wafer

Wafer's agent profiles the full serving path rather than a single layer, pinpointing whether latency or throughput is coming from scheduling, decoding, kernels, memory pressure, or an awkward hardware fit. That diagnosis drives everything that follows, so changes target the real bottleneck instead of a guess. For a team stuck at a latency plateau, it's the difference between guessing and measuring. No more blind tweaks.

The agent generates many candidate setups across batching, decoding, quantization, serving engines, kernels, and hardware, and then it measures each one under conditions close to your real workload so the winner reflects what your traffic actually does rather than a clean lab result. Nothing ships on theory alone. This is the core reason the platform exists: what looks fast on paper often loses to a different combination in practice. Trust the numbers, not the intuition.

Wafer writes fused ops, attention paths, GEMM variants, and decode kernels tuned to a specific model shape and hardware target. These are CUDA kernels and their equivalents, and the same approach covers HIP, Triton, and NKI, so it spans NVIDIA, AMD, and other accelerator stacks. Custom kernels matter most when an off-the-shelf path leaves performance on the table.

Serving engines get tuned for the exact model, traffic shape, memory pressure, and latency target, covering scheduler behavior, KV cache handling, and runtime settings. Auto-tuning here removes the manual sweep most teams would otherwise run by hand. It also keeps the engine aligned with how your workload actually behaves. Less babysitting, more throughput.

Wafer compares speculative decoding, FP8 and FP4 quantization, batching strategies, and expert sharding for MoE models. These choices interact heavily, so testing them together beats tuning them one at a time. For anyone serving large models, decode strategy is often where the biggest wins hide. Skip it and you leave money on the table.

Every candidate has to preserve correctness and meet your reliability targets before it can ship. That guardrail matters when optimization and quantization can quietly change outputs. You get the speedup without silently trading away the results your users depend on. That's the whole point.

The platform stays in the loop after deployment. When traffic shifts, models update, or new hardware arrives, Wafer measures again and adapts. Inference performance drifts, so a one-time tune tends to go stale. Continual re-tuning is what keeps an endpoint competitive over months rather than days.