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DeepInfer is the inference framework DeepSeek uses for its large-scale production services. The open-source release covers offline inference and the core features needed for serving at scale. It currently supports one model, DeepSeek-V4.1-Flash. It was originally built mainly on vLLM, with some ideas from SGLang and TensorRT-LLM.
It includes optimized kernels (FlashMLA, DeepGEMM, DeepEP, TileKernels, DeepSelect and custom TileLang kernels). Parallelism options are attention data parallelism / balanced context parallelism and MoE expert parallelism with load balancing (EPLB). Other features include DSpark decoding with confidence-scheduled verification, SWA bounded replay, asynchronous batch scheduling, batch-invariant inference, deterministic sampling, and structured outputs constrained by regex, JSON schema or EBNF grammar. Batch-invariant inference and deterministic sampling are NVIDIA-only.
It is built from source on Linux (GCC 13 or newer, Python 3.12, CUDA 13 for NVIDIA or CANN 9.2 for Ascend). The default checkpoint is downloaded from Hugging Face on first run. The release is not a complete serving stack: the README lists a KV cache system, prefill/decode disaggregation and load balancing across data-parallel replicas as required but not included. Per a YouTube creator's summary, DeepSeek's reported production figures (such as cache hit rate and margins) rely on these unreleased components.
Watch the moment: the exact second top creators show it. Videos load only when you press play, via youtube-nocookie.com.
Open-sourced inference engine used to serve DeepSeek models at scale. DeepSeek reported 155T tokens/day per cluster, $38 revenue per GPU hour, about 80% margin and a 96% cache hit rate; it runs on Nvidia and Huawei Ascend chips, but the full caching and load-balancing stack isn't released.
Closest neighbours by meaning, not just by category.
Model · DeepSeek
Chinese AI lab behind the open-weight DeepSeek-V4 and DeepSeek-R1 models, with a chat app and low-cost API.
Library
Open-source streaming MoE inference framework for running large sparse models on laptops, phones and single-GPU PCs.
Model · DeepSeek
DeepSeek's open-weights reasoning LLM (671B MoE, 37B active) trained with large-scale reinforcement learning.
Model · DeepSeek
Open-weights Flash-tier model in DeepSeek's V4 family, offered via chat and a low-cost API.
Model · DeepSeek
DeepSeek's V4 Pro large language model, available via API and as an open model, with low per-token pricing.
Model · DeepSeek
Experimental vision-enabled version of DeepSeek V4 Flash with image understanding; open weights under MIT, now retired on the API.
