Qwen3-30B-A3B-abliterated
This is an uncensored version of Qwen/Qwen3-30B-A3B created with a new abliteration technique. See this article to know more about abliteration.
Params
30.5 B
Context
40,960
Downloads 30d
459 K
Likes
41
Download history
daily snapshots · 10 days459 K448 K
Jul 28Jul 31Aug 3Aug 6
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | f32 | 183.2 GB | 206.6 GB | ❌ Won’t fit |
| model.safetensors (bf16, full) | bf16 + 40K ctx | 183.2 GB | 224.9 GB | ❌ Won’t fit |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Run it
copy-paste, exact tags checked against the Hub$ curl -s https://aimodelscomparison.com/api/v1/models/qwen3-30b-a3b-abliterated
{
"hf_id": "mlabonne/Qwen3-30B-A3B-abliterated",
"params_b": 30.53,
"context_length": 40960,
"license": { "id": "apache-2.0", "commercial": "yes" },
"downloads_30d": 458937,
"vram_estimates": [
{ "quant": "f32", "gb": 206.6 }
],
"updated_at": "2026-07-28T18:05:31Z"
}
Specifications
- Architecture
- Qwen3MoeForCausalLM
- Parameters
- 30.5 B
- Tensor type
- F32
- Context length
- 40,960
- Vocabulary
- 151,936
- Layers / heads
- 48 / 32
- Licence
- apache-2.0
- First seen on the Hub
- 2025-04-30
- Base model
- Qwen3-30B-A3B
- Training datasets
- undisclosed
- Added to our catalog
- 2026-07-28
Compare with
Sponsored · GPU cloud
Not enough VRAM?
Spin up a 24 GB L4 instance in 40 seconds. $0.44/hr.