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Old Zhang's AI Learning
Old Zhang's AI Learning
Apr 19, 2026 · Artificial Intelligence

Qwen3.6-35B: 4‑bit Quantization, DFlash Speedup, Claude Opus Distillation

The article reviews three optimization paths for the Qwen3.6‑35B model—four‑bit AWQ quantization variants, the DFlash speculative decoding accelerator, and a Claude Opus‑based distillation—detailing their implementation steps, benchmark results, and guidance on selecting the best version for different hardware and performance needs.

AIDFlashDistillation
0 likes · 11 min read
Qwen3.6-35B: 4‑bit Quantization, DFlash Speedup, Claude Opus Distillation
Old Zhang's AI Learning
Old Zhang's AI Learning
Apr 17, 2026 · Artificial Intelligence

How DFlash Achieves 8× Lossless Acceleration for Large‑Model Inference (Qwen3.5‑27B Example)

The article explains how DFlash’s block‑diffusion draft model and KV Injection boost speculative decoding speed by 5‑8× without sacrificing output quality, and how DDTree further raises the gain to over 8×, backed by benchmark results and integration guides for major inference frameworks.

DDTreeDFlashLarge Language Model Inference
0 likes · 7 min read
How DFlash Achieves 8× Lossless Acceleration for Large‑Model Inference (Qwen3.5‑27B Example)
Old Zhang's AI Learning
Old Zhang's AI Learning
Apr 14, 2026 · Artificial Intelligence

Qwen3.5-27B-DFlash Delivers Up to 5× Faster Inference Without Quality Loss

The DFlash approach replaces speculative decoding’s autoregressive drafter with a block diffusion model and injects target‑model hidden features into every KV‑cache layer, achieving up to 5× speed‑up for Qwen3.5‑27B on single‑GPU and 1.5–1.9× on high‑concurrency workloads while preserving output quality.

DFlashInference AccelerationQwen3.5
0 likes · 12 min read
Qwen3.5-27B-DFlash Delivers Up to 5× Faster Inference Without Quality Loss