LLMPods
Operational Start Building →
LLMPods compute network TR-IST-01 online

Spin up
intelligence.

We handle the metal.

Run production AI workloads on high-performance GPU infrastructure with familiar APIs, scalable capacity and regional control.

  • OpenAI compatible
  • Regional compute
  • Dedicated GPU
  • Autoscaling
  • No infrastructure setup
API gateway
Router · auto
Inspect pod
POD IDPOD-018
MODELLLAMA 70B
GPUPRO 6000
REGIONTR-IST
LOAD67%
STATEACTIVE
Inspect pod
POD IDPOD-024
MODELQWEN CODER
GPUPRO 6000
REGIONTR-IST
LOAD22%
STATEWARM
Inspect pod
POD IDPOD-031
MODELCUSTOM
GPUPRO 6000
REGIONTR-IST
LOAD41%
STATEDEDICATED
Inspect pod
POD IDPOD-041
MODELEMBEDDING
GPUPRO 6000
REGIONTR-IST
LOAD4%
STATESTANDBY
Inspect pod
POD IDPOD-007
MODELDEEPSEEK
GPUPRO 6000
REGIONTR-IST
LOAD0%
STATESTANDBY
Inspect pod
POD IDPOD-012
MODELWHISPER
GPUPRO 6000
REGIONTR-IST
LOAD0%
STATESTANDBY
Request traceDemo data
RequestPOST /v1/chat/completions
RegionTR-IST-01
RouterAUTO
PodPOD-018
ModelLLAMA-70B
First tokenDEMO 24ms
StreamACTIVE
01/System

API in.
Tokens out.

Every request crosses the same seven layers. Each layer reports what it did, so the path is never a black box.

01
Application
REQUEST7F39A21
METHODPOST
02
LLMPods API
AUTHOK
SCHEMAOPENAI
03
Regional router
REGIONTR-IST
ROUTINGAUTO
04
Model scheduler
MODELQWEN
QUEUE0
05
Compute Pod
POD018
STATEACTIVE
06
GPU
CLASSPRO 6000
ALLOCRESERVED
07
Token stream
STATUSSTREAMING
FIRST TOKENDEMO 24ms
LAYER 03 Developer control
LAYER 02 System intelligence
LAYER 01 Physical compute
LAYER 03 Developer

Illustrative telemetry · demonstration data

02/Developer experience

Change the endpoint.
Keep your stack.

LLMPods speaks the OpenAI schema. Point your existing SDK at a new base URL and keep the code around it.

api.llmpods.com/v1
1from openai import OpenAI
2import os
3
4client = OpenAI(
5 base_url="https://api.llmpods.com/v1",ONE LINE MIGRATION →
6 api_key=os.environ["LLMPODS_API_KEY"],
7)
8
9stream = client.chat.completions.create(
10 model="llama-70b-instruct",
11 messages=[{"role": "user", "content": "Hello, Pod."}],
12 stream=True,
13)
14
15for chunk in stream:
16 print(chunk.choices[0].delta.content or "", end="")
  • 01OpenAI-compatible schema
  • 02Streaming responses
  • 03Tool calling
  • 04Embeddings
  • 05Familiar SDK support
  • 06Developer-first migration
03/Pods

A Pod for
every workload.

Four deployment topologies on one runtime. The difference between them is how much of the machine is yours.

Topology 01
SHARED GPU POOL

Shared Pod

For elastic inference.

  • Usage-based
  • Autoscaling
  • Managed infrastructure
  • Shared GPU pool
Topology 02
RESERVED PARTITION

Performance Pod

For predictable production workloads.

  • Reserved capacity
  • Priority scheduling
  • Consistent performance
  • Predictable availability
Topology 03
ISOLATED

Dedicated Pod

For isolated workloads.

  • Dedicated GPU
  • Custom models
  • Private endpoint
  • Isolated resources
Topology 04

Enterprise Pod

For strategic infrastructure requirements.

  • Multi-GPU
  • Dedicated cluster
  • Private networking
  • Regional control
  • SLA
  • Custom architecture
04/Scale

Capacity follows traffic.

Scale capacity around demand instead of provisioning infrastructure for peak traffic.

Incoming traffic
Requests / s
120
Active Pods
1
GPU utilization
58%
Queue
0
Tokens / s
9.6K
Router · TR-IST-01 Baseline · 1 Pod
001 ACTIVE
002 STANDBY
003 STANDBY
004 STANDBY
005 STANDBY
006 STANDBY
007 STANDBY
008 STANDBY
009 STANDBY
010 STANDBY
011 STANDBY
012 STANDBY
013 STANDBY
014 STANDBY
015 STANDBY
016 STANDBY
Active Warm Standby
Illustrative simulation
End of Home 1 · Hero and system Next — 05 / Models →