Deploy AI Inference Infrastructure

Run production-ready inference endpoints with low latency and automatic scaling.

PyTorchDeepSpeedMegatron-LMHugging FaceRayKubernetes

Real-time inference, built for production

Serve LLMs, vision models and multimodal applications using optimized GPU instances built for real-time inference.

Reduce costs while maintaining high throughput and predictable latency.

Built for serious training runs

01

Ultra-low latency

Optimized serving stack for fast time-to-first-token.

02

Autoscaling

Capacity adjusts automatically as demand changes.

03

Load balancing

Traffic distributed evenly across your GPU pool.

04

Dedicated GPUs

Reserve capacity for predictable, isolated performance.

05

Private networking

Keep inference traffic off the public internet.

06

API deployment

Stand up an endpoint without managing servers.

07

Monitoring

Track latency, throughput and utilization in real time.

Match GPU to your traffic pattern

RTX 4090

Affordable AI computing.
Light trafficDev/test

L40S

Balanced performance and efficiency.
Mixed workloads
Recommended

H100

Best for large models and high concurrency.
High throughput

B200

Maximum throughput for demanding inference.
Enterprise scale

How requests are served

Inference requests are routed through a load balancer to dedicated or shared GPU pools, with autoscaling adjusting capacity to demand and private networking keeping traffic isolated.

Client / API Request

Your application or user

GPU Inference Pool

Autoscaled, load-balanced

Checkpoints & Registry

Versioned model outputs

Where teams put inference to work

Chatbots

Conversational assistants with real-time responses.

AI APIs

Public or internal APIs backed by GPU inference.

Enterprise Assistants

Internal copilots for employees and teams.

Vision AI

Image and video model serving at scale.

Recommendation Engines

Personalization models with low-latency scoring.

RAG Applications

Retrieval-augmented generation pipelines.

Throughput and latency, under control

  • High request throughput
  • Batch inference
  • Streaming responses
  • GPU sharing
  • Dedicated endpoints
LlamaMistralQwenGemmaDeepSeekMixtralWhisperCLIP

Built to fit your existing stack

Every deployment ships with API access and console control, so you can automate provisioning or hand it to your engineer of record.

Full API reference and setup guides are shared during onboarding — reach out and we'll get you access.

Talk to an Expert
# Send an inference request curl -X POST https://api.interhash.ai/v1/ai-inference \ -H "Authorization: Bearer $INTERHASH_KEY" \ -H "Content-Type: application/json" \ -d '{"model": "llama-3-70b", "prompt": "Hello", "stream": true}'

Frequently asked questions

What latency should I expect?
Latency depends on model size and GPU choice — our team will help you pick a configuration that meets your SLA.
Can I bring my own model?
Yes, custom and fine-tuned models can be deployed alongside the optimized models we support out of the box.
Is streaming supported?
Yes, both streaming and batch response modes are available for supported models.

Deploy your inference endpoint today.