Skip to content
Wafer raises $40M in Series A fundingLearn more
50T+ tokens of hyper-optimized inference

Inference hyper-optimizedfor your workload

Wafer serves open models on a stack tuned to your real traffic, layer by layer: model, decoding, engine, kernels and hardware.

[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
  • You.com
  • Cekura
  • Greptile
  • The Prompting Company
  • 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 your model, traffic shape, and latency target. Wafer tunes the full stack to your workload and keeps optimizing after it goes live.

Tailored to your traffic

Your real request mix drives batching, caching, routing, and decoding decisions

The whole stack searched

Model, decoding, engine, kernels, and hardware are tuned 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

Four ways to run on Wafer.

Serverless

Pay per token for open models through an OpenAI-compatible API.

Start building

Reserved

Reserve throughput for predictable traffic, backed by an uptime SLA.

Talk to sales

Dedicated

An isolated endpoint tuned to your model and traffic, with performance targets in your SLA.

Bring us your workload

Batch

Coming soon

Asynchronous inference for large offline jobs.

Wafer Batch Coming soon

Bring your batch workload.

Tell us your model and volume. Continue in your email app to review and send your early-access request.

Your details are covered by our privacy policy.

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, decoding, engine, kernels, and hardware are tuned 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.

Bring us your workload