arXiv:2606.08761v1 Announce Type: cross Abstract: W4A4 quantization promises full utilization of INT4 Tensor Cores, yet group dequantization overhead on CUDA Cores has driven existing systems to mixed-precision fallbacks. We present the first systematic study of how intra-SM compute balance governs this bottleneck. Through controlled benchmarks across four GPUs from Ampere and Ada architectures, we identify the Tensor Cores to CUDA Cores throughput ratio ($\rho$) as the primary hardware indicator: the W4A4-g128 kernel yields $2.0$--$2.5\times$ speedup on RTX~3090 ($\rho=16$) yet degrades to $0
Rapid advancements in AI model size and complexity necessitate more efficient inference methods, pushing research into novel quantization and hardware utilization techniques.
Improved W4A4 quantization efficiency allows for significantly faster and more memory-friendly LLM inference, making advanced AI models more accessible and cost-effective.
The ability to fully leverage INT4 Tensor Cores through optimized intra-SM compute rebalancing dramatically boosts W4A4 inference performance, especially on specific GPU architectures.
- · AI developers
- · Cloud AI providers
- · NVIDIA (Ada architecture)
- · Edge AI computing
- · AI models requiring high precision
- · Less efficient quantization methods
Widespread adoption of W4A4 quantization for large language models, reducing compute costs and increasing deployment flexibility.
Accelerated development and fine-tuning of larger and more complex AI models due to reduced inference barriers.
Disruption to some AI accelerator markets if NVIDIA's current architectures gain a significant performance edge in efficient low-bit inference.
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Read at arXiv cs.AI