Ling-3.0 7.9B (1.3B active) Tiny

User benchmarks Ling 3.0 tiny against Gemma 4 26B-A4B for batched JSON speaker attribution on an NVIDIA T1000 8GB. Ling 3.0 tiny runs at 245 t/s prefill and 54 t/s decode, 1.8x faster end to end than Gemma. Setup is llama.cpp with the official Q4_K_M GGUF at 16384 context, all 25 layers on the GPU, no speculative decoding. Gemma uses UD-Q4_K_XL with experts in system RAM and an MTP draft head. Ling scores 56.9% exact speaker match versus Gemma's 94.6% across 485 lines, with narrator swaps and refusals. User is not adopting it.