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Serving Qwen 3.8 27B with llama.cpp

Qwen 3.8 27B has 27 billion parameters. llama.cpp targets workstations and modest hardware. This page gives the memory required and the platforms that qualify.

By QDNA · Published on · Updated on

Short answer. llama.cpp serves Qwen 3.8 27B as soon as the machine offers at least 15.5 GB of memory, the weight footprint in NVFP4. 8 of the 8 platforms in the catalogue meet that bar.

What is the minimum memory?

Qwen 3.8 27B totals 27 billion parameters. In NVFP4 its weights take about 15.5 GB including the runtime margin: 27 billion parameters × 0.5 byte (NVFP4) = 13.5 GB of weights; × 1.15 runtime margin = 15.5 GB. In FP8 the footprint doubles, to about 31.0 GB (27 billion parameters × 1 byte (FP8) = 27.0 GB of weights; × 1.15 runtime margin = 31.0 GB.) The attention cache is not calculated on this page. These models’ architectures (compressed latent, linear or sparse attention, Mamba layers) share no per-token formula; it is measured on the machine, with the target context and concurrency [TO BE MEASURED].

What is llama.cpp for?

llama.cpp targets workstations and modest hardware. See the llama.cpp and Qwen 3.8 27B fact sheets.

On which platforms?

How much memory for Qwen 3.8 27B with llama.cpp?

About 15.5 GB in NVFP4 for the weights, excluding the attention cache.

Does llama.cpp suit Qwen 3.8 27B?

llama.cpp serves Qwen 3.8 27B as soon as the machine offers at least 15.5 GB of memory, the weight footprint in NVFP4. 8 of the 8 platforms in the catalogue meet that bar.

Method and sources

Memory footprints are calculated, not measured: a reading on real hardware may differ depending on the engine and the exact weight format. Every figure below carries its source and the date of its last check.