Model Overview
- Model Architecture: DeepseekV41ForCausalLM
- Input: Text, Image
- Output: Text
- Supported Hardware Microarchitecture: AMD MI355 / MI350 (gfx950)
- ROCm: 7.2.0
- PyTorch: 2.12.0
- Transformers: 5.17.0
- Operating System(s): Linux
- Inference Engine: vLLM
- Model Optimizer: AMD-Quark (v0.13.0)
- Quantized layers:
experts, shared_experts, self_attn, and engram.embed are quantized to PTPC-FP8. The vision part is not quantized.
Model Quantization
Quantized from deepseek-ai/DeepSeek-V4.1-Flash
with AMD Quark. The source MXFP4
and MXFP8 weights are converted to PTPC-FP8: experts,
shared_experts, self_attn, and engram.embed are quantized with PTPC-FP8.
The vision tower is kept in its original precision.
Quantization script
MODEL_DIR="deepseek-ai/DeepSeek-V4.1-Flash"OUTPUT_DIR="amd/DeepSeek-V4.1-Flash-Quark-PTPC-FP8" HIP_VISIBLE_DEVICES=0 \QUARK_MXFP4_IMPL=triton \python3 Quark/examples/torch/language_modeling/llm_ptq/quantize_quark.py \ --model_dir "${MODEL_DIR}" \ --output_dir "${OUTPUT_DIR}" \ --quant_scheme ptpc_fp8 \ --layer_quant_scheme "*.engram.embed" ptpc_fp8 \ --quant_flow file2file \ --model_export hf_format \ --keep_excluded_layers_as_original_model_state \ --exclude_layers \ "embed" \ "*head*" \ "*ffn.gate*" \ "*attn.compressor*" \ "*attn.indexer.weights_proj*" \ "*attn.indexer.wk*" \ "*hc_*" \ "*vision*" \ "*aligner*"
Deployment
Use with vLLM
This model can be deployed efficiently using the vLLM backend based on the Docker image vllm/vllm-openai-rocm:nightly-eed1f3d0c6043bd494424a22443ee198dd56f657.
Required vLLM patches: #57760, #57071.
Evaluation
The model was evaluated on GSM8K (8-shot) using the vLLM framework.
Accuracy
Table with columns: Benchmark, deepseek-ai/DeepSeek-V4.1-Flash, amd/DeepSeek-V4.1-Flash-Quark-PTPC-FP8 (this model), Recovery| Benchmark | deepseek-ai/DeepSeek-V4.1-Flash | amd/DeepSeek-V4.1-Flash-Quark-PTPC-FP8 (this model) | Recovery |
|---|
| GSM8K (8-shot, flexible-extract) | 92.95 | 92.95 | 100.0% |
GSM8K is measured with lm-eval. The base and quantized models are run with the
same framework, settings, and host.
Reproduction
The evaluation runs against a vLLM server, using the Docker image
vllm/vllm-openai-rocm:nightly-eed1f3d0c6043bd494424a22443ee198dd56f657.
Launching server
export HIP_VISIBLE_DEVICES=0,1,2,3export VLLM_ROCM_USE_AITER=1export VLLM_ROCM_USE_AITER_MOE=1export VLLM_ROCM_USE_AITER_MHA=1export SAFETENSORS_FAST_GPU=1export VLLM_MOE_SKIP_PADDING=0vllm serve amd/DeepSeek-V4.1-Flash-Quark-PTPC-FP8 \ --tensor-parallel-size 4 \ --trust-remote-code \ --moe-backend aiter \ --max-model-len 8192 \ --gpu-memory-utilization 0.90 \ --port 8008
Evaluating GSM8K in a new terminal
lm_eval \ --model local-completions \ --tasks gsm8k \ --model_args "model=amd/DeepSeek-V4.1-Flash-Quark-PTPC-FP8,base_url=http://127.0.0.1:8008/v1/completions,num_concurrent=32,max_retries=5,timeout=1800,tokenized_requests=False,tokenizer_backend=None,max_length=8192" \ --gen_kwargs "max_gen_toks=4096" \ --num_fewshot 8 \ --log_samples \ --output_path /tmp/gsm8k-dsv41-ptpc-fp8
License
This model is a quantized derivative of
deepseek-ai/DeepSeek-V4.1-Flash
and is distributed under the same license as the source model: the
MIT License.
A copy of the upstream LICENSE is included in this repository.
Modifications Copyright (c) 2026 Advanced Micro Devices, Inc. All rights reserved.
AMD has modified selected language-model weights by quantizing them to PTPC-FP8
with AMD Quark; the modifications are provided under the same MIT License and
are not subject to any separate or different license.