Qwen3.5-35B-A3B Benchmarks Reveal Optimal Quantization for RTX 5080 Consumer AI Workstations
New benchmarks on the Qwen3.5-35B-A3B model reveal that Q4_K_M quantization delivers the best balance of speed and accuracy on a consumer-grade RTX 5080, while Unsloth's UD-Q4_K_XL underperforms. The study also uncovers a 7% performance gain from manual CPU-GPU offloading over auto-fit tools.
Qwen3.5-35B-A3B Benchmarks Reveal Optimal Quantization for RTX 5080 Consumer AI Workstations
summarize3-Point Summary
- 1New benchmarks on the Qwen3.5-35B-A3B model reveal that Q4_K_M quantization delivers the best balance of speed and accuracy on a consumer-grade RTX 5080, while Unsloth's UD-Q4_K_XL underperforms. The study also uncovers a 7% performance gain from manual CPU-GPU offloading over auto-fit tools.
- 2Optimizing Large Language Models on Consumer Hardware: Qwen3.5-35B-A3B Benchmarks on RTX 5080 A groundbreaking performance analysis of the Qwen3.5-35B-A3B large language model on a consumer-grade NVIDIA RTX 5080 workstation has revealed critical insights into quantization efficiency, offloading strategies, and real-world inference speed.
- 3Conducted by an independent AI researcher using llama.cpp and published on the r/LocalLLaMA subreddit, the benchmarks provide a rare, detailed look at how state-of-the-art open-weight models perform under realistic hardware constraints—particularly on systems with limited VRAM.
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Optimizing Large Language Models on Consumer Hardware: Qwen3.5-35B-A3B Benchmarks on RTX 5080
A groundbreaking performance analysis of the Qwen3.5-35B-A3B large language model on a consumer-grade NVIDIA RTX 5080 workstation has revealed critical insights into quantization efficiency, offloading strategies, and real-world inference speed. Conducted by an independent AI researcher using llama.cpp and published on the r/LocalLLaMA subreddit, the benchmarks provide a rare, detailed look at how state-of-the-art open-weight models perform under realistic hardware constraints—particularly on systems with limited VRAM.
According to the original benchmark report, the Qwen3.5-35B-A3B model, despite its 35 billion parameters, was successfully run on a single RTX 5080 16GB GPU with PCIe 5.0 offloading to an AMD Ryzen 9 9950X CPU. The system, running Ubuntu 24.04.3 with CUDA 13.1 and a custom-built llama.cpp binary, demonstrated that even models too large for VRAM can achieve high throughput through intelligent layer offloading.
Quantization Quality: Q4_K_M Outperforms UD-Q4_K_XL
Quantization benchmarks using WikiText-2 perplexity (PPL) showed that the standard Q4_K_M quantization delivered a minimal 2.1% degradation in quality compared to the full-precision Q8_0 baseline (PPL: 6.6688 vs. 6.5342), while reducing model size from 36.9GB to ~20GB. In stark contrast, the recently promoted UD-Q4_K_XL quantization—marketed by Unsloth as a high-efficiency alternative—achieved a significantly higher PPL of 7.1702, a 9.7% drop in quality, despite a nearly identical file size. This finding aligns with prior observations on MoE-based architectures like Qwen3-30B-A3B, suggesting that dynamic quantization techniques may not generalize well across all model types.
"If you're running Qwen3.5-35B-A3B at Q4, use standard Q4_K_M," the researcher concluded, warning against the adoption of newer, less-tested quantization formats without empirical validation.
Speed Optimization: Manual Offloading Beats Auto-Fit
Speed benchmarks under 65K context length revealed dramatic performance differences based on offloading strategy. The Q4_K_M model with full CPU offloading achieved 49.8 tokens per second (tok/s), while the same model with partial GPU offloading using --n-cpu-moe 24 surged to 67.0 tok/s—a 34% increase. Notably, the newer --fit on auto-optimization feature in llama.cpp delivered 62.3 tok/s, but still fell 7% short of the manually tuned configuration.
The sweet spot for the 16GB RTX 5080 was found to be 24 MoE layers offloaded to CPU, with 16 remaining on GPU. Lower values (e.g., 16) caused out-of-memory crashes, while higher values (32) resulted in underutilization of GPU capacity. The researcher emphasized that "hand-tuning beats auto-fit," particularly for MoE architectures where layer distribution is non-uniform.
KV Cache Optimization: A Free Lunch for Throughput
Another major discovery was the negligible cost and substantial benefit of using Q8_0 quantization for the key-value (KV) cache. Switching from FP16 to Q8_0 KV caches increased throughput by 12–38% while reducing VRAM usage—without measurable impact on output quality. The researcher recommends always enabling -ctk q8_0 -ctv q8_0 as a "free lunch" optimization.
Implications for the AI Community
These findings are significant for developers, researchers, and hobbyists seeking to deploy large models on consumer hardware. With no official release of the RTX 5080 as of this writing, the benchmark assumes a hypothetical Blackwell-based GPU—likely a speculative or prototype configuration. However, the methodology and conclusions remain valid for any PCIe 5.0 system with 16GB VRAM and a high-core-count CPU.
The study underscores a critical principle: model performance is not determined solely by architecture or quantization labels, but by the synergy between hardware, software stack, and fine-tuned configuration. As AI models grow larger and more complex, such granular, reproducible benchmarks will become indispensable for practical deployment.
