Size a gpt-oss-120b dedicated server for measured demand
Run a private assistant on isolated GPU capacity. Size memory, context, concurrency, queue depth, and latency while your team owns the runtime, access, integrations, and recovery.
GPU dedicated servers sized for inference demand
Verify GPU memory fit
Start with OpenAI’s single 80 GB GPU guidance, then verify hardware against your inference engine and workload.
Measure request load
Test context length, concurrency, batching, queue depth, and token generation demand with the selected inference engine.
Own the serving stack
Control the operating system, drivers, inference engine, model files, API layer, authentication, observability, and releases.
Separate assistant roles
Keep inference capacity separate from retrieval, identity, workflow integrations, observability, and the assistant interface.
Available configurations
Need a Custom Build?
Run a customer-controlled gpt-oss-120b production assistant
A private assistant is more than a model file. Teams must turn gpt-oss-120b into a production serving plan that covers GPU memory, context length, concurrency, batching, queue depth, latency targets, identity, retrieval, evaluation, and recovery before employees rely on its answers.
Melbicom provides isolated GPU-capable infrastructure with customer control over the operating environment. Size CPU, RAM, storage, and network capacity for the chosen inference engine, then verify the current configuration against OpenAI’s single 80 GB GPU starting point, expected request patterns, and workload measurements under representative load.
Your team owns drivers, runtime, model artifacts, APIs, access controls, integrations, observability, releases, evaluation, and incident response. Clear boundaries separate infrastructure from assistant behavior, assign recovery work, and tie capacity changes to measured utilization while all application decisions stay with the operating team throughout production operations.
Keep private assistant capacity measurable
Track model-serving pressure before queue time and answer latency reach employees.
Verified GPU memory fit
Measured context demand
Attributable queue pressure
Controlled serving stack
Explicit incident ownership
Evidence-led capacity changes
Own each layer of model serving
Set clear owners for hardware, runtime, access, telemetry, releases, and recovery before launch.
Isolated GPU-capable hardware
Customer-owned operating environment
GPU memory matched to model
CPU and RAM sized to runtime
Storage sized for model artifacts
Bandwidth choices for data paths
Custom ISO upload support
Customer-managed serving software
Ready-to-deploy server configurations
Custom server configuration options
Plan the inference build
Check model memory, runtime, load, storage, and network before selecting hardware.
Talk to an expert
Run internal AI workloads with clear ownership
Knowledge assistant
Serve authenticated chats while the application controls retrieval, permissions, citations, session state, and responses.
Support assistant
Generate drafts and summarize cases while your systems enforce customer-data access, approvals, and review workflows.
Research assistant
Synthesize long contexts while teams track prompt size, queue depth, generation time, and source-handling behavior.
Workflow assistant
Connect reasoning to internal tools with application-owned authorization, action limits, audit logs, and rollback controls.
Policy assistant
Answer policy questions from trusted sources with identity, retrieval, versioning, and escalation controlled internally.
Engineering copilot
Assist with code and system questions while repositories, permissions, tool calls, and review gates remain application-owned.
Inference API
Expose inference through an internal API with controlled schemas, authentication, quotas, telemetry, and versioned releases.
Evaluation service
Compare prompts, runtimes, and model customizations against task quality, latency, resource use, and regression thresholds.
FAQ
Is gpt-oss-120b the same as ChatGPT?
What are the model’s core specifications?
What does OpenAI say about GPU memory?
Is one 80 GB GPU enough for production?
Who manages the inference software?
Can gpt-oss-120b be customized?
What should we measure before ordering?
Does Melbicom provide OpenAI’s hosted API?
Can we upload a custom ISO?
Are specific GPU models always available?
How quickly are custom configurations delivered?
What support does Melbicom provide?
Create your account
Access the control panel, review server configurations, and prepare the order for your inference stack.
Create an account
Review the model fit
Share model memory, runtime, load, storage, and network requirements before selecting a server.
Talk to an expert