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About

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.

About

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.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Machine learning engineers and data scientists seeking a tool to optimize their deep learning operations

Audience

AI infrastructure and product teams that need faster, production-ready inference for open LLMs without managing the full optimization stack

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

NVIDIA
Founded: 1993
United States
developer.nvidia.com/tensorrt

Company Information

Wafer
United States
www.wafer.ai/

Alternatives

OpenVINO

OpenVINO

Intel

Alternatives

Photon

Photon

Moondream

Categories

Categories

Integrations

Dataoorts GPU Cloud
Kimi K2
Kimi K2.5
Kimi K2.6
Kimi K3
MATLAB
NVIDIA AI Enterprise
NVIDIA Clara
NVIDIA DeepStream SDK
NVIDIA Jetson
NVIDIA Merlin
NVIDIA Morpheus
NVIDIA NIM
NVIDIA Riva Studio
OpenRouter
Rosepetal AI
TensorFlow
Thunder Compute
Ultralytics
Vercel AI Gateway

Integrations

Dataoorts GPU Cloud
Kimi K2
Kimi K2.5
Kimi K2.6
Kimi K3
MATLAB
NVIDIA AI Enterprise
NVIDIA Clara
NVIDIA DeepStream SDK
NVIDIA Jetson
NVIDIA Merlin
NVIDIA Morpheus
NVIDIA NIM
NVIDIA Riva Studio
OpenRouter
Rosepetal AI
TensorFlow
Thunder Compute
Ultralytics
Vercel AI Gateway
Claim NVIDIA TensorRT and update features and information
Claim NVIDIA TensorRT and update features and information
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Claim Wafer and update features and information