Cerebras in AGNT
Wafer-scale inference for open models. Every AGNT workflow is provider-portable, so the same agents run here or anywhere else you point them.
Why teams pick it
Models
- Llama and Qwen at speed
Strengths
- thousands of tokens/sec
- great for iterative agents
- generous free tier
In AGNT
Agents, workflows and tools use Cerebras like any other provider — same nodes, same receipts, swap models per step if you want.
Wafer-scale throughput
Cerebras serves open models at token rates high enough to alter workflow design. When generation is effectively instant, iterative approaches that would be impractical elsewhere — draft, critique, revise, repeat — become cheap enough to use routinely, which can produce better output than a single pass from a stronger model.
What to watch
As with any fast-inference provider, the model menu is the constraint rather than the hardware. Confirm which checkpoints are currently served before designing a step around a specific one, and treat the generous free tier as a way to evaluate rather than as capacity to build on.
Using Cerebras in a workflow
A workflow can use Cerebras for one step and another provider for the next. Teams typically reserve Llama and Qwen at speed for work that rewards thousands of tokens/sec, then hand bulk steps to a faster tier. The model behind an operation is a setting in AGNT, so this is tuned by measurement rather than decided up front.
Connect Cerebras in two minutes
- Download AGNT Community Core — free, local-first, no account needed to run.
- Open Settings → Providers, choose Cerebras, and paste your API key. Connect by pasting an API key into AGNT’s vault — stored encrypted on your machine, never uploaded.
- Give an agent a job — or install one from the marketplace — and watch the first receipt come back.
Cerebras in AGNT — common questions
What does extreme speed change architecturally?
It makes multi-pass patterns practical — generating and critiquing several times can beat one pass from a larger model.
Which models are available?
Open-weight families including Llama and Qwen. Check the current list before pinning one.
Is it suitable for batch work?
Yes — high throughput is exactly what large batch jobs need.