MiniCPM-SALA-AWQ-8bit
> [!NOTE] > ### 🏆 2026 Sparse Operator Acceleration & Race (SOAR) is Now Live! > > "The MiniCPM-SALA architecture is just the beginning. Realizing its full potential requires deep system-level synergy and cross-layer compilation optimization." > > In collaboration with SGLang and NVIDIA, OpenBMB invites global geeks to push the boundaries of 9B-scale, 1M-token inference on NVIDIA 6000D. > > 💰 Prize Pool: >$100,000...
Params
3.1 B
Context
524,288
Downloads 30d
616 K
Likes
0
Download history
daily snapshots · 41 days
▲ 186 K in the last 30 days (43.3%)
619 K451 K
Aug 23Sep 2Sep 12Sep 21
619 K166 K
Aug 12Aug 25Sep 8Sep 21
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | i32 | 10.6 GB | 12.7 GB | ✅ Runs comfortably |
| model.safetensors (bf16, full) | bf16 + 512K ctx | 10.6 GB | 42.0 GB | ❌ Won’t fit |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Run it
copy-paste, exact tags checked against the Hub$ curl -s https://aimodelscomparison.com/api/v1/models/minicpm-sala-awq-8bit
{
"hf_id": "cyankiwi/MiniCPM-SALA-AWQ-8bit",
"params_b": 3.10,
"context_length": 524288,
"license": { "id": "apache-2.0", "commercial": "yes" },
"downloads_30d": 615964,
"vram_estimates": [
{ "quant": "i32", "gb": 12.7 }
],
"updated_at": "2026-08-12T01:00:35Z"
}
Specifications
- Architecture
- MiniCPMSALAForCausalLM
- Parameters
- 3.1 B
- Tensor type
- I32
- Context length
- 524,288
- Vocabulary
- 73,448
- Layers / heads
- 32 / 32
- Licence
- apache-2.0
- First seen on the Hub
- 2026-02-15
- Training datasets
- undisclosed
- Added to our catalog
- 2026-08-12
Compare with any text-generation model