Wafer raises $40M in Series A fundingLearn more
2T+ tokens of continual inference

Inference thatkeeps getting better

Wafer learns how your workload behaves and continuously optimizes the serving stack for better performance, reliability, and efficiency.

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[Wafer] has the lowest latency we’ve seen from any provider we’ve tried. And it doesn’t go off a cliff when you increase the requests per minute.

Harry BleyanCo-founder & CTO, Neon Health
  • Vercel
  • Ollama
  • Command Code
  • Orchestra
  • Neon Health
  • Convoso
  • DigitalOcean
  • AWS
  • Vapi
  • Sarvam AI
  • Tavus
  • Inworld
  • Brilliant
  • OnDeck AI
  • Magnitude
  • Sponsor 7
  • Evergrove Labs
  • Sponsor 11
  • Sponsor 12
  • Linzumi
dedicated

The inference your product actually needs

Bring us the model, traffic shape, and SLO. Wafer builds the endpoint around them—and keeps optimizing after it goes live.

Tailored to your traffic

Your real request mix — not a generic benchmark — drives batching, caching, routing, and decode decisions

The whole stack searched

Model, engine, kernels, and hardware are optimized together against the outcome you care about

Reliable by design

Every candidate must preserve correctness and meet your reliability targets before it can ship

Never finished optimizing

When traffic shifts, models update, or new hardware lands, Wafer measures again and adapts

Wafer for Startups

$500 in free credits. Then 1:1 matching up to $10,000.

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Wafer Technology

Why across-the-stack
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

  1. The agent profiles the stack to see whether latency or throughput is coming from scheduling, decoding, kernels, memory pressure, or hardware fit.

  2. The agent generates candidate configurations across batching, decoding, quantization, engines, kernels, and hardware, and measures each one

  3. Deploy the fastest configuration on the target stack and continue profiling production traffic to identify bottlenecks as load, models, and hardware evolve

Why across-the-stack
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

  1. 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.

  2. Try Many Paths

    The agent generates candidate configurations across batching, decoding, quantization, engines, kernels, and hardware, and measures each one

  3. 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

Model, engine, kernels, and hardware are optimized together against the outcome you care about

  • NVIDIA B200/B300
  • AMD MI350X/MI355X
  • AWS Trainium
  • Google TPUs

Custom kernels per traffic shape

Write fused ops, attention paths, GEMM variants, and decode kernels tuned to the model shape and hardware target

  • CUDA
  • HIP
  • Triton
  • NKI

Engine configs per workload

Auto-tune serving engines for the specific model, traffic shape, memory pressure, and latency target

  • Scheduler
  • KV cache
  • Runtime

Decode strategy search

Compare speculative decoding, FP8 and FP4 quantization formats, batching strategies, and expert sharding for MoE models

  • Speculative Decode
  • FP8/FP4
  • Batching
  • Expert Sharding
Your workload, optimized

Find the fastest configuration
for your workload

Bring your model and real traffic. Wafer profiles the full serving stack and deploys only verified improvements.

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