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Local LLM Guide

Model

Qwen3-VL 8B Instruct

Start with a supported compressed build and short context. 16GB RAM estimate. Best use: Screenshots, receipts, PDFs, web pages, interface agents, chart reading, and everyday visual reasoning tasks locally.

8.77BVision language

Best use

Screenshots, receipts, PDFs, web pages, interface agents, chart reading, and everyday visual reasoning tasks locally.

Memory planning

16GB RAM estimate

Expected pace

Depends on your hardware, app, and conversation length

License

Apache-2.0; read the model’s terms before use

Who should use this model?

  • Screenshots, receipts, PDFs, web pages, interface agents, chart reading, and everyday visual reasoning tasks locally.

Choose something else when

  • Avoid if you need tiny downloads, extreme speed, guaranteed OCR, or direct video support everywhere.

What hardware makes sense?

The download is only part of the memory needed. Leave room for the app, the input it processes, and other open programs. For chat models, context includes the instructions, conversation, and answer. Longer context needs more memory.

CPU only
A possible starting point with a compressed model and short conversation; check the exact file size.
8GB system memory
Memory or response time may limit use. Try a smaller model first.
16GB system memory
A possible starting point with a compressed model and short conversation; check the exact file size.
32GB system memory
A possible starting point with a compressed model and short conversation; check the exact file size.
8GB GPU memory
Memory or response time may limit use. Try a smaller model first.
12GB GPU memory
Memory or response time may limit use. Try a smaller model first.
24GB GPU memory
A possible starting point with a compressed model and short conversation; check the exact file size.
Apple unified memory
A possible starting point with a compressed model and short conversation; check the exact file size.

Model facts that change the decision

Parameters
8.77B
Input
Text input · Image input · Multi image input · Video input in supported runtimes · Text output
Published context limit
262,144 tokens
Model format
safetensors · GGUF · Ollama · MLX
Suggested compression
Q4_K_M for first local attempts unless a cited runtime recommends a different default.
Typical Q4 file
5.03GB before runtime and context overhead
Languages
Unknown

Using image input

This model needs an app or library that supports its image-processing components. Follow the exact model’s instructions for preparing image inputs. Some GGUF downloads also need a matching projector file; loading the text model alone does not establish image support.

First setup

Ollama

ollama run qwen3-vl:8b

LM Studio: Check that your installed app supports the exact model format and task before downloading it.

llama.cpp

Use the cited GGUF repository (Qwen/Qwen3-VL-8B-Instruct-GGUF) with current llama.cpp-compatible tooling.

Conditions that change the answer

  • Best overall local catalog recommendation for beginners who want advanced vision-language capability.
  • Ollama lists text and image input; video workflows may require Transformers, vLLM, qwen-vl-utils, or sampled frames.
  • GGUF sizes are for Q4_K_M and Q8_0; a Q5 size was not found in the cited sources.
  • Use dedicated OCR or human review for legal, financial, or high-stakes extraction.
  • Commercial use: Permitted under Apache-2.0 license, subject to license terms.
  • Qwen3-VL 8B is the best starter pick for advanced local vision. It reads screenshots, documents, images, and sampled videos well while staying small enough for many laptops and desktops with useful OCR-like extraction and reasoning.

Read the controlling license text →