DFlash Boosts 30B Model Speed to 84 tok/s on Single GPU via Speculative Decoding
A developer tested the DFlash speculative decoding method on a Meta Muse Glimmer 30B model running on an NVIDIA RTX PRO 4000 GPU with a 256K context window. Using a five-layer drafter generating up to 15 candidate tokens per block, DFlash pushed decode speed from a baseline of 17.98 tokens per second to over 84 tokens per second on structured code tasks. Performance varied significantly by workload, with code generation achieving high acceptance rates while mixed agent tasks involving planning and prose dropped to around 38 tokens per second. Two separate integration bugs in the NVFP4 quantization pipeline — a skipped RoPE permutation and missing FFN scale paths — were found to corrupt output while the model still loaded without errors. Ultimately, Q5_K_M quantization was selected as the best overall configuration, balancing perplexity, throughput, and acceptance rate.
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