DeepInfer

What the public engine contains

The repository documents optimized kernels, attention data/context parallelism, expert parallelism and load balancing, speculative decoding with confidence-scheduled verification, bounded replay, asynchronous scheduling and structured outputs. The default offline example uses deepseek-ai/DeepSeek-V4.1-Flash-1017; the framework, model checkpoint and hosted API are separate objects.

Prerequisites before trying offline inference

The documented prerequisites are Linux, GCC 13 or newer and Python 3.12, with CUDA 13 for NVIDIA or CANN 9.2 for Ascend. Choose the accelerator before building, then follow the repository’s offline example. This guide has not installed the engine, measured GPU memory or executed a checkpoint; community ports and unmerged pull requests do not expand the official support list.

Why this is not a complete hosted service

The README explicitly excludes a complete KV-cache system, visual-encoder/prefill/decode disaggregation and load balancing across data-parallel replicas. Much engine functionality can exist without a deployable end-to-end service. Hosted DeepSeek API prices, quotas and access remain their own contract.

• DeepSeek V4.1 Flash

  Model weights, hosted API and checkpoint distinctions.

• DeepSeek

  Company and current service boundaries.

How to read performance evidence

DeepSeek’s performance figures specify hardware and scale-up conditions, and its evaluation guide describes generation, scoring and coverage. They are reference results rather than independent GetLLMs measurements. Compare the same hardware, checkpoint, prompts, sampling settings and complete task coverage; an offline throughput number does not establish production latency or total serving cost.

Useful first jobs

• Inspecting DeepSeek’s public offline serving engine.
• Planning an inference stack around a specific accelerator.
• Reproducing scoped reference evaluation configurations.