Parallel token diffusion replaces sequential generation, trading quality for speed: 1,000+ tokens/sec on H100, 3.8B active parameters fit in 18GB VRAM.
Summary
Cuts latency for latency-sensitive workloads (code infilling, inline editing) without architectural changes to inference pipelines. Enables local deployment on consumer GPUs where standard models won't fit.
Why it matters
Cuts latency for latency-sensitive workloads (code infilling, inline editing) without architectural changes to inference pipelines. Enables local deployment on consumer GPUs where standard models won't fit.
Implementation verdict
Replaces Gemma 2 26B for speed-critical tasks only—acknowledge quality regression on all benchmarks. Requires HuggingFace integration, Unsloth quantization stack, or Nvidia NIM wrapper. Ready to test now; production use depends on tolerance for lower accuracy.
Sources
Dev Signal
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