AI Inference

Fast, steady inference for AI agents.

With our AI agents that continuously tune how models run on the hardware.

Start building Add to ChatGPT
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Long runs that finish. Agents complete long tasks without one failure breaking the whole run, and latency stays flat as runs get longer.

Consistency you can rely on. Steady performance on every call, so your team builds product instead of retries and backup providers.

How It Works02

Production traffic guides every improvement.

  1. 01

    Observe real workloads

    Execution graphs, context lengths, and changing batch sizes show where inference spends time.

  2. 02

    Write better kernels

    Our AI agents use hardware-specific LLMs to generate and refine code for those bottlenecks.

  3. 03

    Test against the workload

    Check correctness and measure whether the changes improve inference under the conditions that exposed the problem.

  4. 04

    Deploy and measure

    Release verified improvements into serving, then monitor their effect on production performance.

  5. 05

    Feed the next cycle

    Successful kernels and measured results inform the next experiments and further development of our hardware-specific LLMs.

Our Kernel Development Models03

The LLMs powering
this process.

Meridian, Helix, and Stride power the agents in this process. Each is fine-tuned for the hardware it optimizes.

01 / CUDA

Meridian · NVIDIA

Kernel development for NVIDIA GPUs and CUDA.

View benchmarks
Parallel / OrderedCUDA
02 / ROCm

Helix · AMD

Kernel development for AMD GPUs and ROCm.

Coming soon
Branching / AdaptiveROCm
03 / TT-Metalium

Stride · Tenstorrent

Kernel development for Tenstorrent accelerators and TT-Metalium.

Coming soon
Mesh / DistributedTT-Metalium
Customer stories 04

Real workloads. Trusted infrastructure.

From optimized inference to dedicated production deployments, teams choose Geodd for performance, responsive support, and infrastructure they can rely on.

Explore case studies
152+

Teams using Geodd

Across the Geodd platform

Since 2023

Operating High density Compute

About
In their words

DFINITY

Their infrastructure is high quality, delivering strong performance and consistently low latency, and we have not experienced any issues with their nodes to date. The team responds quickly to all requests and fulfills them promptly.

Rüdiger Birkner, Team Lead, Node, Boundary Node & Decentralized Reliability Engineering, DFINITY

High performance node infrastructure

DFINITY View case study
1 / 3
Pricing & Deployment

Serverless Inference

Qwn 3.8 Flash Next

Model Type
Text
Quantization
fp8

A fast, efficient multimodal AI model designed for coding, reasoning, AI agents, automation, and image/video understanding. Supports 262K tokens natively, extendable to 1M tokens with long-context configuration.

Qwen/Qwen3.8-Flash-Next
$0.150
Input / 1M tokens
$0.450
Output / 1M tokens

GLM 5.3

Model Type
Text
Quantization
fp8

A powerful reasoning model optimized for complex coding, autonomous AI agents, software engineering, and long-running workflows. Supports a 1M-token context window and up to 128K output tokens.

zai-org/GLM-5.3
$1.000
Input / 1M tokens
$4.300
Output / 1M tokens

Qwen 3.8 27B

Model Type
Text
Quantization
fp8

A powerful, efficient 27B-parameter multimodal model optimized for coding, complex reasoning, AI agents, research, and professional workflows. Supports text, image, and video understanding, function calling, and adjustable thinking modes.

Qwen/Qwen3.8-27B
$0.200
Input / 1M tokens
$2.500
Output / 1M tokens

Explore All Models

Multi-Regional

Deploy in US-EAST and Norway. Expansion in Colombo, Sri Lanka is underway.

GDPR Ready & SOC 2 Pending

Enterprise-grade security and data isolation for all workloads.

Unified API

One SDK for both serverless inference and dedicated compute.

Locations Topology

Built for Scale

Select a region

Select a location dot or region to inspect capacity

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Deployment footprint

Global infrastructure

Regional compute capacity positioned for production inference workloads.

3
Global
regions
2
Active
regions
Active
Expansion
Blog

Latest Updates

Dedicated GPU vs Dedicated AI Inference

Dedicated GPU vs Dedicated AI Inference

A dedicated GPU gives your team reserved GPU compute. Dedicated AI inference gives your team a dedicated or isolated inference environment that may in...

Total Cost of Self-Hosted Inference

Total Cost of Self-Hosted Inference

The total cost of self-hosted inference is not only the hourly GPU price. It includes GPU capacity, supporting compute, storage, networking, orchestra...

Integration

Developer-First
Control

Fully compatible with the OpenAI SDK. Switch providers with a single line of code. No migration headaches, just immediate performance gains. This example uses the GPT-OSS-120B API.

  • Direct OpenAI SDK compatibility
  • Real-time token usage and observability
  • Privacy first with Zero Data Retention (ZDR) and logging policy
deploy_inference.py
from openai import OpenAI

# Switch to Geodd by changing base_url
client = OpenAI(
  api_key="GEODD_API_KEY",
  base_url="https://api.geodd.io/inference/v1"
)

completion = client.chat.completions.create(
  model="openai/gpt-oss-120b",
  messages=[
    {"role": "user", "content": "What is machine learning?"}
  ]
)

print(completion.choices[0].message)

Ready to Scale?

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Today.

Get instant access to our Model APIs and dedicated GPUs. Precision engineered for the most demanding production workloads.

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deploy — mistral-large
Provisioning GPUdone
Preparing Serverdone
Configuring Accessdone
Downloading Modelrunning…
Starting Model Serverpending
Finalizing Servicepending
$awaiting pipeline