Input
A request enters the system: a spoken sentence, a typed message, a document, or a data record that needs to be understood.
ENTERPRISE AI INFRASTRUCTURE & INFERENCE
Intelligence matters. Runtime makes it real.
Models provide the intelligence. Inference is the runtime that turns that intelligence into a fast, reliable, and scalable enterprise service. It is the layer where a trained model meets real requests, real data, and real business outcomes.
CBOT gives enterprises control over how that runtime behaves, from response speed and GPU efficiency to scaling and deployment, across cloud, hybrid, and fully on-premise environments.
CBOT runtime
VOICE REQUEST
Speech to textModelWorkflow actionText to speech
DIGITAL REQUEST
Context and knowledgeModel inferenceTool or APIResponse
BACKGROUND TASK
Document eventModel processingStructured outputSystem update
Runtime
CONCURRENT REQUESTS
FIRST TOKEN LATENCY
TOKENS PER SECOND
ACTIVE REPLICAS
QUEUE DEPTH
GPU ALLOCATION
SERVICE HEALTH
HealthyWhat is AI inference?
Inference is the process of running a trained model on new input to produce a result. Training teaches a model. Inference is where that trained model does its work, taking a request and returning an answer, a classification, a transcription, or a spoken response.
Every voice call, message, search, and automated decision an enterprise AI handles passes through inference. It is the moment the model becomes a working service rather than a static file.
- Input
- Inference
- Output
- Enterprise action
Inference
The trained model processes the input and produces a result, running on the infrastructure allocated to that workload.
Output
The model returns an answer, a classification, an extraction, a transcription, or generated text ready to be used.
Enterprise action
The output moves into the business workflow, triggering a response, an update, an integration, or a next step.
The model defines what AI can do. Inference determines how well it performs in the real world.
Why inference performance defines enterprise AI.
A capable model is only as useful as the runtime that serves it. Inference performance shapes how fast the system responds, how much it can handle, and how efficiently it uses infrastructure. These six dimensions define that performance.
Latency
How quickly the system responds after receiving a request. Latency is what users feel first, especially in real-time voice.
Throughput
How many requests the system can handle at the same time without slowing down or forming queues.
GPU efficiency
How well compute resources are used, so capacity produces useful work instead of sitting idle or being oversized.
Scalability
How the service grows under load, adding capacity where demand appears rather than expanding everything at once.
Reliability
How consistently the service stays available and responsive, including during peaks and partial failures.
Privacy
Where inference runs and where data is processed, so sensitive workloads can remain inside the approved environment.
Enterprise AI performance is infrastructure performance.
The layers behind every inference request.
An enterprise AI request does not reach a single model in isolation. It moves through a runtime built from distinct layers, each with its own responsibility and its own infrastructure needs.
- InteractionVoiceChatWhatsAppAPI
- AI processingSpeech to textLLM inferenceText to speech
- Knowledge and agentRetrievalToolsWorkflowsMemory
- Runtime and infrastructureGPU nodesAutoscalerQueueModel server
INTERACTION LAYER
- Voice
- Web
- Mobile
- Messaging
- Enterprise applications
- Internal workflows
AI PROCESSING LAYER
- CBOT LLM
- Commercial and open-source language models
- Proprietary NLP and NLU
- CBOT Speech-to-Text
- CBOT Text-to-Speech
- Classification and extraction models
KNOWLEDGE AND AGENT LAYER
- Enterprise RAG
- Embedded or connected vector storage
- AI agent workflows
- Business rules
- Tools and APIs
RUNTIME AND INFRASTRUCTURE LAYER
- Model serving
- CPU and GPU resource allocation
- Request queues
- Caching
- Streaming responses
- Container orchestration
- Health checks
- Autoscaling
- Logs and monitoring
Every service receives the infrastructure it needs, without forcing the entire platform to scale in the same way.
Real-time voice depends on every layer working together.
In a real-time voice interaction, several inference steps happen in sequence, and the user experiences only the final response. Each step adds time, so the runtime is optimized end to end.
- 01
Listen
Speech-to-Text converts the caller's voice into text as they speak.
- 02
Understand
Proprietary NLP and NLU interpret intent, entities, and context from the text.
- 03
Infer
The selected language model reasons over the request, enterprise knowledge, and workflow state.
- 04
Act
AI agent logic applies business rules, calls tools, and decides the next step.
- 05
Respond
Text-to-Speech turns the result back into natural speech for the caller.
Speech stream
Partial transcription
Intent and context
Model generating
First response tokens
Speech output
Time
OPTIMIZATIONS THAT REDUCE END-TO-END LATENCY
- Streaming Speech-to-Text
- Streaming model responses
- Low-latency proprietary NLP
- Response caching
- Pre-warmed model containers
- Connection pooling
- Asynchronous processing
- Optimized Speech-to-Text and Text-to-Speech serving
- Workload-specific model selection
The user experiences one response. CBOT optimizes every millisecond behind it.
Performance varies according to model size, infrastructure, concurrency, and deployment configuration.
Scale the workload, not the whole platform.
Enterprise demand is uneven. Some services face sudden peaks while others stay steady. CBOT scales each workload independently, so capacity follows demand instead of expanding the entire platform at once.
Baseline
LLM inference
Speech to text
Text to speech
Digital runtime
Voice traffic increases
LLM inference
Speech to text
Text to speech
Digital runtime
Horizontal scaling
Add more service replicas to handle more concurrent requests as demand grows.
Vertical scaling
Give a service more CPU, GPU, or memory when a heavier workload requires it.
