#rdna4

Vulkan on Intel Battlemage lands within 12% of dual-Radeon AI PRO R9700 for code generation at Q4_K_M, at roughly half the system cost. Arc is the price-performance leader for sub-$2K builds; multi-GPU AMD wins raw throughput once you accept the complexity.

MTP speculative decoding on Qwen3.6-27B (single Radeon AI PRO R9700, llama.cpp Vulkan) holds 55-65 tok/s generation across 8K-80K context - degrading roughly 1.3x over that range - while prompt processing falls from ~760 to ~580 tok/s as context fills. The practical finding: MTP keeps working at long context (acceptance 66-75%), but KV-cache pressure steadily erodes both prompt and generation speed. Founder's prior short-prompt runs hit ~75-86 tok/s; the numbers here use real document prompts and are correspondingly a bit lower.

On a single Radeon AI PRO R9700 (gfx1201, RDNA4), llama.cpp Vulkan with the MTP speculative-decoding head is the only path that activates Qwen3.6's Multi-Token Prediction — delivering 66 tok/s, roughly 2.5x the 27 tok/s class of every other backend. If you don't load the -mtp.gguf draft head, Vulkan, HIP, and Ollama all land within a narrow 26-32 tok/s band and the architecture's main speed advantage is left on the table.

For the Ornith-1.0-35B MoE model (qwen35moe family) on a single Radeon AI PRO R9700, Ollama is the clear winner at 78.3 tok/s - roughly 2.9x faster than llama.cpp Vulkan (27.3 tok/s), which also spilled past the 32GB VRAM limit into system RAM. The HIP backend failed outright: it could not parse the Ornith GGUF (version mismatch). On MoE models without an MTP head, Ollama's expert routing currently beats hand-tuned llama.cpp on this build.