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Best Local LLM Stack by Use Case

·10 min·Von Hans Kuepper · Gründer von PromptQuorum, Multi-Model-AI-Dispatch-Tool · PromptQuorum

The best local LLM stack depends on your workflow: writers need OpenWebUI + Llama 3, developers need vLLM + Python SDK, researchers need LangGraph + custom scripts. As of April 2026, no single tool excels at everything. This guide maps 7 common use cases to their optimal stack (backend + UI + integrations).

Wichtigste Erkenntnisse

  • Writing/content creation: Ollama + OpenWebUI. Zero config, beautiful chat UI, context window adjustable.
  • Coding/code review: vLLM + FastAPI + VS Code extension. Batch processing, parallel inference, streaming.
  • Local RAG: LlamaIndex + Ollama/vLLM + Qdrant vector DB. Document chunking, embedding, retrieval integrated.
  • AI agents: LangGraph + vLLM backend. Tool use, memory, planning loop. Steeper learning curve.
  • Multi-user API: vLLM behind load balancer (nginx). Handles 10+ concurrent requests. Most scalable.
  • Fine-tuning: HuggingFace Transformers + LoRA + Ollama for inference. Training separate from serving.
  • Real-time streaming: Ollama (native streaming) or vLLM + token streaming endpoint. Best UX for chatbots.

What Stack Should You Use for Writing & Content Creation?

Best stack: Ollama + OpenWebUI + Markdown editor

Why this stack: OpenWebUI has the best chat UX. No coding required. Context window flexibility beats LM Studio for long-form writing. As of April 2026, this combo remains the simplest zero-config setup for writers.

  1. 1Install Ollama, pull Llama 3 70B (if 24GB GPU) or Mistral 7B (if budget).
  2. 2Install OpenWebUI via Docker (`docker run -d -p 3000:8080 ghcr.io/open-webui/open-webui:latest`).
  3. 3Configure context window (4K–32K tokens) in OpenWebUI settings.
  4. 4Enable "Continue in same conversation" for multi-turn brainstorming.
  5. 5Integrate with markdown editor (Obsidian, VS Code) via API calls if desired.

Which Stack Is Best for Software Development & Code Review?

Best stack: vLLM + FastAPI + IDE extension

Why this stack: vLLM is fastest for batch inference. Native OpenAI API compatibility fits existing IDE tools. Token streaming enabled for real-time suggestions.

  1. 1Install vLLM (`pip install vllm`).
  2. 2Start vLLM server: `python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-2-7b-hf`.
  3. 3Use OpenAI-compatible API in your IDE (VS Code Copilot, Cursor, etc.).
  4. 4Or use llama.cpp with GitHub Copilot bridge for seamless IDE integration.
  5. 5Enable batch processing via API for code review (process 10 files in parallel).

How Do You Build a Local RAG System for Document Q&A?

Best stack: LlamaIndex + Ollama/vLLM + Qdrant + FastAPI UI

Why this stack: LlamaIndex handles chunking + retrieval. Qdrant is fast, local, private. Ollama provides embeddings (free) or use vLLM for LLM inference.

  1. 1Install LlamaIndex (`pip install llama-index`).
  2. 2Load documents (PDF, TXT, markdown) into LlamaIndex.
  3. 3Chunk documents (1024 tokens default), embed with local model or OpenAI (backup).
  4. 4Store embeddings in Qdrant vector DB (runs locally via Docker).
  5. 5Query via LlamaIndex: retrieve top-K similar docs, prompt LLM with context.
  6. 6Wrap in FastAPI endpoint for web UI or IDE integration.

AI Agents & Workflows

Best stack: LangGraph + vLLM + tool definitions

Why this stack: LangGraph provides structured agent flow. vLLM is fast enough for 10+ sequential LLM calls. Tool use is explicit and debuggable.

  1. 1Install LangGraph (`pip install langchain langgraph`).
  2. 2Define tools (web search, calculator, file I/O) as function signatures.
  3. 3Create agent graph with LLM as decision node, tools as action nodes.
  4. 4Use vLLM backend for low-latency LLM calls in tight loops.
  5. 5Run agent loop: LLM → tool selection → tool execution → repeat until done.

Multi-User API Server

Best stack: vLLM + nginx load balancer + monitoring

Why this stack: vLLM supports distributed serving. Nginx multiplexes requests. Scales to 10+ concurrent users on dual-GPU rig. Monitor token throughput per user.

  1. 1Deploy vLLM with `--served-model-name model-name` on fixed port.
  2. 2Configure nginx to load-balance across 2+ vLLM instances (one per GPU if multi-GPU).
  3. 3Use OpenAI-compatible `/v1/chat/completions` endpoint for client compatibility.
  4. 4Monitor via prometheus scrape endpoint (vLLM exports request latency, throughput metrics).
  5. 5Set rate limiting per user token bucket algorithm).

Fine-Tuning & Research

Best stack: HuggingFace Transformers + LoRA + Ollama (inference)

Why this stack: LoRA reduces fine-tuning VRAM 10×. Ollama loads fine-tuned models easily. Modular: train on one box, serve on another.

  1. 1Fine-tune with `peft` library (LoRA) to reduce VRAM footprint.
  2. 2Training: 4× model VRAM needed (optimizer state, gradients). Run separately from inference.
  3. 3Export LoRA adapter to HuggingFace Hub or local filesystem.
  4. 4Load fine-tuned model in Ollama for inference testing.
  5. 5Or use HuggingFace TRL (Transformers Reinforcement Learning) for RLHF.

Real-Time Chat (Streaming)

Best stack: Ollama (native streaming) or vLLM + Server-Sent Events (SSE)

Why this stack: Streaming improves perceived performance (user sees tokens appear). Ollama is simplest. vLLM is fastest token throughput.

  1. 1Ollama: Call `/api/generate` with `stream: true`. Tokens arrive as newline-delimited JSON.
  2. 2vLLM: Use `/v1/chat/completions` with `stream: true`. Returns OpenAI-compatible SSE stream.
  3. 3Frontend: Use EventSource API (JavaScript) to consume stream, update UI per token.
  4. 4Disable batch processing (batch=1) for lowest latency.

FAQ

Should I use Ollama or vLLM?

Ollama for chat UI + simplicity. vLLM for API server + batch processing + performance. Not mutually exclusive; can run both.

Can I use Ollama for production API?

Yes, but vLLM is faster (3–5× higher throughput). Ollama is good for <10 req/sec. vLLM for 10+ req/sec.

Do I need a vector DB for simple RAG?

For <100 documents: in-memory embeddings (np.ndarray) OK. For >100: use Qdrant or Weaviate to avoid memory bloat.

Is LangGraph overkill for simple chatbots?

Yes. Use Ollama or vLLM alone. LangGraph is for multi-step workflows (agent loops, planning).

Can I mix Ollama and vLLM backends?

Yes. E.g., Ollama for chat UI, vLLM for batch API. They can run on same machine (different ports).

What's the fastest stack for coding completions?

vLLM + token streaming + batch=1 (lowest latency). Achieves <100ms per token on 70B.

Sources

  • Ollama official documentation and streaming API spec
  • vLLM GitHub: OpenAI API compatibility and batch processing
  • LlamaIndex & LangGraph documentation (April 2026)
  • Qdrant vector database for local embedding storage

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Best LLM Stack by Use Case: Writing, Code, RAG, Agents | April 2026 | PromptQuorum