Editorial illustration for QuickReduce FP4 delivers ~4.1× speedup over RCCL at TP=4 for large messages
QuickReduce FP4 delivers ~4.1× speedup over RCCL at TP=4...
Training today's massive AI models creates a monumental traffic jam. The gridlock isn't the raw horsepower of the GPUs themselves. It's the agonizingly slow process of shuttling data between them, an operation called all-reduce.
As models swell, these data packets explode into gigabytes. Standard libraries like NVIDIA's NCCL—or AMD's own RCCL—start to seize up.
At large message sizes, QR FP4 achieves roughly 4.1x speedup over RCCL. TP = 4# The figure below shows the TP=4 speedup over RCCL and illustrates that QuickReduce becomes more effective as message size increases. At TP=4, the same pattern holds: CR leads in the low-volume regime, but QR INT4 and QR FP4 become dominant as message size increases.
In the large-message regime, QuickReduce delivers more than 3x speedup over RCCL, with FP4 and INT4 remaining very close. TP = 8# The figure below shows the TP=8 speedup over RCCL, where the crossover point moves to larger message sizes because more GPUs participate in the all-reduce. At TP=8, the crossover point shifts to larger message sizes.
CR remains favorable for small messages, while QuickReduce starts to show clear gains only once the message size reaches the multi-megabyte regime. Even in this more demanding setting, QR INT4 and QR FP4 still provide the highest speedup in the large-message region. Key Observations# From the results, we draw the following conclusions: For message sizes above the crossover point, QuickReduce delivers the highest large-message speedup over RCCL.
At 1 GB message size, QR FP4 achieves 4.14x speedup at TP=2, 3.43x speedup at TP=4, and 1.52x speedup at TP=8. FP4 and INT4 deliver comparable performance -- there is no meaningful throughput difference between the two quantization schemes. At TP=8, the crossover point shifts higher -- QuickReduce begins to outperform CR and RCCL at approximately 4 MB rather than 1 MB, due to the increased coordination overhead with more GPUs.
CR dominates at small message sizes -- for data volumes below ~512 KB, Custom AllReduce consistently delivers the lowest latency across all TP configurations. End-to-End Performance and Accuracy# We selected Qwen3-30B-A3B-Instruct-2507 and DeepSeek-R1-0528 as our test models and used vLLM for both performance and accuracy evaluation.
Benchmarks tell one story. A live training run tells the real one. AMD's engineers validated QuickReduce on the Qwen3-30B and DeepSeek-R1 models using the vLLM engine.
The promised speedups materialized in actual workloads without breaking model accuracy. Sure, for tiny messages under 512KB, the older Custom AllReduce technique still wins. That's irrelevant.
The crippling, project-stalling bottlenecks hit at the multi-megabyte and gigabyte scale. That's exactly where QuickReduce operates. For any team pushing distributed training on AMD's MI355 hardware, this changes the math.
Less time waiting. More time working. In an industry fighting for single-digit percentage gains, that's no mere step forward.
It's a vault.
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Common Questions Answered
What performance improvement does QuickReduce FP4 achieve compared to RCCL at TP=4 for large messages?
QuickReduce FP4 delivers approximately 4.1× speedup over RCCL when handling large messages at tensor parallelism of 4. This significant performance improvement addresses the critical bottleneck that occurs when data packets reach multi-megabyte and gigabyte scales during distributed AI model training.
Why is the all-reduce operation considered a bottleneck in training large AI models?
The all-reduce operation, which shuttles data between GPUs, becomes a major traffic jam as AI models grow larger and data packets expand into gigabytes. Standard libraries like NVIDIA's NCCL and AMD's RCCL start to seize up under this load, making the data transfer process the limiting factor rather than the raw GPU horsepower itself.
Which AI models did AMD use to validate QuickReduce's real-world performance?
AMD's engineers validated QuickReduce on the Qwen3-30B and DeepSeek-R1 models using the vLLM engine. The benchmarked speedups were confirmed to materialize in actual training workloads without compromising model accuracy.
At what message sizes does QuickReduce outperform the older Custom AllReduce technique?
QuickReduce outperforms the older Custom AllReduce technique at multi-megabyte and gigabyte message scales, which represent the actual project-stalling bottlenecks in modern AI training. For tiny messages under 512KB, the older Custom AllReduce technique still maintains an advantage, but this is irrelevant to solving the critical performance constraints.
Further Reading
- QuickReduce: Up to 3x Faster All-reduce for vLLM and SGLang — AMD ROCm Blog
- QuickReduce — GitHub
- vLLM V1 performance optimization — AMD ROCm Documentation
- Faster LLM Inference on AMD Requires Rethinking All-Reduce — DriveNets Blog
- Optimizing Allreduce Operations for Heterogeneous Architectures — arXiv
