Breaking Through Inference Barriers with JAX: A Mamba-2 Case Study

Inference workloads are often capped by reliance on specialized kernels. Mamba-2 is challenging this paradigm. By leveraging state space duality (SSD) recurrence, it introduces a compiler-friendly approach that sidesteps custom kernels. This method utilizes JAX primitives and achieves remarkable efficiencies.

What Mamba-2 Delivers

On a single Google Cloud TPU v6e, Mamba-2's batch-1 prefill reaches an impressive 140 TFLOPS. That's about 15% of the model FLOP utilization, brushing up against the theoretical ceiling in this setup. cached decode, the utilization rate jumps to 64% of the hardware bandwidth, a significant leap in efficiency.

Context matters, and at a 4096-token context, the cached decode demonstrates being 27 to 36 times faster than traditional full-prefix recomputation. This improvement spans across five Mamba-2 checkpoints ranging from 130 million to 2.7 billion parameters. Such advancements illustrate that the real bottleneck isn't the model. it's the infrastructure.

Portability Across Platforms

Another standout feature of this approach is its portability. The same single-source code runs seamlessly on NVIDIA L40S, maintaining sequence-length independence across different model scales. This flexibility is essential as it simplifies deployment across various hardware platforms without sacrificing performance.

the validation performance on WikiText-103 is within a hair's breadth of the Triton reference, underscoring the approach's accuracy. Hidden states are consistent, down to float32 rounding tolerance. Cloud pricing tells you more than the product announcement. The economic implications are clear: more performance per dollar spent on compute resources.

Why It Matters

So, why should we care about these technical intricacies? The answer lies in throughput and efficiency. As companies scale their AI workloads, the economics of inference at scale can't be ignored. With increased utilization, costs drop, and performance gains translate directly into business value.

Can the industry afford to ignore such leaps in inference efficiency? Probably not, especially when these advancements pave the way for more scalable and economically viable AI solutions. Every organization with a heavy AI footprint should follow the GPU supply chain closely, as these hardware capabilities will dictate what's possible in AI applications.

In sum, Mamba-2's deployment using JAX primitives is a significant step forward in inference technology. By improving throughput and utilization, it's setting a new bar for what can be achieved on existing hardware.