DeepSeek Releases Open-Source DeepInfer Inference Framework

DeepInfer

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_DECODE controls 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=compact and DEEPINFER_YOCO_PREFILL_KEEP_TOKENS=140 for 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=0 and pass a fixed seq_seed to SamplingParams (NVIDIA only).
• Structured outputs: constrain generation to a regex, JSON schema, or EBNF grammar. Pass so_args={"schema_type": ..., "schema": ...} to SamplingParams.

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

Getting started

Prerequisites

• Linux
• GCC >= 13.0
• Python 3.12
• CUDA 13 (NVIDIA only)
• CANN 9.2 (Ascend only)

Build from source

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.sh

Offline inference

The default checkpoint, 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.py

License

DeepInfer is released under the Apache License 2.0.