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REDDIT · REDDIT// 3h agoMODEL RELEASE
Qwen 3.6 Q4 hits 112 t/s in Pi harness
LocalLLaMA users report exceptional reliability and speed with Qwen 3.6's Q4 quantization, which handles 131k context windows flawlessly at over 110 tokens per second. The model is being paired with Mario Zechner's minimalist "Pi" coding harness for high-performance agentic workflows that rival cloud-based LLM latency.
// ANALYSIS
The sparse MoE architecture in Qwen 3.6-35B-A3B is a game-changer for local agentic workflows, proving that Q4 quantization is now the professional-grade sweet spot for speed and context stability.
- –Q4 quantization offers a massive 2.2x speedup (112 t/s vs 50 t/s) with negligible impact on coding logic, allowing for near-instant agent feedback loops.
- –The model maintains remarkable consistency through multiple "compacting" cycles at 131k context, solving the long-context retrieval issues that plagued earlier 2.5-series quants.
- –Using the minimalist Pi harness reduces system prompt overhead to under 1,000 tokens, maximizing the available window for repository-level reasoning.
- –Sparse MoE (3B active parameters) allows for high-throughput inference on consumer GPUs while retaining the logic depth of a much larger dense model.
- –For most coding tasks, the "perplexity tax" of Q4 is offset by the ability to run more agentic verification loops per minute compared to the slower Q8.
// TAGS
qwen-3-6llmai-codinglocal-llminferencequantization
DISCOVERED
3h ago
2026-04-17
PUBLISHED
6h ago
2026-04-17
RELEVANCE
8/ 10
AUTHOR
GotHereLateNameTaken