Summary
Fahd Mirza demonstrates how to enable multi-token prediction (MTP) on Qwen3.6 27B using ik_llama.cpp — a community fork of the popular local inference engine — before the feature lands in mainline llama.cpp. The setup runs on an NVIDIA RTX 6000 with 48GB of VRAM, uses a Q4KM quantized model file just over 16GB, and requires building the fork from source (approximately 30–60 minutes) plus three extra command-line flags to enable MTP at runtime.
The core result: MTP delivers roughly a 20% throughput boost over the baseline of 34.2 tokens per second for Qwen3.6 27B, with no second model file required. Mirza also provides a clear architectural comparison to DFlash (covered in a prior video), which achieves approximately 3x speedups by using a separate small draft model for speculative decoding. MTP wins on simplicity — one GGUF file, minimal configuration — while DFlash wins on raw speed at the cost of a more complex two-model setup.
For developers running local inference on consumer or prosumer GPUs who want a quick performance bump without restructuring their stack, MTP via ik_llama.cpp represents a low-friction option. The video includes step-by-step build commands, a before/after server comparison, and a code snippet for benchmarking output tokens per second against the running endpoint.
📺 Source: Fahd Mirza · Published May 10, 2026
🏷️ Format: Benchmark Test
