vLLM on NVIDIA DGX Spark

Plain-English answer

Plain-English answer guidance: Use vLLM on DGX Spark when you are building an API for multiple users, need batching or prefix caching, or serve a supported Transformers/NVFP4 model. For one-person GGUF chat, llama.cpp is usually simpler and often the better starting point.

Why it matters

Why it matters guidance: Recommended for internal APIs, agent platforms, and RAG services. Avoid it for a quick offline GGUF workflow or an unsupported just-released architecture that llama.cpp already handles. Use FP8 KV cache when context or concurrency, not model weights, causes memory pressure. Calibrated scales are preferable when output quality matters.

Real StackBench examples

Common misconception

Common misconception guidance: ARM64 builds and rapidly changing model kernels require more version discipline than a packaged desktop app. A successful model load does not prove stable throughput at the intended concurrency; soak-test memory and tail latency.

Measured / example table

Decision rule

Decision rule guidance: Choose vLLM when concurrent API traffic is a requirement. Choose llama.cpp when model-format flexibility and minimal setup are the requirement.

Related benchmarks

Related benchmarks guidance: vLLM's advantage is aggregate throughput under batching, not necessarily the fastest single stream. Start with `max-num-seqs` between 4 and 16 and raise it while watching per-request latency and KV-cache pressure. NVIDIA NIM/TensorRT-LLM offers a more curated NVIDIA path; SGLang is competitive for agentic/prefix-heavy workloads; llama.cpp is the portability choice.

Planner / Explore CTA

Compare this recommendation against your model, context, concurrency, latency, and budget in the ComputeSage Planner.

Sources / methodology

Recommendations combine official specifications, public model/runtime documentation, adjacent benchmark observations, and clearly labeled engineering estimates. Review the ComputeSage evidence methodology.

• vLLM installation and hardware documentation
• vLLM quantized KV-cache documentation
• NVIDIA DGX Spark specifications
• NVIDIA DGX Spark User Guide
• vLLM Project DGX Spark Nemotron evaluation