Audience
Developers and infrastructure teams deploying or evaluating DeepSeek model inference on supported accelerator hardware.
About DeepInfer
DeepInfer is an open-source inference framework for running DeepSeek language models on Linux systems with supported NVIDIA or Huawei Ascend accelerators. It provides offline inference and runtime components for model execution, optimized kernels, distributed parallelism, asynchronous batch scheduling, speculative decoding, deterministic generation, and structured outputs such as JSON Schema, regular expressions, and EBNF formats. The project documents DeepSeek-V4.1-Flash support and includes build instructions, configuration guidance, examples, and evaluation tools. It is intended for developers and infrastructure teams building or evaluating self-managed inference deployments. Its repository notes that production services may require additional KV-cache, prefill/decode disaggregation, and load-balancing components not included in this release.
Other Popular Alternatives & Related Software
ModelArk
ModelArk is ByteDance’s one-stop large model service platform, providing access to cutting-edge AI models for video, image, and text generation. With powerful options like Seedance 1.0 for video, Seedream 3.0 for image creation, and DeepSeek-V3.1 for reasoning, it enables businesses and developers to build scalable, AI-driven applications. Each model is backed by enterprise-grade security, including end-to-end encryption, data isolation, and auditability, ensuring privacy and compliance. The platform’s token-based pricing keeps costs transparent, starting with 500,000 free inference tokens per LLM and 2 million tokens per vision model. Developers can quickly integrate APIs for inference, fine-tuning, evaluation, and plugins to extend model capabilities. Designed for scalability, ModelArk offers fast deployment, high GPU availability, and seamless enterprise integration.
Learn more about ModelArk
DeepSeek-V2
DeepSeek-V2 is a state-of-the-art Mixture-of-Experts (MoE) language model introduced by DeepSeek-AI, characterized by its economical training and efficient inference capabilities. With a total of 236 billion parameters, of which only 21 billion are active per token, it supports a context length of up to 128K tokens. DeepSeek-V2 employs innovative architectures like Multi-head Latent Attention (MLA) for efficient inference by compressing the Key-Value (KV) cache and DeepSeekMoE for cost-effective training through sparse computation. This model significantly outperforms its predecessor, DeepSeek 67B, by saving 42.5% in training costs, reducing the KV cache by 93.3%, and enhancing generation throughput by 5.76 times. Pretrained on an 8.1 trillion token corpus, DeepSeek-V2 excels in language understanding, coding, and reasoning tasks, making it a top-tier performer among open-source models.
Learn more about DeepSeek-V2
Wafer
Wafer delivers the fastest open source LLMs for enterprise through serverless and dedicated inference built for production AI workloads. Its serverless inference gives teams access to top open models with no infrastructure, no deployment overhead, and fast APIs, including GLM-5.2-Fast for low-latency inference with EAGLE speculative decoding and a per-stream throughput SLA, GLM-5.2 as a flagship model with stronger coding and reasoning capabilities, and more. Wafer’s technology uses agents that optimize inference across the stack, identifying and enhancing bottlenecks in orchestration, algorithms, serving engines, GPU kernels, and diverse hardware. It profiles the stack to see whether latency or throughput comes from scheduling, decoding, kernels, memory pressure, or hardware fit, then tries many paths and ships the measured winner. Instead of relying on a single switch or heuristic, Wafer searches model, engine, kernel, and hardware combinations.
Learn more about Wafer
NVIDIA TensorRT
NVIDIA TensorRT is an ecosystem of APIs for high-performance deep learning inference, encompassing an inference runtime and model optimizations that deliver low latency and high throughput for production applications. Built on the CUDA parallel programming model, TensorRT optimizes neural network models trained on all major frameworks, calibrating them for lower precision with high accuracy, and deploying them across hyperscale data centers, workstations, laptops, and edge devices. It employs techniques such as quantization, layer and tensor fusion, and kernel tuning on all types of NVIDIA GPUs, from edge devices to PCs to data centers. The ecosystem includes TensorRT-LLM, an open source library that accelerates and optimizes inference performance of recent large language models on the NVIDIA AI platform, enabling developers to experiment with new LLMs for high performance and quick customization through a simplified Python API.
Learn more about NVIDIA TensorRT
Pricing
Free Version:
Free Version available.
Integrations
API:
Yes, DeepInfer offers API access
No integrations listed.
Company Information
DeepSeek
Founded: 2023
China
github.com/deepseek-ai/DeepInfer
Videos and Screen Captures
Other Useful Business Software
Go from Code to Production URL in Seconds
Skip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
Product Details
Platforms Supported
Linux
On-Premises
Training
Documentation
DeepInfer Frequently Asked Questions
Q: What kinds of users and organization types does DeepInfer work with?
DeepInfer works with these users and organization types: Mid Size Business, Small Business, Enterprise, Freelance, Nonprofit, Government, and Startup.
Q: What languages does DeepInfer support in their product?
DeepInfer supports these languages: English.
Q: Does DeepInfer have an API?
Yes, DeepInfer has an API.
Q: What type of training does DeepInfer provide?
DeepInfer provides training in the form of documentation.
