Dr. Mira Osei
2026-02-28
How modern attention mechanisms and speculative decoding combine to achieve 3× throughput at 40% cost on production LLM workloads.
The naive approach to scaling language model inference is simple: throw more GPUs at it. But at the 10B+ parameter scale, this strategy becomes financially untenable. At AXON, we’ve spent the last eighteen months developing a more surgical approach — combining speculative decoding with custom attention kernels to achieve throughput gains that would otherwise require tripling hardware spend.
The Core Insight
Transformer inference is memory-bandwidth bound, not compute-bound. This distinction matters enormously for how you approach optimization…