Wafer

What is Wafer?

Wafer is a platform for AI inference optimization that uses “autonomous agents” to profile, diagnose, and optimize GPU inference across an end-to-end stack—from kernels to models to production pipelines. Its stated purpose is to help users run faster AI inference on different hardware configurations.

The site also describes Wafer as a way to access and run fast open models through a subscription (Wafer Pass), with support for model-focused and agent workflows that aim to improve throughput and cost-efficiency.

Key Features

• Autonomous inference-optimization agents that profile and diagnose performance across the stack, helping target bottlenecks at multiple layers (kernels, model behavior, and pipeline).
• Model- and hardware-oriented optimization workflow that focuses on “any AI model, for any AI hardware,” with the goal of maximizing inference speed for a given setup.
• Kernel-focused optimization capabilities, including “custom agents that optimize kernels” and enable scaling of developer ecosystems around those kernel improvements.
• Throughput-oriented model optimization examples, including a comparison claim of “2.8x faster than base SGLang” for Qwen3.5-397B, positioned as output-throughput and performance-focused tuning.
• Wafer Pass subscription offering limited access to “fastest open-source LLMs” through one subscription for personal and coding agents, including model listings such as Qwen3.5-Turbo-397B and GLM 5.1-Turbo.
• Reported compatibility with several client/workflow tools listed on the site (e.g., Claude Code, OpenClaw, Cline, Roo Code, Kilo Code, OpenHands).

How to Use Wafer

1. Decide whether you want Wafer Pass (subscription access to fast open-source LLMs for personal/coding agents) or Wafer’s broader optimization workflow for your own inference stack.
2. For Wafer Pass, select an available model from the listed options (e.g., Qwen3.5-Turbo-397B, GLM 5.1-Turbo) and use it via the site’s described agent/coding workflows.
3. For stack optimization, run Wafer agents to profile and diagnose your current inference setup, then apply their kernel/model/pipeline optimization approach to improve throughput.
4. If your team ships to different environments, repeat optimization across deployment targets so the system can tune inference performance more consistently.

Use Cases

• AI teams optimizing throughput on existing GPU stacks: Use Wafer agents to profile and diagnose inference bottlenecks across kernels, models, and pipelines to improve output throughput.
• Developers validating performance for specific open models: Use Wafer Pass to try listed open models in agent workflows and compare inference behavior (the site explicitly frames performance as a key outcome).
• Hardware-focused teams (ASICs and GPU platforms): Use Wafer’s custom kernel optimization agents to unlock performance from hardware by improving the software layers that run inference.
• Cloud providers tracking new model releases: Run Wafer’s model optimization approach so teams can move quickly when new models become available and target fast, cost-sensitive inference.
• AI labs deploying models across environments: Apply end-to-end inference optimization “everywhere” so models can run as fast and cheap as possible across different deployment targets.

FAQ

• What does Wafer optimize? Wafer is described as optimizing GPU inference across the stack, including kernels, models, and production pipelines.
• Is Wafer only for a specific model or hardware? The site states the agents are intended to optimize “any AI model” for “any AI hardware,” positioning the workflow as broadly applicable.
• What is Wafer Pass? Wafer Pass is described as limited access to “the fastest open-source LLMs through one subscription” for personal and coding agents.
• Which models are included with Wafer Pass (as listed on the site)? The page lists Qwen3.5-Turbo-397B (with a throughput comparison claim) and GLM 5.1-Turbo, with “more models coming soon.”
• Do I need to integrate with a specific tool? The page lists multiple tools it “works with” (Claude Code, OpenClaw, Cline, Roo Code, Kilo Code, OpenHands), but it does not provide detailed integration instructions.

Alternatives

• General-purpose model serving and inference frameworks: Alternatives are inference-serving stacks that focus on deployment and scaling, but may not provide an agentized profiling/optimization workflow across kernels, models, and pipelines in the way Wafer describes.
• Kernel-level optimization tooling: Some solutions focus specifically on GPU kernels (e.g., custom kernels, kernel scheduling, or low-level performance tuning). These may require more manual work across model and pipeline layers.
• In-house performance benchmarking plus tuning: Teams can build their own benchmarking loops and tune inference settings (batching, precision, runtime parameters). This can be flexible but typically lacks an automated, end-to-end optimization agent approach.
• Specialized inference optimization services: Instead of agent-driven profiling, some providers offer managed performance tuning for inference endpoints, focusing on deployment-level optimization rather than cross-stack kernel/model/pipeline diagnosis.