Workload-based scaling
Scale each service on its own signals, so a busy component grows without touching the rest.
Peak readiness
Prepare capacity ahead of expected peaks so the service stays responsive under pressure.
Cold-start optimization
Keep model containers warm so newly added capacity can serve requests without a slow first response.
Scale the service under pressure, not the entire platform.
Use GPU capacity where it creates value.
GPU is the most expensive part of most AI infrastructure, so it should be allocated deliberately. Not every workload needs a GPU, and not every model needs the largest one. CBOT sizes compute around the work each service actually performs.
Right-sized models
Match model size to the task instead of running the largest model for every request.
Workload separation
Keep GPU-heavy workloads apart from lighter ones so each is served on suitable resources.
Independent allocation
Allocate compute per service, so one workload can scale without over-provisioning the others.
CPU where appropriate
Run services that do not require a GPU on CPU, reserving GPU capacity for workloads that need it.
Capacity planning
Plan compute around expected concurrency and traffic patterns rather than a single fixed assumption.
The most expensive infrastructure is capacity that produces no additional value.
INFRASTRUCTURE SIZING DEPENDS ON
- Selected model
- Model size
- Quantization
- Text or voice workload
- Concurrent sessions
- Tokens per second
- Latency target
- Availability model
- Deployment architecture
There is no single universal hardware recommendation. Detailed sizing is completed during solution design, based on the specific workload and requirements.
Run the complete inference stack inside your own environment.
Some platforms keep their application layer private while still depending on external services for models, speech, or retrieval. CBOT can bring the full inference stack into the customer-controlled environment, so sensitive workloads run without leaving the approved boundary.
COMPONENTS THAT CAN RUN ON-PREMISE
Language model inferenceCBOT LLMOpen-source modelsCBOT Speech-to-TextCBOT Text-to-SpeechProprietary NLP and NLUEnterprise RAGEmbedded vector databaseAI agent runtimeWorkflow orchestrationAnalytics and operational servicesIntegrations and APIs
DEPLOYMENT OPTIONS
Choose the boundary that fits each workload.
Your models. Your data. Your infrastructure boundary.
01
SaaS
Use CBOT inference through a managed cloud service for workloads approved for external operation.
02
Private cloud
Run inference within an isolated private-cloud environment shaped by your security and network requirements.
03
Hybrid
Combine cloud models with private, customer-hosted, and on-premise inference, routing each workload by its requirements.
04
Fully on-premise
Keep models, data, and runtime inside your own infrastructure, without sensitive data leaving the approved network boundary.
Infrastructure sizing is part of the solution, not a template.
The right infrastructure depends on the specific workload, not on a fixed formula. CBOT works with enterprise teams to size the runtime around real requirements and expected growth.
WHAT INFRASTRUCTURE DESIGN CONSIDERS
- Use case and channel
- Model selection
- Text or voice processing
- Expected concurrency
- Average and peak traffic
- Latency targets
- Token throughput
- GPU availability
- High-availability requirements
- Data-security policies
- Cloud or on-premise deployment
- Growth and scaling expectations
Infrastructure sizing is not a template. It is part of the AI solution.
Frequently asked questions
What is AI inference?
AI inference is the process of running a trained model on new input to produce a result, such as an answer, a classification, a transcription, or a spoken response. It is where a trained model becomes a working service.
Why is inference important?
Inference determines how a model performs in production. It shapes response speed, how much load the system can handle, how efficiently infrastructure is used, and where data is processed.
What is LLM inference?
LLM inference is the process of running a large language model on a given input to generate text. It is the runtime step where the model reasons over a request and produces a response.
Does every AI workload require a GPU?
No. Language model and selected speech workloads may require GPU, while many other services can run on CPU. The right allocation depends on the models and workloads involved.
Can CBOT run language models on-premise?
Yes. CBOT LLM, supported open-source models, and customer-hosted models can run inside the customer-controlled environment. Commercial models remain available through external or private endpoints.
Can CBOT Speech run on-premise?
Yes. CBOT Speech-to-Text and Text-to-Speech can operate within the customer's environment, so voice data can remain inside the approved boundary.
Can the complete CBOT platform run on-premise?
Yes. The platform, AI agent runtime, model inference, proprietary NLP, speech, RAG, vector storage, analytics, and integrations can operate inside the customer's environment.
How does CBOT scale AI workloads?
CBOT scales each workload independently, adding capacity where demand appears through horizontal and vertical scaling, workload-based signals, peak readiness, and cold-start optimization.
How are GPU requirements calculated?
GPU requirements depend on the selected model, model size, quantization, workload type, concurrency, throughput, latency targets, and availability model. Detailed sizing is completed during solution design.
Does CBOT support Kubernetes and OpenShift?
Yes. CBOT supports containerized deployment using Docker, Kubernetes, and Red Hat OpenShift, with independent service scaling and customer-controlled GPU allocation.
How does CBOT reduce inference latency?
CBOT reduces latency through streaming speech and model responses, response caching, pre-warmed containers, connection pooling, asynchronous processing, and workload-specific model selection.
Can CBOT support cloud and private models in the same architecture?
Yes. A hybrid architecture can combine approved cloud models with private, customer-hosted, and on-premise models, routing each workload by its security, performance, and data requirements.
FROM MODEL TO PRODUCTION
Build the runtime your enterprise AI needs to perform.
Talk to CBOT about latency, GPU efficiency, scaling, and deployment. We can help you design an inference architecture that fits your workloads, your data, and your infrastructure, across cloud, hybrid, and fully on-premise environments.