llama3.2 on M1 Pro (16-core GPU) + 16GB unified
MeasuredOn 2026-08-18, llama3.2 Q8_0 ran at 103.1 tok/s (chat-short) on an M1 Pro (16-core GPU) with ollama. Run r_w-p7v--eync.
M1 Pro (16-core GPU) + 16GB unified
suite suite-v1
cli 0.0.5
signed8raoNQEcn3…
Embed badgesubmitted Aug 18, 2026Workload results
| Workload | Backend | Model | decode tok/s | prefill tok/s | TTFT | p50 | p95 |
|---|---|---|---|---|---|---|---|
| chat-short | ollama@0.32.14 | llama3.2Q8_0 | 103.1tok/s | 8.77tok/s | 14,247ms | 9.4ms | 10.5ms |
| chat-long | ollama@0.32.14 | llama3.2Q8_0 | 81.35tok/s | 1,246.5tok/s | 2,529ms | 11.5ms | 14.9ms |
| concurrent-decode | ollama@0.32.14 | llama3.2Q8_0 | 97.26tok/s | — | — | 9.9ms | 12.5ms |
| agent-trace | ollama@0.32.14 | llama3.2Q8_0 | 93.14tok/s | 2,975.3tok/s | 516ms | 10.5ms | 12.9ms |
Reproduce on your machine
Start with this model and workload selection. This command does not capture the original artifact, backend version or server settings:
Match quantization, context capacity, caching and runtime settings for a useful comparison. Duration varies by workload and hardware; the published client may differ from this run's version. How it's measured.
Share this run
Copy the short link, a Reddit-ready summary, or a README badge. Every number is the measured result on this page, and each badge links back to it.
https://llm-speed.com/r/r_w-p7v--eyncRelated benchmarks
- More llama3.2 benchmarks — every backend and rig that has run this model.
- More M1 Pro (16-core GPU) LLM benchmarks — every model measured on this hardware.
Provenance
- Run ID
- r_w-p7v--eync
- Fingerprint hash
- 43f99b877c2d9bba
- Public key
- 8raoNQEcn33R/v+VSYxZsgizfls+6Rnx9z8JnDkiXEs=
- Received
- 2026-08-18 23:17:20