AutoJev — local AI model by denis-pplx
Written by Jakub Rusinowski · Last updated
A 27B multimodal decision model on Qwen3.8-27B, the highest-scoring downloadable model on the community Decision Index as of 28 Sep 2026.
Variants
The smallest AutoJev variant needs about 18 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache.
| Model | VRAM at Q4 | VRAM | Context | Run it |
|---|---|---|---|---|
| AutoJev 27B → 27B | ~17.6 GB | Not published | autojev-serve :8000 |
Memory is quantized weights plus overhead at Q4_K_M, from the same engine as the GPU & VRAM checker.
How to run AutoJev locally
Install Ollama, then pull the tag.
Served by autojev-serve (default port 8000). This model does not run in Ollama.
Pick a size above for its own VRAM figure, speed estimate and install command.
Licence
Commercial use permitted. No usage restrictions beyond attribution.
Applies to: AutoJev 27BRecommended GPU
The cheapest catalogued GPU that runs AutoJev locally (min 18 GB VRAM) is the AMD Radeon RX 7900 XT (20 GB).
AutoJev — frequently asked questions
How much VRAM does AutoJev need?
AutoJev needs about 18 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: AutoJev 27B (18 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Can I run AutoJev on an RTX 4090 (24 GB)?
Yes — AutoJev runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4_K_M.
What quantization should I use for AutoJev?
Q4_K_M is the best balance of quality and VRAM for AutoJev in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.
How do I run AutoJev locally?
AutoJev is a decision model: it is called over an HTTP endpoint (/v1/systemone), not chatted with. Where a variant is available in Ollama 0.35 or newer, pull it with `ollama pull` and send requests to the local server; the others ship their own server. Each variant page shows the exact commands for that model.