llama.cpp on RTX 5090

Plain-English answer

Plain-English answer guidance: llama.cpp is the easiest RTX 5090 runtime for GGUF models and single-user local inference. Use full CUDA offload and a 4–6 bit quantization; switch to vLLM only when batching and API throughput outweigh GGUF flexibility.

Why it matters

Why it matters guidance: Recommended for local chat, coding, document work, and people who download community GGUF releases. It is less ideal for a large shared API with many simultaneous requests. Start at 8K–16K and Q8 KV cache. Increase only after confirming the selected GGUF leaves sufficient memory under real prompt lengths.

Real StackBench examples

Common misconception

Common misconception guidance: New architectures and advanced multimodal/MoE features can lag, while built-in multi-user scheduling is less sophisticated than vLLM. Use GGUF files from a trusted converter and avoid requantized-on-quantized releases when quality matters.

Measured / example table

Decision rule

Decision rule guidance: Choose llama.cpp when the target model has a trusted GGUF and concurrency is low. Keep all layers on GPU; if that is impossible, use a smaller quant or higher-memory card before accepting heavy offload.

Related benchmarks

Related benchmarks guidance: A tuned 27B–35B 4-bit model should deliver interactive output in the high tens to low hundreds of tok/s. CPU-offloaded layers can sharply reduce that advantage. vLLM offers continuous batching and OpenAI-compatible serving; Ollama and LM Studio provide friendlier wrappers; TensorRT-LLM/NIM targets curated NVIDIA performance.

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.

• llama.cpp project and backend documentation
• NVIDIA GeForce RTX 5090 specifications
• vLLM installation and hardware documentation
• StackBench controlled lab approved source qwen38-rtx5090-owner-matrix-20260816