Local inference foundation
Run AI Locally: Setup, Integration and Reliable Inference
Local inference gives you control over where a supported model runs, but it also makes you responsible for hardware, model provenance, access and reliability. Start with one working endpoint, then add an application and measure the workload. These documentation-based guides make no hands-on benchmark or automatic privacy claim: cloud models, embeddings, extensions and agent tools can have separate network paths.
Decisions this guide helps you make
- Which runtime and supported model artifact fit your machine and application.
- How to prove the server, installed model and real client agree before adding complexity.
- Where local inference ends and authentication, data handling and tool permissions begin.
- When to reduce context or concurrency, change models, or evaluate shared serving capacity.
Choose a runtime and a model artifact
Start with the job: visual exploration, a local application endpoint, or a shared inference service. Check the exact architecture, quantization and hardware together. A downloadable model is not a guarantee that it fits or that its licence permits your use.
Ollama vs LM Studio vs vLLM
Choose by workflow, supported APIs and measured concurrency, not unsupported speed rankings.
Read guide →GGUF vs GPTQ vs AWQ
Separate model containers from quantization methods and verify runtime compatibility.
Read guide →LM Studio setup
Download a supported artifact, validate chat and connect a local API. Includes the headless decision.
Read guide →Local AI on Windows
Choose native Ollama, LM Studio or WSL without mixing servers, storage and drivers.
Read guide →Establish one private working endpoint
Prove the server is reachable, list its installed models and generate one short answer before adding clients. Local APIs are not automatically authenticated. Keep a single-machine service on loopback and add a deliberate access layer before sharing it.
Ollama setup and operating boundary
Install once, identify the serving process and verify a model and API separately.
Read guide →Ollama in Docker
Persist model storage and distinguish host, container and remote client addresses.
Read guide →Open WebUI with Ollama
Add browser chat, persistent accounts and a private backend network.
Read guide →Serve an LLM with vLLM
Validate a private endpoint before adding shared traffic, quotas and release gates.
Read guide →Connect a useful application
A responding model is not yet a reliable coding assistant or document system. Test the application contract: valid edits, relevant evidence, explicit permissions and human review. Audit embeddings, tools and fallback providers separately before calling a workflow offline.
Continue with Ollama
Configure YAML model roles and test chat, edits and autocomplete independently.
Read guide →Aider with Ollama
Limit repository context and accept changes through diffs and deterministic tests.
Read guide →Build a local RAG pipeline
Version chunks and embeddings, retrieve evidence first and evaluate unsupported-answer behavior.
Read guide →Find the failing layer
Match the symptom before changing anything. Connection refusal is not a missing model; a missing model is not a runner crash. Preserve logs and model provenance. Blanket cache deletion or opening a public port can make the situation worse.
Connection refused
Find the listener and caller network namespace before changing bindings.
Read guide →Model not found
Match exact tags to the actual server, service account and persistent store.
Read guide →Model loading failed
Use runner logs to separate unsupported architectures, allocation and storage errors.
Read guide →Client/server version mismatch
Identify and upgrade the process that really owns the endpoint.
Read guide →Size and measure the workload
Weights are only part of memory use. Context, active requests, caches and other applications change the capacity budget. Compare the same prompt, output budget and concurrency; keep cold loading separate from warm generation. No universal hardware throughput is assumed.
Out of memory
Separate RAM, VRAM, context and concurrent-work pressure.
Read guide →GPU not detected
Verify host support, container device access and actual model offload.
Read guide →Slow inference
Measure loading, input processing, generation and queueing separately.
Read guide →API timeouts
Distinguish connection, cold load, stream idle and overall deadlines.
Read guide →Context limits
Preserve instructions, evidence and output headroom within the effective context.
Read guide →