Workload and model evaluation
Define the task, quality requirements, latency, throughput, language coverage, licenses, and candidate models. Benchmark rather than selecting by reputation.
Private LLM Deployment
Deploy open-weight LLMs inside your environment
Izwi designs and implements private LLM inference stacks in customer-controlled infrastructure. Move from model selection and GPU sizing to production serving, API integration, observability, optimization, documentation, and handover.
Customer VPC and cloud account. On-premises and data center. Open-weight model serving.
- Customer VPC
- Customer cloud account
- On-premises
- Data center
- Edge and offline
- Planned isolated deployment
What we deliver
More than installing a model server
Infrastructure and GPU sizing
Translate the workload into memory, compute, storage, network, availability, and scaling requirements before committing to capacity.
Inference runtime and APIs
Package an approved serving runtime, model artifacts, configuration, API, health checks, and scaling approach for the target environment.
Security and platform integration
Integrate with the selected network boundary, secrets process, authentication, logging, registry, and operational controls.
Observability and optimization
Instrument health, latency, throughput, queueing, errors, memory, utilization, and capacity, then tune where the workload supports it.
Runbooks and handover
Deliver architecture, manifests or infrastructure code, benchmarks, configuration, upgrade and rollback procedures, and an agreed responsibility model.
Additional components
Build the serving stack your application needs
We can add retrieval, routing, evaluation, and availability components where they support the workload. Your application experience and business logic remain separate unless you ask us to include them.
- embeddings and reranking
- vector search and retrieval infrastructure
- model routing and policy gateways
- content filtering or guardrails
- document-ingestion interfaces
- evaluation pipelines and caching
- autoscaling or high-availability design
- isolated model-delivery and update process
Your environment
Deploy to infrastructure your team can govern
Run model serving in your cloud account, VPC, data center, or another compute environment approved by your team.
- Customer cloud account
- Private VPC
- On-premises or data center
- Planned isolated environment
Model selection
Choose a model for the workload, not for the headline
Izwi evaluates commercially usable open-weight model families against the task, language needs, hardware, license, security review, and operating budget. Model names and versions are validated during the engagement.
Leading open-weight model families from multiple vendors and research communities may be evaluated, subject to license and deployment fit.
Delivery path
From benchmark to production
01
Assess
Define the workload, data requirements, model candidates, hardware, integrations, risks, and success criteria.
02
Pilot
Deploy one real workflow in one target environment and benchmark quality, performance, utilization, and operational fit.
03
Deploy
Package the approved runtime and models with controls, observability, automation, documentation, and rollout guidance.
04
Operate
Monitor performance, manage updates, respond to incidents, and report against the agreed operating model.
Typical deliverables
What your team receives
- workload and architecture brief
- model evaluation and benchmark report
- hardware and capacity recommendation
- deployment manifests or infrastructure code for the target environment
- model-serving endpoint and integration guidance
- identity, network, secrets, health, and performance integration
- security and data-flow documentation
- operations, upgrade, and rollback runbooks
- knowledge transfer and production recommendation
FAQ
Private LLM deployment questions
Do you host the model for us?
The preferred model is deployment inside infrastructure the customer owns or controls. A managed hosting arrangement can be discussed where available, but is not assumed.
Can you deploy any model?
No single model or runtime fits every environment. Izwi evaluates models against the workload, hardware, license, security requirements, language needs, and operating constraints.
Do you provide the GPUs?
The default approach is to size and deploy on customer-owned hardware or capacity in the customer's cloud account. Other capacity arrangements can be scoped separately where available.
Is this a chatbot-development service?
Izwi focuses on private model serving and infrastructure. Application experience, workflow logic, and broader systems integration are separate unless explicitly scoped.
Can this run in an isolated environment?
Potentially. Isolated deployments require explicit planning for delivery, updates, observability, support access, vulnerability handling, and operational ownership.
Start with the workload, not the model name
Share the use case, target environment, expected load, and constraints. We’ll recommend the most useful next step: an assessment, benchmark, pilot, or production plan.