Who this blueprint is for
DGX Spark combines 128GB of unified memory with the NVIDIA AI software stack for teams that want dedicated LLM capability at a desk or development site. For a Qwen 35B-class model, usable performance depends on quantization, context length, KV cache, concurrency, and the inference framework—not parameter count alone.
Validate internal knowledge, development, or service scenarios before expanding to a GPU cluster.
Keep models, documents, vector data, and invocation logs in the enterprise environment.
Launch without a dedicated GPU server room by using desktop-class hardware.
Establish measurable quality and performance baselines before committing to larger infrastructure.
Five parameters to define before deployment
Confirm the exact weights, context capability, and inference compatibility.
Long documents, codebases, and multi-turn sessions materially increase memory use.
Single-user R&D, departmental sharing, and external service require different capacity models.
Define time to first token, generation rate, and end-to-end latency separately.
Specify knowledge bases, WeCom, OA, APIs, and agent tools before sizing.
Reference architecture
Standard delivery sequence
Capacity sizing
Determine feasibility from model, precision, context, concurrency, and latency targets.
Environment deployment
Install drivers, inference runtime, model service, and monitoring with locked versions.
Business validation
Use anonymized data to test knowledge, coding, or business-assistant quality.
Performance acceptance
Measure memory, time to first token, generation rate, concurrency, and sustained stability.
Recommended acceptance criteria
DGX Spark is suitable for validation and controlled production workloads, but it does not replace a multi-node GPU cluster. High concurrency, ultra-long context, multiple large models online at once, or strict high availability require server-class GPU infrastructure.
Size the workload before buying compute
Share the model build, use case, user count, and context requirements. We will define capacity boundaries and acceptance criteria first.