Summary
Fahd Mirza follows up his full-precision Muse Glimmer 30B video with a focused benchmark of the GGUF quantized version running with DFlash speculative decoding on an Nvidia A6000 48GB GPU via llama.cpp. The quantized model fits into just under 21GB of VRAM, making it deployable on consumer 24GB cards. Before testing the speed claims, Mirza establishes a careful baseline: without the DFlash drafter, the model runs at approximately 31.5 tokens per second; with speculative decoding enabled, he measures the uplift under real conditions and discusses when gains are largest (predictable, structured text) versus minimal (unusual or highly variable sequences).
Beyond speed benchmarking, the video runs two substantive agentic evaluations. First, a multi-turn autonomous bug-fixing task using the Hermes agent framework, where Muse Glimmer identifies bugs in a web application, writes its own tests, verifies them, and confirms the fix via curl — a roughly 40-minute end-to-end run that Mirza walks through in detail, including a context window error he hits and resolves live. Second, a code generation challenge requiring a self-contained HTML infographic with physics calculations, animated SVG charts, and multiple interactive tabs, which the quantized model completes without getting sidetracked.
The video is a practical setup guide for anyone wanting to run Muse Glimmer locally on affordable hardware, including instructions for upgrading llama.cpp to support the new architecture and configuring the DFlash drafter file alongside the main weights.
📺 Source: Fahd Mirza · Published August 11, 2026
🏷️ Format: Benchmark Test
