DeepSeek R1 Distill Qwen 32B AWQ
Model Overview¶
This quantized model was created using AutoAWQ version 3.2.7.post3 with quant_config: { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM" }
DeepSeek-R1 and its distilled models represent a significant advancement in reasoning capabilities for LLMs by combining RL, SFT, and distillation. The DeepSeek-R1-Distill-Qwen-32B is a distilled version of the Qwen-32B large language model (LLM), optimized for efficient performance while retaining high-quality generative capabilities and is particularly suited for scenarios where computational efficiency is critical.
- Model Architecture: DeepSeek-R1-Distill-Qwen-32B is based on a transformer architecture, distilled from the larger Qwen-32B model to reduce computational requirements while maintaining competitive performance. The distillation process ensures that the model retains the core capabilities of the original model, making it suitable for a wide range of text generation tasks.
- Model Source: Valdemardi/DeepSeek-R1-Distill-Qwen-32B-AWQ
- License: apache-2.0
QPC Configurations¶
| Precision | SoCs / Tensor slicing | NSP-Cores (per SoC) | Full Batch Size | Chunking Prompt Length | Context Length (CL) | QPC URL | QPC Size | QPC Download | Onnx URL | Onnx Download | Generation Date |
|---|---|---|---|---|---|---|---|---|---|---|---|
| MXFP6 | 2 | 16 | 1 | 128 | 4096 | https://dc00tk1pxen80.cloudfront.net/SDK1.21.2/Valdemardi/DeepSeek-R1-Distill-Qwen-32B-AWQ/Valdemardi_DeepSeek-R1-Distill-Qwen-32B-AWQ_qpc_16cores_128pl_4096cl_1fbs_2devices_mxfp6_mxint8.tar.gz | 22GB | Download | https://dc00tk1pxen80.cloudfront.net/SDK1.21.2/Valdemardi/DeepSeek-R1-Distill-Qwen-32B-AWQ/Valdemardi_DeepSeek-R1-Distill-Qwen-32B-AWQ_ONNX.tar.gz | Download | 18-Mar-2026 |
Run This Model¶
Download QPCs¶
mkdir -p valdemardi/DeepSeek-R1-Distill-Qwen-32B-AWQ
cd valdemardi/DeepSeek-R1-Distill-Qwen-32B-AWQ
# Download QPC
wget <QPC_Download_URL>
tar xzvf <qpc_filename.tar.gz>
Run QPC¶
Replace QPC_PATH with actual extracted QPC directories.
python3 -m vllm.entrypoints.openai.api_server \
--port <PORT> \
--model valdemardi/DeepSeek-R1-Distill-Qwen-32B-AWQ \
--device-group <DEVICE_IDS> \
--max-model-len <CTX_LEN> \
--max-seq-len-to-capture <PREFILL_SEQ_LEN> \
--max-num-seqs <MAX_NUM_SEQS> \
--quantization mxfp6 \
--kv-cache-dtype mxint8 \
--override-qaic-config "num_cores:[num_cores] qpc_path:[qpc_path] ccl_enabled:True comp_ctx_lengths_prefill=[1024,2048,4096] comp_ctx_lengths_decode=[1024,2048,4096]"
Run Inference¶
Once the server is running, send a request to the OpenAI-compatible endpoint:
curl http://localhost:<PORT>/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "valdemardi/DeepSeek-R1-Distill-Qwen-32B-AWQ",
"messages": [
{"role": "user", "content": "<PROMPT>"}
],
"max_tokens": 200
}'