Improve throughput without replacing cards

The real moat is shifting from silicon to serving software, because the easiest performance gain in inference often comes from packing work onto the same card more efficiently, not from buying a new one. Furiosa’s stack adds hybrid batching, prefix caching, and Kubernetes native scheduling, which means the same RNGD card can spend less time idle, reuse repeated prompt work, and handle more live requests before a customer needs another hardware purchase.1 2 3 4 5

• In practice, this means the serving layer decides which requests to group together, which pods should handle them, and when cached prompt state can be reused. Furiosa’s llm-d integration adds prefix cache aware routing and separate prefill and decode placement across Kubernetes clusters, improving card utilization without touching the board itself.2 4 5
• This is the same general playbook used across the category. NVIDIA pushes throughput with in flight batching and KV cache reuse in TensorRT-LLM, and AMD highlights vLLM and llm-d based cache and batching optimizations in ROCm. The competitive fight is no longer only whose chip is faster, but whose software wastes less memory and less time between tokens.6 7 8 9
• That matters for how money flows. A customer that can double requests per second through a software update delays a card replacement cycle and lowers cost per served token. For Furiosa, stronger software also reduces the integration work between a successful benchmark and a production deployment, which is where many new chips lose momentum.1 3 10 11

The next step is that inference hardware vendors increasingly sell a full operating stack, not just a PCIe card. As models get larger and traffic gets spikier, more of the performance battle will be won by request scheduling, cache management, and cluster orchestration, which gives Furiosa a path to keep raising output per installed card base over time.2 4 5 10 11