01What it can do
- Versatile multilingual dense 28B
- 262k native context
- Responses without guardrails (uncensored use)
- Permissive Apache 2.0 license
- —~16 GB VRAM in Q4: 16–24 GB GPU recommended
- —Abliteration = possible loss of alignment and safety
- —No Ollama tag — install via Hugging Face
04Install
Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.
02Required memory
Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.
What hardware do you need for Qwen 3.8 27B Obliterated?
To run Qwen 3.8 27B Obliterated locally with Q4 quantization, you need about 16 GB of VRAM. An option to compare: GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) — leave some headroom for the system and context; check engine compatibility with the GPU.
Why this choice? Our complete guide on GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) →
Affiliate links — possible commission at no extra cost to you; independent recommendations. As an Amazon Associate, BestLLMfor earns from qualifying purchases.
On the go: Qwen 3.8 27B Obliterated also runs on a RTX laptop PC (16 GB of VRAM) →
The AI that actually answers: an uncensored model running locally, without abusive refusals on health, history, security or writing — legal framework included.
- Lifetime online access
- PDF + files
- Lifetime updates
03Expected speed
Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.