DeepInfer is the high-performance inference framework that powers DeepSeek's large-scale production services. This open-source release provides offline inference with the core features needed for serving at scale.
Supported models:
- DeepSeek-V4.1-Flash
Supported features:
- Optimized kernels, model and sampler runtime
- Parallelism: Attention DP / balanced CP, MoE EP + EPLB
- DSpark and confidence-scheduled verification
- SWA bounded replay
- Asynchronous scheduling (zero-overhead batch scheduler)
- Batch-invariant inference
- Deterministic sampling
- Structured outputs
Feature details and configuration
- Optimized kernels: FlashMLA, DeepGEMM, DeepEP, TileKernels, DeepSelect, and custom TileLang kernels.
- DSpark and confidence-scheduled verification: accelerate decoding with draft tokens and use confidence estimates to schedule verification. Set
DEEPINFER_ENABLE_MTP_VARLEN=1;DEEPINFER_USE_NEXTN_DECODEcontrols the maximum draft length (default: 5). - Decoder SWA bounded replay: reduce decoder prefill work by replaying only a bounded suffix. Set
DEEPINFER_YOCO_PREFILL_TRUNCATION_MODE=compactandDEEPINFER_YOCO_PREFILL_KEEP_TOKENS=140for the default V4.1 settings; the retained suffix must cover the SWA window plus the maximum backtracking length. - Asynchronous scheduling: overlap CPU batch scheduling with device execution. Enabled by default.
- Batch-invariant inference: keep model results identical across different batch compositions. Enable with
DEEPINFER_BATCH_INVARIANT=1(NVIDIA only). - Deterministic sampling: produce identical outputs for identical input tokens and sampling parameters. Set
DEEPINFER_BATCH_INVARIANT=1 DEEPINFER_ENABLE_MTP_VARLEN=0and pass a fixedseq_seedtoSamplingParams(NVIDIA only). - Structured outputs: constrain generation to a regex, JSON schema, or EBNF grammar. Pass
so_args={"schema_type": ..., "schema": ...}toSamplingParams; see examples.
Supported hardware:
- NVIDIA SM100 GPUs
- Huawei Ascend 950 NPUs
Required for a complete inference service (not included in this release):
- A KV cache system
- VE/Prefill/Decode disaggregation (most required engine functionality is already implemented)
- Load balancing across data-parallel replicas for both prefill and decode
- Linux
- GCC >= 13.0
- Python 3.12
- CUDA 13 (NVIDIA only)
- CANN 9.2 (Ascend only)
Before building DeepInfer, please create and activate an empty Python 3.12 environment.
# Use `ascend` instead for the Ascend platform
# Keep it exported in the environment where you run DeepInfer
export DEEPINFER_ACCELERATOR=nvidia
./build.shThe default checkpoint, deepseek-ai/DeepSeek-V4.1-Flash-1017, is
downloaded into the Hugging Face cache on the first run; set DEEPINFER_HF_MODEL_DIR=/path/to/DeepSeek-V4.1-Flash-1017 to
run from a local copy instead.
python examples/offline_inference.pySee examples for details.
| Prefill | Decode |
|---|---|
 |
 |
Measured on 4 SM100 GPUs for prefill and 64 for decode — each within one scale-up domain. To reproduce the benchmarks, see run_sweep.sh.
See eval/README.md.
DeepInfer is released under the Apache License 2.0. See LICENSE for details.
DeepInfer was originally built primarily on vLLM, with additional inspiration from SGLang and TensorRT-LLM. We thank their developers.
@misc{deepinfer2026,
title = {DeepInfer: High-performance, large-scale inference framework for DeepSeek models},
author = {DeepSeek-AI},
year = {2026},
publisher = {GitHub},
howpublished = {\url{https://github.com/deepseek-ai/DeepInfer}},
}