Summary
Fahd Mirza demonstrates the first end-to-end deployment of Llama Box DFlash with Google’s Gemma 4 31B model, following the merge of PR 232 into the Llama Box Hub repository. Until this update, DFlash — a high-performance local inference server built around hand-tuned C++ and CUDA kernels with no Python in the hot path — was exclusively optimized for Qwen 3.5 and 3.6 27B models. The Gemma 4 integration brings the full DFlash stack: speculative decoding, BSA sparse prefill, and prefix caching.
The video walks through the complete setup on Ubuntu with an NVIDIA RTX 6000 (48 GB VRAM), including building the C++ and CUDA decoder targeting SM_86, downloading a quantized GGUF from Bartowski (19.6 GB on disk), and starting an OpenAI-compatible endpoint on localhost port 8080. The headline result: Gemma 4 31B achieves 76.4 tokens per second with speculative decoding enabled, consuming roughly 23 GB of VRAM. Mirza also runs a side-by-side quality comparison — the same HTML5 canvas animation prompt — with and without DFlash to address community questions about output degradation.
Mirza also offers a clear conceptual explanation of speculative decoding: a small draft model guesses 16 tokens ahead, and the large target model verifies all 16 in a single forward pass, yielding dramatically higher throughput with no loss in output quality. Developers looking to squeeze maximum tokens-per-second out of Gemma 4 31B on a single high-end consumer or prosumer GPU will find this a practical, reproducible reference.
📺 Source: Fahd Mirza · Published May 26, 2026
🏷️ Format: Hands On Build
