Best Local LLM Stack by Use Case 2026: Writing, Coding, RAG, Agents

Quick Decision: Stack by Hardware Tier (April 2026)

1. 1
   For 24 GB VRAM: `ollama pull llama3.3:70b` — highest quality, matches GPT-4 (2023) on writing benchmarks.
2. 2
   For 16 GB VRAM: `ollama pull mistral-small3.1` — 128K context, best quality under 24 GB.
3. 3
   For 8 GB VRAM: `ollama pull llama3.2:8b` — good writing quality, fast on consumer hardware.
4. 4
   Install OpenWebUI via Docker: `docker run -d -p 3000:8080 ghcr.io/open-webui/open-webui:latest`.
5. 5
   Configure context window (8K–32K tokens) in OpenWebUI settings based on document length.

1. 1
   Install vLLM: `pip install vllm`.
2. 2
   Start vLLM server with Qwen2.5-Coder-7B: `python -m vllm.entrypoints.openai.api_server --model Qwen/Qwen2.5-Coder-7B-Instruct --port 8000`.
3. 3
   For 16+ GB VRAM, use 14B: `--model Qwen/Qwen2.5-Coder-14B-Instruct`.
4. 4
   Connect IDE extension (VS Code Continue.dev, Cursor, etc.) to `http://localhost:8000/v1`.
5. 5
   Enable batch processing for code review: process 10 files in parallel via single API call (`vllm` supports batch=10 by default).

# Review 10 files in parallel using vLLM batch processing
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")

code_files = [
    ("utils.py", open("utils.py").read()),
    ("models.py", open("models.py").read()),
    # ... up to 10 files
]

# vLLM processes all 10 in parallel (1 batch request)
reviews = []
for filename, code in code_files:
    prompt = f"Review this code for bugs, style, and performance:

{code}"
    response = client.chat.completions.create(
        model="Qwen2.5-Coder-7B-Instruct",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.2,  # Deterministic for review tasks
    )
    reviews.append((filename, response.choices[0].message.content))

for filename, review in reviews:
    print(f"=== {filename} ===
{review}
")

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

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

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

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

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

Related Reading

• Best AI Coding Assistant for Local LLM — IDE choice for your coding stack (Cursor, Continue.dev, Cody).
• Best Local LLMs for Coding 2026 — Qwen2.5-Coder vs DeepSeek-Coder HumanEval rankings.
• Local RAG Setup 2026 — Complete LlamaIndex + Qdrant + Ollama implementation guide.
• Local LLM Agents with LangGraph — Agent workflow framework with step-by-step examples.
• Ollama vs LM Studio — Backend comparison: CLI vs GUI, speed, batch processing.
• Open WebUI vs SillyTavern — Chat UI comparison: professional vs roleplay.
• How much VRAM do local LLMs need? — Hardware requirements by model size and use case.

Common Mistakes When Choosing an LLM Stack

• Using Ollama for production API without vLLM: Ollama maxes out at <10 req/sec. For production serving 10+ concurrent users, vLLM is mandatory. Test throughput under load before deploying.
• Running LangGraph without vLLM backend: LangGraph agents make 10+ sequential LLM calls. Ollama introduces latency bottleneck. Always pair LangGraph with vLLM for sub-second round-trip times.
• Mixing Ollama + vLLM on same GPU without memory management: Both tools load weights into VRAM. Running two instances of 70B model consumes 32 GB. Use separate GPUs or quantize heavily (Q2) to fit both.
• Choosing wrong context window for writing: Default 4K context limits brainstorming sessions. For long-form writing, set 16K-32K context window in OpenWebUI. Trade-off: slower inference (2-3× slower per token).
• Assuming all backends are equally fast: vLLM + Ollama use different kernels. On same hardware, vLLM is 2-3× faster for inference. Speed difference is backend, not frontend (OpenWebUI, LM Studio are just UIs).

Sources

• Ollama GitHub — Official documentation, streaming API specification, and model library.
• vLLM GitHub — OpenAI API compatibility, batch processing, and continuous batching documentation.
• Qwen2.5-Coder Technical Report — Alibaba Qwen. 82% HumanEval score, specialized for coding tasks. Apache 2.0 licensed.
• LlamaIndex documentation — Document indexing, chunking, and RAG retrieval framework.
• LangGraph documentation — Agent workflow framework, state machines, tool use patterns.
• Qdrant documentation — Vector database for local embedding storage, Docker-ready, Apache 2.0.
• Continue.dev documentation — IDE extension for VS Code and JetBrains using local LLM backends.