> Canonical page: https://posttrainllm.com/artifacts/huge-decode-throughput

Mac runtime benchmark Report artifact 2026-06-06

# Huge Preset Decode Throughput

This is a runtime artifact, not a model-quality claim. Its value is operational: local eval loops need throughput, stable serving, and cheap repeated generation.

- MLX
- decode
- Mac
- throughput

## Headline Numbers

Evidence class applies to each value: measured = retained evaluation or runtime result; historical = recorded result not freshly reproduced; derived = calculated or decided from records; observed = current artifact state; not-measured = an explicit evidence gap.

Huge decode

 696 tok/s 96M/Huge preset, ctx 1024 historical

Mega pilot

 293 tok/s 960M pilot historical

Warm TTFT p99

 5.8ms reported runtime metric historical

## Competitive Context

**System**

**Metric**

**Score**

**Size / Class**

**Comparable?**

**Readout**

 posttrainllm Huge preset decode throughput 696 tok/s 96M Direct Local runtime baseline for cheap repeated eval/smoke loops.

 posttrainllm Mega pilot decode throughput 293 tok/s 960M Direct Shows the throughput drop as local model size approaches specialist scale.

 External serving stacks same benchmark not measured MLX/llama.cpp/Ollama class Not comparable Needs a shared prompt/config/device table before public competitive serving claims.

Direct rows share this artifact's eval setup. Directional rows are useful market context but should not be read as leaderboard claims.

## Runtime numbers

**Metric**

**Value**

**Use**

 Decode throughput 696 tok/s Fast local eval/smoke loops

 Mega pilot throughput 293 tok/s Boundary mapping for larger local models

 Warm TTFT p99 5.8ms Interactive serving viability

## Release Blockers

### Preset-specific

The headline number is not a blanket claim for all HF models or specialists.

 Unblock: Attach latency/RAM/tok-s numbers to each future specialist artifact.

## Evidence

- [README headline metrics](https://github.com/PostTrainLLM/posttrainllm#headline-results)
- [Performance docs](https://posttrainllm.com/docs/performance/performance/)

## Next Release Action

Use this as the baseline expectation for future artifact performance tables.