BACK_TO_FEEDAICRIER_2
M4 Max MacBook benchmarked for OpenCode, Qwen3
OPEN_SOURCE ↗
REDDIT · REDDIT// 13h agoBENCHMARK RESULT

M4 Max MacBook benchmarked for OpenCode, Qwen3

A developer evaluates the MacBook M4 Max's performance using local LLMs for agentic coding, sharing benchmarks for the Qwen3-30B-A3B model. The results showcase the high-throughput capabilities of the 40-core GPU when paired with modern Mixture-of-Experts architectures in a local development environment.

// ANALYSIS

The M4 Max's unified memory remains the definitive "killer feature" for running 30B+ parameter models at usable speeds on consumer-grade hardware.

  • Benchmarks of ~89 tokens/sec on Qwen3-30B-A3B confirm that MoE models are the sweet spot for high-performance local coding agents.
  • OpenCode is emerging as the premier model-agnostic TUI for developers seeking a local-first alternative to proprietary agents like Claude Code.
  • While 32GB of RAM is viable for 30B models, the community increasingly recommends 64GB+ to accommodate the long-context windows required for multi-file codebases.
  • Switching to MLX-native runners provides a significant 30-50% performance boost over llama.cpp for Qwen models on Apple Silicon.
// TAGS
opencodeai-codingm4-maxmacbookqwen3llmagentopen-source

DISCOVERED

13h ago

2026-04-12

PUBLISHED

14h ago

2026-04-11

RELEVANCE

8/ 10

AUTHOR

AnotherDevArchSecOps