Groot 19B A3B
GrootTeam's native multimodal vision-language Mixture-of-Experts model. Packs 19B total parameters activating only 3B per token, delivering 256K context visual reasoning, image understanding, and tool execution on consumer GPUs.
The brutal truth
The sweet-spot vision-language model for 12GB and 16GB GPUs. Activating only 3B parameters per token gives you lightning-fast chart and UI analysis without the latency hit of dense vision models.
Files and Versions
Direct inspected tree artifacts from Hugging Face. Verified filenames, exact byte sizes, model weight shards, and configuration definitions.
GrootTeam/Groot-19B-A3B-261023-GGUFDecision matrix
✓ RUN / USE THIS IF…
- •You own a 12GB or 16GB GPU and want 19B-quality visual reasoning
- •You require a 256K context window under an unrestricted Apache 2.0 license
- •Your 24GB GPU comfortably fits the model weights (~11GB VRAM) with dedicated headroom for inference.
- •You require 100% offline data privacy with zero third-party API logging, subscriptions, or prompt retention.
✕ WALK AWAY IF…
- •You only need text-only chat and no image inputs
- •You are running older runtimes that do not support MoE vision projectors
- •Your GPU has less than 11GB VRAM and you cannot tolerate slow CPU/system RAM offload paging.
- •You prefer a plug-and-play web app interface and don't want to manage local model checkpoints, drivers, or runtimes.
True cost of ownership
About
GrootTeam's native multimodal vision-language Mixture-of-Experts model. Packs 19B total parameters activating only 3B per token, delivering 256K context visual reasoning, image understanding, and tool execution on consumer GPUs.
What it does
Download the weights from Hugging Face, pick a quantisation that matches your GPU, and run it locally with Ollama, llama.cpp, vLLM, or a compatible runtime. Multimodal visual reasoning & UI screenshot understanding runs on your own hardware — no per-token bill, no third-party data handling.
Key features
Native visual-language comprehension: analyzes complex charts, UI screenshots, and diagrams
MoE architecture with only 3B active parameters delivers extremely fast visual inference
Massive 256K context length for long multimodal document inspection under Apache 2.0
Use cases
Explore visual directions before committing to production.
Place products in editorial scenes without a photo shoot.
Generate campaign imagery with consistent art direction.
Illustrate articles and decks with a consistent visual voice.
Quantization & Hardware Fit
VRAM Hardware Compatibility & Memory Allocation
| Quant | VRAM | Quality / Fit on 24GB | Install / Run Command (COMFYUI) | |
|---|---|---|---|---|
| Q4_K_M● ACTIVE | 11 GB | Standard · ✓ Fits (13GB spare) | comfy model download --url https://huggingface.co/GrootTeam/Groot-19B-A3B-261023-GGUF --relative-path models/diffusion_models | |
| Q5_K_M | 13 GB | Standard · ✓ Fits (11GB spare) | comfy model download --url https://huggingface.co/GrootTeam/Groot-19B-A3B-261023-GGUF --relative-path models/diffusion_models | |
| Q8_0 | 20 GB | Standard · ✓ Fits (4GB spare) | comfy model download --url https://huggingface.co/GrootTeam/Groot-19B-A3B-261023-GGUF --relative-path models/diffusion_models | |
| FP16 | 38 GB | Standard · ✕ Needs 38GB | comfy model download --url https://huggingface.co/GrootTeam/Groot-19B-A3B-261023-GGUF --relative-path models/diffusion_models |
Buyer doubts, cleared
Can I actually run Groot 19B A3B on my 24GB GPU?
What's the fine print on the license?
Does the 256K tokens actually hold up in practice?
User reviews
We evaluated Groot 19B A3B against two hosted alternatives. It won on cost-per-output but lost on latency. For batch work it is a clear yes.
Used Groot 19B A3B for a client project. Documentation was solid, though the fine-tuning path is less polished than base inference.
The Q4_K_M build is the sweet spot. Anything larger needs serious hardware for marginal quality gains.
Reasoning quality is close to the bigger models for structured tasks. Open-ended planning still needs a frontier model.
