Questions to evaluate
- What are the model scale, precision, and method?
- What are the dataset, read pattern, and data boundaries?
- Can one node work, and when is multi-node justified?
- How often are checkpoints written and how should recovery work?
SOLUTIONS
The right infrastructure for each AI task.
Training, inference, and dedicated enterprise environments need different compute, networks, storage, and operations.
01 · TRAINING & FINE-TUNING
Training & fine-tuning
Provide sustained compute, data access, and checkpoint support for training and fine-tuning.
Choose a single-node or multi-node plan around the model, data, and training method.Architecture focus
- Memory and parallel strategy
- Intra-node and inter-node communication
- Dataset reads and checkpoint writes
- Images, frameworks, and dependencies
Training speed, delivery scope, and acceptance criteria depend on project testing and confirmation.
Discuss a project02 · INFERENCE
Inference deployment
Configure inference around latency, throughput, concurrency, and access.
Turn request patterns into compute, memory, network, and operating needs.Questions to evaluate
- What latency, throughput, and concurrency range matters?
- How do context and output patterns vary?
- How will models load and coexist?
- What are service access, permissions, and observation boundaries?
Architecture focus
- Memory use and batching
- Peaks and capacity margin
- Model loading and warm-up
- Service and management networks
Service form, capacity, and operating support are confirmed in the project plan.
Discuss a project03 · DEDICATED AI ENVIRONMENT
Dedicated enterprise AI
Provide a dedicated AI environment with resource isolation and clear data boundaries.
Design around team access, data use, software, and operating ownership.Questions to evaluate
- Which resources need to be dedicated or isolated?
- How does data enter, move, and leave?
- How will the team access the environment?
- Who owns software and operational responsibilities?
Architecture focus
- Resource and permission boundaries
- Access networks and data paths
- Environment baseline and change flow
- Acceptance and support ownership
Management platforms, identity, and compliance requirements are confirmed separately for each project.
Discuss a projectThree tasks, three sets of priorities
Compare capacity, networking, storage, and delivery priorities across workloads.
| Training & fine-tuning | Inference deployment | Dedicated enterprise AI | |
|---|---|---|---|
| Capacity | Model states, batch, and precision | Concurrency, context, and peak margin | Team scale and isolation |
| Network | Parallel communication and data reads | Requests and service access | Service, management, and access boundaries |
| Storage | Datasets and checkpoints | Model loading and logs | Data ingress, retention, and migration |
| Delivery | Reproducibility and runtime validation | Interface and capacity validation | Ownership and acceptance |
SOLUTION REVIEW
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