A model that fits in memory for one request may not meet the needs of twenty users. Longer contexts, concurrent sequences, caches, and serving overhead change the picture. Training has another resource profile again.
We separate workload sizing from hardware procurement. Define the model and precision, task mix, context distribution, concurrency, latency expectations, and reliability needs; then benchmark a candidate stack under representative load.
Selected for the workload—not prescribed as a single mandatory stack. Explore the technology ecosystem ↗
A private knowledge assistant at peak usage
An organization wants an in-house RAG service. The workload combines embedding ingestion, vector queries, reranking, and answer generation. Running every component on one accelerator may create contention when ingestion overlaps with user requests.
The capacity design distinguishes online and background work. It considers accelerator memory, CPU and RAM, document storage, index performance, network access, and queues. A limited pilot measures time to first response, full-response latency, utilization, and failures under concurrency.
The result informs whether to separate services, change precision, use a different model, or expand compute. Buying a larger GPU first can hide the actual bottleneck or leave expensive resources idle.
Model/task profile + load targets + deployment boundary
A benchmarked compute and operations plan
Plan the surrounding infrastructure with the accelerators
Power supply, cooling, physical space, storage throughput, network topology, and replacement/support arrangements are part of an in-house stack. Backups need to cover application records, model artifacts, configurations, and derived indexes according to their recovery requirements.
A serving framework such as vLLM may support efficient inference for an appropriate model and hardware combination. Test compatibility rather than assuming support, and put authentication, quotas, network restrictions, and monitoring around the serving layer. Private infrastructure remains an operating system that must be patched and maintained.
Size from real workloads
We profile model size, precision, context length, concurrency, and latency targets. Inference and training have different resource patterns. Benchmarking representative tasks is more informative than sizing solely from a model’s parameter count.
Plan the physical and data stack
GPU memory, CPU, RAM, storage throughput, networking, rack space, power, and cooling need a coherent plan. Retrieval indexes, document stores, model artifacts, and backups also need capacity and recovery arrangements.
Build a private serving layer
Model servers, authenticated gateways, scheduling, quotas, and monitoring sit between applications and accelerators. Serving frameworks such as vLLM are evaluated where compatible with the selected models and workload; no framework is assumed to fit everything.
Keep operations visible
Track latency, utilization, errors, queue depth, and costs. Patch management, artifact provenance, secrets, network boundaries, and incident response are part of the operating design. A private location does not by itself make a system secure.
Choose the approach for the constraint
| When this matters | An approach to consider | What not to assume |
|---|---|---|
| Demand is steady and data boundaries are strict | Benchmark an in-house serving option | Include staffing, power, maintenance, and recovery costs. |
| Demand is bursty or uncertain | Compare cloud or a controlled hybrid boundary | Peak capacity can be expensive to keep idle. |
| A model barely fits | Measure headroom under realistic context and concurrency | Weight size alone is not the total memory requirement. |
The boundary we keep explicit
Hardware recommendations require current availability, compatibility checks, and workload benchmarks. We do not promise that every model fits on a particular GPU or that private hosting is always cheaper.
What a useful evaluation should reveal
Evaluate this workload against representative examples and agreed consequences—not just a convincing response. The review should make these dimensions visible:
- Memory headroom and throughput
- Latency under concurrent load
- Recovery and availability tests
- Total operating cost and ownership
Where this approach fits
- On-premises model serving and private RAG
- In-house GPU workstations and server stacks
- Hybrid deployments with explicit data boundaries
A considered first step
Document the model and task mix, expected users, data boundary, and service targets. Produce a capacity plan and benchmark a representative stack before purchasing or expanding hardware.
Serving enterprise teams in California, Atlanta, Georgia, and across the United States.
Discuss your requirementsQuestions worth resolving
Can we assemble the hardware stack in-house?
Yes, the design can cover workstation or server configurations, operating environment, model serving, storage, and application access. Procurement, installation, and support responsibilities are agreed as part of the scope.
Should we use cloud, on-premises, or hybrid?
We compare data residency needs, utilization, growth, operating skills, and cost. Hybrid can be useful when the boundary between private data and external services is explicit and enforceable.