inference/research
verifiedmedium · 0.80updated 2026-10-18

llama.cpp Metal on Apple M1 (base, 7- or 8-core GPU) (8 GPU): Llama-2-7B Q4_0 pp512 tok/s (scoreboard #4167, 8e672ef)

117.96 tok/s (pp512, batch 512, per-request)

Fields

id
benchmarks/llamacpp-metal-m1-8core-llama27b-q4-0-pp512-8e672ef
Metric
prefill_tok_s
Value
117.96
Unit
tok/s (pp512, batch 512, per-request)
Model
Llama-2-7B gguf-q4-0.gguf
Methodology
COMMUNITY-SCOREBOARD (llama.cpp discussion #4167, ggerganov-maintained). llama-bench -hf ggml-org/Llama-2-7B-GGUF -p 512 -n 128 -ngl 99 --delay 30, Metal backend, full GPU offload. Build commit 8e672ef. pp512 = prefill of one prompt at batch 512; tg128 = batch-1 decode of 128 tokens. mean over the default -r 5. Per-request, NOT aggregate. Chip: Apple M1 (base, 7- or 8-core GPU), 8 GPU cores, 68 GB/s unified-memory bandwidth as printed in the scoreboard.
Measured by
third_party
Reproducible
yes
notes
Maintainer-curated Apple Silicon llama-bench table (discussion #4167), the same Llama-2-7B weights and pp512/tg128 protocol as the CUDA/ROCm scoreboards. This row is the 8-core GPU bin; the other bin of the same die is a separate record. F16 cells the scoreboard leaves blank are not filed.

Notes

Maintainer-curated Apple Silicon llama-bench table (discussion #4167), the same Llama-2-7B weights and pp512/tg128 protocol as the CUDA/ROCm scoreboards. This row is the 8-core GPU bin; the other bin of the same die is a separate record. F16 cells the scoreboard leaves blank are not filed.

Cross-references