior.
• Shipping a team workflow on consumer hardware with no capacity envelope, backup path, or restart procedure.
• Using "local" as the only justification for a stack choice when a small self-hosted or managed setup would be operationally safer.

Escalation Boundaries

Use adjacent skills when:

• you need cluster-scale serving or throughput tuning -> ai-llm-inference
• you need full fine-tuning strategy, dataset design, or evaluation -> ai-llm
• you need product UX, streaming chat, or structured output in an app -> software-ai-integration
• you need deployment, monitoring, or operational governance -> ai-mlops

When To Use This Skill

Use this skill when the user asks:

• "Should I use Ollama or something else locally?"
• "How do I run private models on my machine or a small server?"
• "What should I use for a self-hosted ChatGPT-like interface?"
• "How do I package a model into a low-friction local demo?"
• "When should I stay local vs move to a real serving stack?"
• "What's the best way to run models on Apple Silicon / Mac?"
• "Should I use MLX or Ollama on my M-series Mac?"
• "What small models can I run locally? Gemma / Phi / Qwen / DeepSeek / Mistral?"
• "How do I speed up local inference with speculative decoding or KV-cache quantization?"
• "What is LM Studio / Microsoft Foundry Local for?"
• "Should I run this locally or just call a hosted API?"

Navigation

References

• references/runtime-selection.md - runtime, UI, and packaging choice rules
• references/desktop-runtime-landscape.md - Ollama vs LM Studio vs Microsoft Foundry Local: detailed comparison, decision heuristic, anti-patterns
• references/adaptation-and-packaging.md - lightweight adaptation, portable delivery, and evaluation handoff rules
• references/model-sizing-matrix.md - Llama 4, Mixtral, Qwen 3, GPT-OSS across M3/M4/4090/H100 hardware tiers; MLX and Ollama Apple Silicon paths; throughput estimates; why local training feasibility differs from local inference feasibility
• references/quantization-format-table.md - GGUF Q4_K_M/Q5_K_M/Q8_0, AWQ, GPTQ, FP8, EXL2; KV-cache quantization; speculative decoding; NPU/accelerator tier; decision tree
• references/small-model-tier-table.md - Gemma, Phi, Qwen small-instruct capability tiers: footprint, strengths, selection heuristic (versions hedged — verify current release)
• data/sources.json - local-model tooling sources from the curated repo list

Templates

• assets/templates/ollama-setup-recipe.md - end-to-end Ollama install, model pull, OpenAI-compatible API usage, Modelfile pinning
• assets/templates/openwebui-deployment-recipe.md - Docker / Compose deployment of Open WebUI over Ollama, env vars, reverse proxy, upgrades

Related Skills

• ../ai-llm-inference/SKILL.md - serving, quantization, routing, and throughput tuning
• ../ai-llm/SKILL.md - full LLM lifecycle and fine-tuning strategy
• ../ai-mlops/SKILL.md - deployment, monitoring, and governance
• ../software-ai-integration/SKILL.md - product integration and AI UX

Fact-Checking

• Known bugs, regressions, framework/compiler/runtime footguns, and version-specific crash or workaround guidance must be verified against current primary web sources before being treated as current fact.
• Start from data/sources.json for local-model tooling references.
• Verify current model support, install steps, and hardware caveats before giving time-sensitive recommendations.
• If web access is unavailable, mark runtime-specific claims as unverified.

Learnings Loop

Before applying this skill on a non-trivial task, read learnings.consolidated.md in this directory (and learnings.md if present).

After applying it, if you encountered a pattern worth remembering, a mistake worth preventing, or a domain fact that surprised you, append one dated bullet to learnings.md via agents-skills-feedback-loop/scripts/append_learning.py. Do not modify SKILL.md itself.