Cerebras

Cerebras provides extremely fast inference (2,000+ tokens/second) powered by their Wafer-Scale Engine hardware. This page covers all Cerebras models available in Tambo, including Llama, Qwen, and other open-weight model families.

Cerebras model overview

Cerebras hosts a variety of open-source models optimized for their specialized hardware. Available model families include:

• Llama Family: Meta's Llama 3.1 and 3.3 models with up to 128K context
• Qwen Family: Alibaba's Qwen 3 models for multilingual and structured outputs
• Other Models: GPT-OSS and GLM models for various use cases

All Cerebras models deliver ultra-fast inference at 2,000+ tokens/sec. Context windows are limited to 8,192 tokens on the free tier.

Supported Models

Llama Family

llama3.1-8b

Status: Untested API Name: llama3.1-8b Context Window: 128,000 tokens (8,192 on free tier) Provider Docs: Cerebras Inference Docs

Meta's Llama 3.1 8B model running on Cerebras hardware. Ideal for fast inference at 2,000+ tokens/sec, best suited for simple tasks and cost-effective deployments.

Best for:

• Cost-sensitive applications
• Real-time chat and responses
• Simple text generation tasks
• High-volume, low-complexity workloads

Notes:

• Not yet validated on common Tambo tasks
• Smallest and fastest Llama model on Cerebras
• Good starting point for testing

llama-3.3-70b

Status: Untested API Name: llama-3.3-70b Context Window: 128,000 tokens (8,192 on free tier) Provider Docs: Cerebras Inference Docs

Meta's Llama 3.3 70B model on Cerebras, offering balanced performance with ultra-fast inference. Suitable for complex reasoning and multi-step tasks.

Best for:

• Complex reasoning tasks
• Multi-step problem solving
• Production workloads requiring higher accuracy
• Tasks requiring stronger language understanding

Notes:

• Not yet validated on common Tambo tasks
• Stronger capabilities than the 8B variant
• Maintains fast inference speeds

Qwen Family

qwen-3-32b

Status: Untested API Name: qwen-3-32b Context Window: 32,768 tokens (8,192 on free tier) Provider Docs: Cerebras Inference Docs

Alibaba's Qwen 3 32B model with hybrid reasoning capabilities on Cerebras. Good for multilingual tasks and structured outputs.

Best for:

• Multilingual applications
• Structured output generation
• Hybrid reasoning tasks
• JSON and code generation

Notes:

• Not yet validated on common Tambo tasks
• Excels at multilingual tasks and structured outputs
• Good balance of capability and speed

qwen-3-235b-a22b-instruct-2507

Status: Untested API Name: qwen-3-235b-a22b-instruct-2507 Context Window: 32,768 tokens (8,192 on free tier) Provider Docs: Cerebras Inference Docs

Alibaba's large-scale Qwen 3 model (235B params, A22B architecture) optimized for instruction following on Cerebras.

Best for:

• Complex instruction following
• Tasks requiring maximum model capability
• Advanced reasoning and analysis
• Professional content generation

Notes:

• Not yet validated on common Tambo tasks
• One of the largest models available on Cerebras
• Best for demanding tasks requiring maximum capability

Other Models

gpt-oss-120b

Status: Untested API Name: gpt-oss-120b Context Window: 8,192 tokens Provider Docs: Cerebras Inference Docs

OpenAI open-weight 120B parameter model on Cerebras. Powerful capabilities for demanding applications with Cerebras's fast inference.

Best for:

• General-purpose high-performance tasks
• Applications requiring strong reasoning
• Complex content generation
• Tasks benefiting from larger model scale

Notes:

• Not yet validated on common Tambo tasks
• Based on OpenAI's open-weight release
• Strong general-purpose capabilities

zai-glm-4.6

Status: Untested API Name: zai-glm-4.6 Context Window: 128,000 tokens (8,192 on free tier) Provider Docs: Cerebras Inference Docs

Zhipu AI's GLM 4.6 model on Cerebras with fast inference capabilities.

Best for:

• General text generation
• Conversational AI
• Content creation
• Bilingual (Chinese/English) tasks

Notes:

• Not yet validated on common Tambo tasks
• Strong bilingual capabilities
• Good for Chinese/English applications

zai-glm-4.7

Status: Untested API Name: zai-glm-4.7 Context Window: 128,000 tokens (8,192 on free tier) Provider Docs: Cerebras Inference Docs

Zhipu AI's GLM 4.7 model on Cerebras, the latest iteration with improved capabilities.

Best for:

• General text generation
• Conversational AI
• Content creation
• Bilingual (Chinese/English) tasks

Notes:

• Not yet validated on common Tambo tasks
• Latest GLM iteration with improvements over 4.6
• Strong bilingual capabilities

Configuration

All Cerebras models are configured through your project settings in the Tambo dashboard:

1. Navigate to your project in the dashboard
2. Go to Settings → LLM Providers
3. Select Cerebras as your provider
4. Enter your Cerebras API key (get one from Cerebras Cloud)
5. Choose your desired model from the dropdown
6. Configure any additional parameters (temperature, maxOutputTokens, etc.)
7. Click Save to apply the configuration

Cerebras models support the standard LLM parameters available in Tambo. For detailed parameter configuration, see Custom LLM Parameters.

Model Selection Guide

For speed and cost efficiency:

• Fastest: Llama 3.1 8B
• Balanced: Llama 3.3 70B or Qwen 3 32B

For complex tasks:

• Maximum capability: Qwen 3 235B or GPT-OSS 120B
• Multilingual: Qwen 3 32B or Qwen 3 235B

For bilingual (Chinese/English):

• Latest: GLM 4.7
• Stable: GLM 4.6

See Also

• Labels - Understanding model status labels and observed behaviors
• Custom LLM Parameters - Configuring model parameters for fine-tuned responses