ZYFA: Fast and Accurate FP8 Attention on AMD MI355X
Zyphra presents ZYFA, FP8 and BF16 prefill attention kernels for AMD Instinct MI355X GPUs.
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Introducing MAIA: AI That Makes Teams Better Together
Our mission at Zyphra is to make open superintelligence accessible to everyone through a full stack spanning frontier models, multisilicon infrastructure, and agentic applications. Today, we're excited to introduce MAIA in beta, the application layer that brings this vision directly to people and teams. MAIA is a proactive, multiplayer AI agent that remembers shared context, takes action across tools, and helps teams turn knowledge into outcomes. This beta is an early step toward making powerful intelligence more accessible, useful, and under the control of those who use it. We invite you to try MAIA and help shape what comes next.
ZHA: Zyphra Hierarchical Attention
We introduce Zyphra Hierarchical Attention (ZHA), a sparse attention method that constructs and searches through a hierarchical tree to determine the relevant set of tokens for each query. ZHA achieves subquadratic complexity with suitably chosen top-p thresholds for routing, outperforming existing sparse-attention methods which maintain a quadratic indexer. We demonstrate that we can adapt existing pretrained models to ZHA through a distillation phase using only a few billion tokens, and that ZHA maintains performance on challenging long-context reasoning, agentic coding, and retrieval benchmarks at high levels of sparsity. Our performance model shows that ZHA dramatically outperforms sparse attention methods in speed and computational cost at ultra-long contexts.
Zyphra Inference Wave 2: Faster MoE and Long-Context Serving on MI355X
Zyphra presents the second wave of inference optimizations in the Zyphra Optimization Engine (ZOE) for AMD Instinct MI355X GPUs. We measure the empirical compute and memory-bandwidth limits of the MI355X, write custom HIP kernels for mixture-of-experts layers and sparse attention in our production forks of vLLM and SGLang, and evaluate MXFP4 quantization and speculative decoding. ZOE speeds up GLM-5.3 time to first token at 512K tokens of context by 2.8x with no prefix-cache hits and by 3.6x at a 90% hit rate, and it raises DeepSeek-V4-Pro single-user decode throughput at 1M tokens of context by 1.40x.