Size AI inference server capacity from measured model demand signals
Run customer-operated LLM, vision, speech, and recommendation services on isolated GPU-capable compute sized for model footprint, concurrency, queue depth, and latency targets.
GPU dedicated servers sized from model demand
Size dedicated server capacity to model load
Match GPU, CPU, RAM, storage, and network capacity to model footprint, concurrency, and measured request demand.
Retain the customer-owned serving stack
Your team owns the runtime, containers, model artifacts, API layer, scaling policy, observability, release process, and SLOs.
Link recovery access to incident ownership
Use IPMI/KVM and the management API for recovery access while keeping incident responsibility clearly assigned.
Separate hardware and inference signals
Use component monitoring for supported hardware signals; your team tracks model, runtime, API, and application telemetry.
Available configurations
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Make model serving an explicit production boundary
A trained model is not a production service until the team defines request schemas, authentication, routing, batching, streaming, deadlines, health checks, versioning, and failure behavior. Capacity planning then ties model footprint and KV cache demand to concurrency, queue depth, time to first token, and throughput under representative production load.
Melbicom supplies isolated GPU-capable compute with configurable CPU, RAM, storage, and network capacity, plus IPMI/KVM and management API access. Component monitoring covers supported hardware signals; server configuration, operating-system installation, and specialist administration can be scoped under an agreed task boundary before deployment.
Your team owns the model, GPU software stack, inference runtime, serving layer, REST/gRPC contract, scaling logic, observability, rollouts, and SLOs. This boundary keeps interfaces portable across configurations, makes capacity cost easier to attribute, and assigns each production incident from hardware signal to client response paths with clear ownership.
Keep latency and queue signals measurable
Tie capacity choices to latency, throughput, queue depth, cost, and ownership.
Latency tied to capacity
Queue depth kept visible
Failure ownership explicit
Warm capacity planned
Rollout scope controlled
Cost per request attributable
Operate inference on your own stack
Select the hardware and access mechanisms; keep the complete serving stack under your team's control.
Isolated GPU-capable compute
Configurable CPU and RAM capacity
Storage sized to model artifacts
Network capacity matched to traffic
IPMI/KVM recovery access
Management API for server access
Supported component monitoring
Explicit monitoring scope boundaries
Custom configuration assistance
Scoped specialist administration
Plan your configuration
Share model footprint and request behavior to scope a suitable dedicated server.
Talk to an expert
Map each inference path to explicit capacity
LLM text generation
Size GPU-capable compute for model weights, KV cache, batch policy, concurrency, and time-to-first-token targets.
Vision model serving
Match image and video preprocessing, model memory, batch size, request rate, and response payloads to available capacity.
Speech model serving
Plan streaming, audio preprocessing, model loading, concurrency, and timeout behavior before exposing the team-operated API.
Recommendation serving
Map embedding tables, feature inputs, ranking latency, batch policy, and request volume to hardware capacity.
REST contract control
Define REST schemas, authentication, timeouts, errors, health checks, and version rules in your serving stack.
gRPC streaming control
Implement gRPC streaming, message contracts, deadlines, status handling, and client compatibility in your serving stack.
Multi-model routing
Separate model loading, routing, memory demand, and rollout ownership across models sharing one serving fleet.
Replica recovery planning
Plan warm capacity, rebuilds, model reloads, traffic recovery, and hardware-failure ownership for production replicas.
FAQ
What inputs should size an AI inference server?
Does Melbicom provide managed model inference?
Are REST and gRPC endpoints included?
Which GPU models are available?
What recovery access is available?
What does component monitoring cover?
Can Melbicom help configure the server?
How does dedicated server hosting fit inference?
Who owns scaling and replica planning?
How should production responsibilities be divided?
Opening an account
Create an account, access the control panel, and fund the balance before placing your server order.
Create an account
Plan the serving scope
Share model footprint, request behavior, and ownership boundaries so specialists can scope the configuration.
Talk to an expert