30B models at 256k context crash on 32GB Macs, practitioners report

Stability remains the biggest obstacle to reliable long-context work on Apple Silicon, even with current-generation 30B-parameter models like Gemma 4 and Qwen 3.6, according to a practitioner stress-testing the limits on a 32GB M2 Max MacBook Pro.

The user reports testing "literal hundreds of settings" across llama.cpp, oMLX, and other inference engines, but consistently hits the same wall: server crashes when context windows approach 256k tokens, cache misses that push latency into the minutes, and failures that only surface under stress testing. The use case—summarization and note organization for a memory system—requires both long context and multi-turn stability, a combination that appears to exceed what 32GB of unified memory can reliably deliver at that scale. A shared llama.cpp configuration file documents the tuning attempts, but no combination of quantization level, batch size, or cache strategy has produced a stable result.

Recent developments—MLX performance updates, Turboquant compression, and MTP (Multi-Token Prediction) optimizations—have raised expectations that 30B models at extreme context lengths should be viable on Apple Silicon. The reality, according to this report, is that the software stack hasn't caught up. Most 30B models at Q4 quantization consume roughly 18–22GB for weights alone, leaving 10–14GB for context cache. At 256k tokens, even with efficient KV cache compression, memory pressure becomes acute. The question now circulating among Mac-based practitioners is whether the bottleneck is the hardware limit, the inference engine implementations, or the models themselves—and whether any combination of the three can deliver the stability needed for production agentic workflows.