Qwen3.8-2B-Distill-GGUF
GGUF quantizations of empero-ai/Qwen3.8-2B — a full-parameter distillation of Qwen3.8 2.4T A95B into the Qwen3.5-2B architecture, the smallest member of the family — for llama.cpp, Ollama, LM Studio, Jan, KoboldCpp, and other stock GGUF runtimes.
Params
—
Context
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Downloads 30d
772 K
Likes
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Download history
daily snapshots · 23 days772 K223 K
Aug 30Sep 6Sep 14Sep 21
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 |
|---|---|---|---|---|
| Qwen3.8-2B-Q4_K_M.gguf | Q4_K_M | 1.3 GB | 1.9 GB | ✅ Runs comfortably |
| Qwen3.8-2B-Q5_K_M.gguf | Q5_K_M | 1.5 GB | 2.1 GB | ✅ Runs comfortably |
| Qwen3.8-2B-Q6_K.gguf | Q6_K | 1.6 GB | 2.3 GB | ✅ Runs comfortably |
| Qwen3.8-2B-Q8_0.gguf | Q8_0 | 2.1 GB | 2.8 GB | ✅ Runs comfortably |
| Qwen3.8-2B-BF16.gguf | GGUF | 3.9 GB | 4.8 GB | ✅ Runs comfortably |
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$ ollama run qwen3-8-2b-distill-gguf # pin the quantization explicitly $ ollama run qwen3-8-2b-distill-gguf-q4_k_m
$ huggingface-cli download empero-ai/Qwen3.8-2B-Distill-GGUF-GGUF \
Qwen3.8-2B-Q4_K_M.gguf --local-dir .
$ llama-cli -m Qwen3.8-2B-Q4_K_M.gguf \
-c 8192 -ngl 99 -t 8 --color
$ curl -s https://aimodelscomparison.com/api/v1/models/qwen3-8-2b-distill-gguf
{
"hf_id": "empero-ai/Qwen3.8-2B-Distill-GGUF",
"params_b": null,
"context_length": null,
"license": { "id": "apache-2.0", "commercial": "yes" },
"downloads_30d": 771675,
"vram_estimates": [
{ "quant": "GGUF", "gb": 4.8 },
{ "quant": "Q4_K_M", "gb": 1.9 }
],
"updated_at": "2026-08-30T01:00:20Z"
}
Specifications
- Licence
- apache-2.0
- First seen on the Hub
- 2026-08-15
- Training datasets
- undisclosed
- Added to our catalog
- 2026-08-30
Compare with any text-generation model