Find the Bottlenecks
The agent profiles the stack to see whether latency or throughput is coming from scheduling, decoding, kernels, memory pressure, or hardware fit.
Wafer Technology
Agents that optimize inference
across the stack
Wafer employs agents to identify and enhance inference bottlenecks in orchestration, algorithms, serving engines, GPU kernels, and diverse hardware.
Why across-the-stack
optimization matters
optimization matters
The fastest setup is rarely a single switch. Kernel choices, serving-engine behavior, batching, quantization, hardware, and traffic shape all push on each other
Try Many Paths
Experience lightning-fast, real-time responses tailored for voice agents, intelligent copilots, and interactive AI products
Ship the Measured Winner
Deploy the fastest configuration on the target stack and continue profiling production traffic to identify bottlenecks as load, models, and hardware evolve
An agent reasoning across interacting layers
Real performance is not won at a single layer
Heterogeneous by design
Re-derive optimal configurations per silicon instead of porting assumptions from one accelerator family to another.
NVIDIA B200/B300AMD MI350X/MI355XAWS Trainium
Custom kernels per shape
Write fused ops, attention paths, GEMM variants, and decode kernels turned to the model shape and hardware target
CUDAHIPTritonNKI
Engine configs per workload
Auto-tune serving engines for the specific model, traffic shape, memory pressure, and latency target
vLLMSGLangschedulerKV cache
Decode strategy search
Compare speculative decoding, FP8 and FP4 quantization formats, batching strategies, and expert sharding for MoE models
speculative decodeFP8/FP4batchingexpert sharding
How agents
turn inference work into speed
Wafer transforms profiling data into candidate changes, evaluates them on the target stack, and applies only those that meet correctness and performance standards.
The measured winner — every layer above is chosen
by measurement on the target stack, not heuristics