Custom llama.cpp Build Pushes 7900 XTX Performance
π‘Get practical AMD multi-GPU optimizations for faster Qwen inference, including PCIe compression and tensor parallelism.
β‘ 30-Second TL;DR
What Changed
The build reports up to 920 tokens/second prompt processing for Qwen 3.8 Next Q3_K_XL using two cards.
Why It Matters
The fork could make multi-GPU local inference more practical for developers using AMD hardware, especially when cards are connected through PCIe x4 or a chipset. However, the results are community benchmarks, and the author provides no ongoing support, so production adoption requires careful validation.
What To Do Next
Clone the optimized llama.cpp fork and reproduce its Qwen 3.8 27B Q8_0 benchmark on your RX 7900 XTX PCIe topology before deploying it.
Key Points
- β’The build reports up to 920 tokens/second prompt processing for Qwen 3.8 Next Q3_K_XL using two cards.
- β’Qwen 3.8 27B Q8_0 reportedly reaches 1,600 tokens/second prompt processing and 60β65 tokens/second prose decoding on one chipset-connected card.
- β’Features include compressed Q8_0 PCIe transfers, custom HIP allreduce, adaptive MTP, DFLASH2 tensor parallelism, and AMD-specific kernel tuning.
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Original source: Reddit r/LocalLLaMA β
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