Visual Understanding
Turn images and videos into structured intelligence at scale with FriendliAI's TCache multimodal prefix caching and optimized VLM inference.
problem
Visual understanding is compute-intensive, slow, and expensive at scale
VLM inference bottlenecks image and video pipelines
Processing visual inputs through large VLMs adds significant per-request latency, bottlenecking pipelines that require high-concurrency workloads.
Redundant computation across shared frames
Without caching, every query re-encodes identical frames from scratch, wasting GPU cycles and inflating costs.
Scaling to millions of video hours is challenging
Infrastructure that throttles under sustained, high-concurrency workloads can't keep up with production video and image pipelines as workload volume grows.
Costs scale linearly and become unmanageable
Without prefix caching and efficient encoder reuse, processing thousands of images or video hours re-runs the same work on every request and becomes economically unviable.
solution
FriendliAI makes large-scale visual understanding fast and cost-efficient
Optimized VLM inference for long-context inputs
Custom GPU kernels and memory-efficient serving minimize latency across extended sequences and high-concurrency workloads.
Friendli TCache prefix caching eliminates redundant computation
Shared frames and system prompts are computed once and cached, reducing GPU load and delivering sub-linear cost scaling.
Effortless scaling to millions of hours
Continuous batching and autoscaling sustain throughput across simultaneous jobs, keeping video and image pipelines stable as workload volume grows to millions of hours.
Predictable, cost-efficient visual understanding
Friendli TCache reuses encoded image and video frame representations across requests, while quantization and high tokens-per-GPU throughput drive down cost-per-request as workloads scale.
Open Models for Visual Understanding
Access the world’s largest collection of 540,000 models through seamless Hugging Face integration. From text generation to computer vision, launch any model with a single click.
Have a custom or fine-tuned model?
We'll help you deploy it just as easily. Contact us to deploy your model.
How Teams Scale with FriendliAI
Learn how leading companies achieve unmatched performance, scalability, and reliability with FriendliAI
Our custom model API went live in about a day with enterprise-grade monitoring built in.
Rock-solid reliability with ultra-low tail latency.
Scale to trillions of tokens with 50% fewer GPUs, thanks to FriendliAI.
Fluctuating traffic is no longer a concern because autoscaling just works.
Friendli Engine is an irreplaceable solution for generative AI serving.
Our custom model API went live in about a day with enterprise-grade monitoring built in.
Rock-solid reliability with ultra-low tail latency.
Scale to trillions of tokens with 50% fewer GPUs, thanks to FriendliAI.
Fluctuating traffic is no longer a concern because autoscaling just works.
Friendli Engine is an irreplaceable solution for generative AI serving.
Additional Resources
Docs, demos, and resources for visual understanding applications.
Automating Industrial Inspection with Vision Language Models
Friendli TCache: Flexible Multimodal Prefix Caching
NVIDIA Nemotron™ 3 Nano Omni, Day-0 on FriendliAI: Unified Multimodal Reasoning, at Peak Performance
Deploy Multimodal Models from Hugging Face to FriendliAI with Ease
How to Compare Multimodal AI Models Side-by-Side
Automating Industrial Inspection with Vision Language Models
Friendli TCache: Flexible Multimodal Prefix Caching
NVIDIA Nemotron™ 3 Nano Omni, Day-0 on FriendliAI: Unified Multimodal Reasoning, at Peak Performance
Deploy Multimodal Models from Hugging Face to FriendliAI with Ease