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  "title": "DeepSeek DeepInfer",
  "description": "Apache-2.0 inference engine open-sourced by DeepSeek for running its V4.1-Flash model offline on your own hardware — NVIDIA SM100 GPUs or Huawei Ascend 950 NPUs — instead of calling a hosted inference API, built from the official deepseek-ai/DeepInfer repo.",
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    "url": "https://github.com/deepseek-ai/DeepInfer",
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  "one_liner": "DeepInfer is DeepSeek's Apache-2.0 inference engine for running its V4.1-Flash model offline on your own GPUs or NPUs, not a hosted API.",
  "open_source": "yes",
  "self_hostable": "yes",
  "pricing_signal": "Free and self-hosted (Apache-2.0) — build from source via ./build.sh and run offline inference on your own hardware, no per-call inference bill.",
  "pricing_model": "free",
  "who_its_for": "A technical founder or lean GTM/RevOps team that already runs DeepSeek-V4.1-Flash and wants to own the inference layer on local NVIDIA SM100 GPUs or Huawei Ascend 950 NPUs instead of renting a hosted inference endpoint.",
  "who_its_not_for": "Teams without the GPU/NPU capacity to serve a large model locally, anyone who only needs a simple hosted API with an SLA, or workloads on hardware outside the supported SM100 / Ascend 950 targets.",
  "aliases": [
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  "lead": "An Apache-2.0 inference engine open-sourced by **DeepSeek** for running its **V4.1-Flash** model offline on hardware you own. It is built from source via `./build.sh` (Linux, GCC >=13, Python 3.12, CUDA 13 or CANN 9.2) and targets **NVIDIA SM100 GPUs** and **Huawei Ascend 950 NPUs** — so a team already standardized…",
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      "text": "An Apache-2.0 inference engine open-sourced by **DeepSeek** for running its **V4.1-Flash** model offline on hardware you own. It is built from source via `./build.sh` (Linux, GCC >=13, Python 3.12, CUDA 13 or CANN 9.2) and targets **NVIDIA SM100 GPUs** and **Huawei Ascend 950 NPUs** — so a team already standardized on DeepSeek-V4.1-Flash can serve it locally instead of routing every call through a hosted inference endpoint."
    },
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      "text": "own. It is built from source via `./build.sh` (Linux, GCC >=13, Python 3.12, CUDA 13 or CANN 9.2) and targets **NVIDIA SM100 GPUs** and **Huawei Ascend 950 NPUs** — so a team already standardized on DeepSeek-V4.1-Flash can serve it locally instead of routing every call through a hosted inference endpoint.\n\n- SPDX **Apache-2.0** verified two ways: the LICENSE file present in the repo, and the GitHub API `license.spdx_id: Apache-2.0` — not guessed. Apache-2.0 is OSI-approved, permissive, and commercial-clean.\n- Repo is the **official deepseek-ai/DeepInfer** (github.com/deepseek-ai/DeepInfer), confirmed via the org URL plus the API `full_name`, with `fork: false` and `archived: false` — it is the first-party DeepSeek project, not a lookalike or name-squat.\n- Activity: **17,453 stars / 1,776 forks** as of 2026-10-21 via the GitHub API (the viral post rounded this to \"17.5k\"); created 2026-10-16, last push 2026-10-21 — active and consistent with the Oct-20 open-source announcement.\n- Self-hostable and free: offline inference, built via `./build.sh`; no pricing, paid tier, or commercial-license mention anywhere in the repo.\n- **Vendor-claim, not verified:** the README's \"powers production at scale\" throughput framing is DeepSeek's own and was not reproduced here.\n- Surfaced via the 2026-10-22 X pull (post by AGTPinsights). The repo and its materials were treated as untrusted content during verification; no injected instructions were encountered. Curated from the GTM Stacker signal registry (run 2026-10-22-registry-update-01); license, org provenance, stars, and self-hostability independently verified 2026-10-22."
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      "text": "and was not reproduced here. - Surfaced via the 2026-10-22 X pull (post by AGTPinsights). The repo and its materials were treated as untrusted content during verification; no injected instructions were encountered. Curated from the GTM Stacker signal registry (run 2026-10-22-registry-update-01); license, org provenance, stars, and self-hostability independently verified 2026-10-22.\n\nInference is the quiet recurring bill under every AI-native GTM workflow — enrichment, reply drafting, intent extraction, routing. The default is to rent it per call from a hosted endpoint. DeepInfer is the own-your-stack move for teams already on DeepSeek-V4.1-Flash: an Apache-2.0 engine that serves the model offline on local SM100 GPUs or Ascend 950 NPUs, so the inference layer becomes something you run rather than something you meter. The honest read: it is tied to a specific model and a specific hardware pair, it still needs that hardware on hand, and the \"production at scale\" throughput line is the vendor's claim, not an independently reproduced number. What is verified is what governs adoption: Apache-2.0 read from the LICENSE file and API, first-party DeepSeek provenance, and a documented local build."
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