Help › Choosing an AI Model: Local vs Cloud for Lightroom

Help: Choosing an AI Model

The exact model lists exposed by the plugin come from the backend at runtime. The names below reflect the curated lists shipped with the current backend (server-rs/crates/lrg-providers/ for cloud providers, server-rs/crates/lrg-api/src/{llm_models,mlx_models}.rs for the built-in local ones). Pricing and availability change over time — verify with each provider before relying on production cost estimates.

Decision factors

Choose based on:

  • privacy requirements (cloud vs. local)
  • quality expectations (description detail, keyword accuracy, edit recipe sanity)
  • runtime per image and batch throughput
  • per-image cost (cloud) or hardware cost (local)
  • available local hardware (VRAM/RAM, Apple Silicon vs. discrete GPU)

Cloud models

Google Gemini

Configure in Plug-in Manager → API Keys → Gemini API key. Models exposed today:

  • gemini-2.5-flash-lite — cheapest and fastest; good for bulk keywording.
  • gemini-2.5-flash — balanced default for analyze-and-index runs.
  • gemini-2.5-pro — highest 2.5-tier quality; use for tricky scenes or when description quality matters more than throughput.
  • gemini-3-flash-preview, gemini-3.1-flash-lite-preview, gemini-3.1-pro-preview — latest preview tier. Expect higher quality and better instruction following, but preview pricing/quotas can change.

The backend automatically tunes a thinking budget for gemini-2.5-* and gemini-3-pro-preview, so you don’t need to configure that yourself.

OpenAI / ChatGPT

Configure in Plug-in Manager → API Keys → OpenAI API key. Models exposed:

  • gpt-4.1 — proven vision quality; the safe baseline.
  • gpt-5-nano, gpt-5-mini, gpt-5 — current GPT-5 tier; pick nano/mini for batch jobs and gpt-5 for higher-fidelity descriptions.
  • gpt-5.4-nano, gpt-5.4-mini, gpt-5.4, gpt-5.4-pro — newest GPT-5.4 tier; gpt-5.4-pro is the highest-quality option but the most expensive.

Note: GPT-5 and GPT-5.4 models ignore the temperature slider and use a fixed reasoning effort — small differences in plugin temperature settings will not affect output for these models.

They do use the Max Tokens setting (General tab → AI Model section), and they spend part of that budget on internal reasoning before writing a single word of the answer. If a GPT-5 model reports that it stopped because the token limit was reached, raise Max Tokens to 4096 or higher — the same value that is comfortable for gpt-4.1 can be too tight here.

Vertex AI (embeddings only) — REMOVED

⚠️ Removed in August 2026. The plugin no longer offers Vertex AI anywhere: no project/location settings, no Vertex embeddings during Analyze & Index, no Semantic (Vertex AI) search option. Semantic search now runs on the built-in SigLIP2 embeddings only. Existing Vertex embeddings stay in the database but are not updated or queried. Description below kept for reference.

Vertex AI is used for the multimodalembedding@001 model that powers the image_embeddings_vertex semantic-search collection. It is not an alternative LLM for keywords/descriptions — pair it with a Gemini, ChatGPT, or local provider for metadata generation. See Google Vertex AI Login.

Local models

Local providers run on your own machine, so privacy is the strongest argument for using them. Quality of small open-weights vision models has improved significantly, but cloud frontier models still lead on tricky scenes.

There are two kinds of local option: the engines built into the backend (nothing else to install), and external servers you run yourself (Ollama, LM Studio).

Built-in: llama.cpp and MLX (no external app)

The backend runs vision models itself. Open Plug-in Manager → LrGeniusAI, find the Local AI Model sections, pick a model, and click Download — it then appears in the model dropdown as llamacpp: <model> or mlx: <model>.

