llama.cpp vs vLLM on DGX Spark

Quick verdict

Quick verdict guidance: Choose llama.cpp for flexible local GGUF inference and vLLM for concurrent API serving. On the same hardware, the better runtime is the one matching the workload—not the one with the largest isolated tok/s screenshot.

Comparison table

Memory / capacity

Memory / capacity guidance: llama.cpp can partially offload and use many quantizations, making borderline models accessible. vLLM expects a supported checkpoint and benefits most when weights/cache remain accelerator-resident.

Observed LLM performance

Observed LLM performance guidance: llama.cpp often provides excellent single-user decode; vLLM often wins aggregate throughput as concurrent requests rise. Model architecture and kernel support can reverse the result.

Prefill vs decode

Prefill vs decode guidance: Both support cache controls; vLLM's scheduler is more natural for many sequences, while llama.cpp exposes direct K/V quant choices for local experiments.

Power

Power guidance: Batching can increase utilization and throughput per watt, while a personal runtime can idle efficiently between requests.

Current market cost

Current market cost guidance: Operational complexity matters: a slightly faster server can be worse value if its build or model compatibility is fragile.

Which models fit

Which models fit guidance: llama.cpp can partially offload and use many quantizations, making borderline models accessible. vLLM expects a supported checkpoint and benefits most when weights/cache remain accelerator-resident.

Who each option suits

Who each option suits guidance: Individuals, desktop apps, and GGUF collectors should start with llama.cpp. Teams serving multiple clients or agents should start with vLLM.

What stands out

Use llama.cpp at concurrency one and vLLM for a queue of requests. Benchmark both only if the exact checkpoint is well supported by both runtimes.

llama.cpp often provides excellent single-user decode; vLLM often wins aggregate throughput as concurrent requests rise. Model architecture and kernel support can reverse the result.

SGLang is strong for agentic/prefix-heavy workloads; NVIDIA NIM/TensorRT-LLM is the curated optimized path; Ollama/LM Studio simplify llama.cpp-based use.

Evidence limitations

• Do not compare a batched vLLM aggregate rate with llama.cpp single-stream decode.
• Pin runtime and model revisions because kernel support evolves quickly.

Planner CTA

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Sources / methodology

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

• llama.cpp project and backend documentation
• vLLM installation and hardware documentation
• vLLM quantized KV-cache documentation
• vLLM Project DGX Spark Nemotron evaluation