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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchTo benchmark local LLM inference on a Raspberry Pi 5, build llama.cpp, run llama-bench against a specific GGUF model, and report prompt-processing and token-generation rates separately. Start with a CPU-only run using -ngl 0; results are meaningful only alongside the board, model, build, workload, thread count, and thermal and power conditions that produced them.
What the benchmark measures
llama-bench reports distinct workload types: prompt processing (pp), text generation (tg), and combined prompt-plus-generation (pg). Keep their results separate. Prompt processing measures how quickly the model handles input tokens; generation measures output-token throughput. A combined result answers a different question and should be labeled accordingly.
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The benchmark’s token-per-second measurements exclude tokenization and sampling time, according to the llama.cpp benchmark documentation. They therefore are not end-to-end application latency and should not be presented as such.
The Tool Desk
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Use a GGUF model supported by the llama.cpp build you intend to benchmark. Before testing, confirm that the model artifact and its context fit the Pi 5’s available memory; there is no single model size established as suitable for every memory configuration and workload.
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Record these details with every result:
- Raspberry Pi 5 memory configuration, operating system, and thermal and power conditions.
- Exact model repository or source, GGUF filename, and quantization.
- llama.cpp revision, build options, and backend.
- Thread count and any changed context, batch, or other benchmark settings.
- Prompt and generation token counts, repetition count, and whether the metric is
pp,tg, orpg.
Build llama.cpp for the Pi
Use the upstream llama.cpp build guide for the current prerequisites and CMake instructions. Project options and defaults can change, so note the checked-out revision and the options used rather than treating an old command as timeless. Backend-related build options matter: a CPU result and an accelerated-backend result are not directly interchangeable.
Run a repeatable CPU-only baseline
After placing the selected model at models/model.gguf, this documented-options example requests a CPU-only benchmark with separate prompt-processing, generation, and combined workloads:
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./build/bin/llama-bench
-m models/model.gguf
-ngl 0
-p 512
-n 128
-pg 512,128
-t 4
-r 5
-o jsonlHere, -ngl 0 requests no GPU-layer offload; -p 512 sets the prompt length, -n 128 sets the generation length, and -pg 512,128 requests a combined test using those lengths. -t 4 sets four threads, -r 5 repeats each test five times, and -o jsonl selects JSON Lines output. These are example workload values, not a claim that they suit every model or research question. The available modes and options are described in the benchmark documentation.
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Keep comparisons fair
For a configuration comparison, change one variable at a time and hold the rest constant: board and memory, model file and quantization, llama.cpp revision and build, backend, thread count, prompt and generation lengths, context depth, and other benchmark options. Repeat runs with -r and retain the JSONL output. The tool reports average tokens per second and standard deviation; include variability or individual repetitions rather than presenting an average without context.
Published Pi 5 figures illustrate why the setup must travel with the number:
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| Published result | Reported workload and setup | What the figure means |
|---|---|---|
| 24 tokens per second | Raspberry Pi’s September 2026 article reports a llama.cpp Q4_0 result using 1,024 prefill tokens, 256 decode tokens, and four CPU threads. | A result for that article’s model and workload, not a prediction for other models, quantizations, or token lengths. Raspberry Pi article |
3.91 tokens per second for tg64; 27.77 tokens per second for pp17; about 16,998 ms for pp17+tg64 |
A 2026 mudler / vllm.cpp benchmark report describes a Qwen3.5-2B GGUF setup, four threads, and a named llama.cpp build. | Measurements for that report’s exact setup, not general Pi 5 performance guarantees. mudler / vllm.cpp benchmark report |
These workloads differ substantially, so their rates are not a like-for-like comparison. Before drawing conclusions across sources, align or disclose the model, quantization, prompt and generation lengths, build, thread count, backend, and measurement type.
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Report results with their limits
A useful report gives prompt-processing and generation tokens per second separately, and includes combined throughput only when relevant. State the number of repetitions and provide the standard deviation or individual results. Identify tokenization and sampling as excluded from llama-bench measurements so readers do not mistake throughput for full request latency.
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Attach the complete configuration to any quoted result. A number without its model, quantization, workload lengths, threads, backend, and build cannot establish how another Pi 5 setup will perform.
Treat Vulkan or other acceleration as an experiment
A CPU-only run with -ngl 0 is a clear baseline. Do not assume Raspberry Pi 5 VideoCore Vulkan offload will work or improve a result. A 2026 llama.cpp issue describes workgroup-size and shared-memory constraints for the Pi 5 V3D Vulkan path; an earlier issue also records Vulkan problems. Issue reports are warnings, not a complete compatibility matrix.
For an offload test, report the exact llama.cpp revision, Mesa and driver versions, build configuration, model, and offload settings. Check output correctness as well as speed; a throughput number is not useful if the run did not produce valid results.
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