  • llamacpp — llama.cpp compiled into the backend, using GGUF models. Available on macOS (Metal), Windows (Vulkan, any GPU vendor), and Linux (CPU). Recommended entries: Gemma 4 E4B as the default, Gemma 4 12B (QAT) if you have 24 GB of RAM, Ministral 3 8B or Qwen3.5 9B as alternatives, Qwen2.5-VL 3B on modest hardware.
  • mlx — Apple’s MLX stack via a small Metal helper process, Apple silicon only. Recommended entries: Gemma 4 E4B as the default, Gemma 4 E2B for speed, Ministral 3 8B or Qwen3-VL 4B as alternatives.

On an Apple silicon Mac both are available and worth a side-by-side run on the same 10–20 photos: MLX is Apple’s native inference stack, while llama.cpp reuses the shared prompt prefix across the photos in a batch (MLX re-processes it per photo), which matters more the larger the batch.

Both reuse models you already have: llama.cpp picks up GGUFs under ~/.lmstudio/models, and MLX picks up LM Studio’s MLX models and the huggingface-cli cache. Full guide: Local AI Models.

One caveat that applies to both: do not enable keyword aliases or bilingual keywords with a local model. They turn every keyword into a structured object, which small models handle badly — the Analyze & Index dialog warns about it.

Ollama

Install and start Ollama from ollama.com, then pull at least one vision-capable model. Recommended starting points:

ollama pull qwen3-vl:4b-instruct-q4_K_M     # fast, ~6 GB VRAM
ollama pull qwen3-vl:8b-instruct-q4_K_M     # better quality, ~10 GB VRAM
ollama pull gemma3:4b-it-q4_K_M             # good general default
ollama pull gemma3:12b-it-q4_K_M            # higher quality if you have VRAM
ollama pull llava                            # legacy fallback

Browse all vision models: ollama.com/search?c=vision. See Ollama Setup.

LM Studio

Worth running as an external server mainly if you already use it for other things, or want its model browser and per-model tuning; otherwise the built-in engines above cover the same ground with one fewer app running.

Download from lmstudio.ai, enable server mode, and download one or more vision models from inside the app. Recommended:

  • qwen/qwen3-vl-4b — fast baseline.
  • qwen/qwen3-vl-8b — better description quality.`
  • gemma-4-e4b / google/gemma3-12b — strong general-purpose options.

On Apple Silicon prefer the MLX variants of the same model — they run significantly faster than the GGUF builds. See LM Studio Setup.

Quick recommendations

WorkflowSuggested first try
Cheap bulk keywording (cloud)gemini-2.5-flash-lite or gpt-5-nano
Balanced default (cloud)gemini-2.5-flash or gpt-5-mini
Best description quality (cloud)gemini-2.5-pro, gpt-5.4, or gpt-5.4-pro
Privacy-first, simplest setupBuilt-in llamacpp with Gemma 4 E4B
Apple Silicon, localBuilt-in mlx with Gemma 4 E4B (E2B if 8–16 GB)
Windows with any discrete GPU, localBuilt-in llamacpp (Vulkan) with Gemma 4 E4B
Modest hardware / 8 GB RAMmlx Gemma 4 E2B or llamacpp Qwen2.5-VL 3B
Already running Ollama / LM StudioOllama qwen3-vl:8b or LM Studio qwen3-vl-8b

Practical recommendation

The dropdown in Analyze & Index always reflects what the backend currently advertises — newer models that ship with future backend updates will appear automatically. If a model you expect is missing, check that the corresponding API key or local server is configured and reachable from the backend (the Plugin Manager → Status section reports availability per provider).

When evaluating, run the same batch of 10–20 representative photos through two candidates and compare:

  • keyword coverage and accuracy
  • description quality and language correctness
  • runtime per image and end-to-end batch time
  • system load (local) or token cost (cloud)

Throughput on cloud providers

Cloud runs are dominated by network round trips, not by the model. The backend overlaps up to four requests at a time for OpenAI and Gemini, which hides most of that latency without looking like a burst to their rate limiters.

Ollama and LM Studio deliberately stay at one request at a time: the server on the other end is a single local model that serialises the work anyway, and piling requests on it only competes with Lightroom for the same machine.

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