๐ฆReddit r/LocalLLaMAโขStalecollected in 62m
Dual 7900 XTX Hits 123 tok/s on Qwen3.5-35B
#amd-gpu#moe-inference#llama-benchllama.cpp-vulkan-on-dual-rx-7900-xtxrx-7900-xtxqwen3.5-35bllama.cppvulkan
๐ก123 tok/s on dual AMD GPUs beats NVIDIAโkey for local 35B MoE runs
โก 30-Second TL;DR
What Changed
Dual 7900 XTX Vulkan: 123 tok/s TG128, 2,647 tok/s PP512 on Qwen3.5-35B-A3B Q4_K_M
Why It Matters
Demonstrates Vulkan backend maturity on AMD GPUs for high-speed MoE inference, making dual consumer cards viable for 35B models. Highlights llama.cpp superiority over vLLM on ROCm for now.
What To Do Next
Benchmark your dual 7900 XTX with llama.cpp Vulkan build b8516 on Qwen3.5-35B.
Who should care:Developers & AI Engineers
Key Points
- โขDual 7900 XTX Vulkan: 123 tok/s TG128, 2,647 tok/s PP512 on Qwen3.5-35B-A3B Q4_K_M
- โขBeats single 7900 XTX HIP (76-78 tok/s) and RTX 3090 CUDA (111 tok/s)
- โขvLLM ROCm broken: garbage output at 5 tok/s, OOM issues
- โขllama.cpp HIP graphs: 86 tok/s TG, solid alternative
๐ง Deep Insight
AI-generated analysis for this event.
๐ Enhanced Key Takeaways
- โขThe Qwen3.5-35B-A3B model utilizes a Mixture-of-Experts (MoE) architecture with 3 billion active parameters, which is the primary driver for the high token throughput compared to dense models of similar total parameter counts.
- โขThe performance disparity between Vulkan and ROCm backends on AMD hardware is largely attributed to the maturity of the Vulkan memory management layers in llama.cpp versus the ongoing instability of the ROCm-specific kernels for RDNA3 architectures in vLLM.
- โขDual 7900 XTX configurations benefit significantly from the 24GB VRAM per card (48GB total), allowing the Q4_K_M quantized model to reside entirely in VRAM, avoiding the massive latency penalties associated with PCIe bus offloading.
๐ Competitor Analysisโธ Show
| Feature | Dual RX 7900 XTX (Vulkan) | RTX 3090 (CUDA) | RTX 4090 (CUDA) |
|---|---|---|---|
| VRAM | 48GB | 24GB | 24GB |
| Est. Perf (Qwen3.5-35B) | ~123 tok/s | ~111 tok/s | ~145-155 tok/s |
| Backend Maturity | High (llama.cpp) | Excellent (Native) | Excellent (Native) |
| Power Efficiency | Low | Medium | High |
๐ ๏ธ Technical Deep Dive
- Model Architecture: Qwen3.5-35B-A3B is a sparse MoE model. The 'A3B' designation indicates 3 billion active parameters per token, significantly reducing the compute requirement per forward pass compared to a dense 35B model.
- Backend Comparison: Vulkan implementation in llama.cpp bypasses the complex ROCm driver stack, utilizing a more direct abstraction layer that currently exhibits better stability for multi-GPU RDNA3 setups.
- Memory Bottleneck: The 384-bit memory bus of the 7900 XTX provides 960 GB/s of bandwidth per card; in a dual configuration, the effective bandwidth for model weights is critical for maintaining high token generation rates during MoE expert switching.
๐ฎ Future ImplicationsAI analysis grounded in cited sources
AMD will prioritize Vulkan-based LLM optimization in future ROCm releases.
The consistent performance lead of Vulkan over ROCm in community benchmarks forces AMD to address the abstraction layer gap to remain competitive in the local AI market.
Multi-GPU consumer setups will become the standard for running 30B+ parameter models locally.
As model sizes grow, the VRAM capacity of dual-consumer cards provides a more cost-effective path to high-speed inference than single high-end professional cards.
โณ Timeline
2022-12
AMD releases the Radeon RX 7900 XTX based on the RDNA 3 architecture.
2023-06
llama.cpp adds initial support for AMD HIP/ROCm, enabling local LLM inference on Radeon hardware.
2024-05
llama.cpp introduces Vulkan backend support, providing an alternative to ROCm for AMD GPU users.
2026-01
Qwen3.5 model series is released, featuring advanced MoE architectures.
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Original source: Reddit r/LocalLLaMA โ