Qwen3 Next 80B A3b Instruct Fp8
80B Power 3B Speed
Activate Sparse Efficiency
Hybrid Attention
Gated DeltaNet Boost
Combines Gated DeltaNet and Attention for 262K context handling in Qwen3 Next 80B A3B Instruct FP8.
MoE Sparsity
3B Active Params
Activates 3B of 80B params per token in Qwen3 Next 80B A3B Instruct FP8 model for 10x throughput.
FP8 Precision
Memory Optimized
FP8 quantization cuts memory 50% versus FP16 in Qwen3 Next 80B A3B Instruct FP8 API.
Examples
See what Qwen3 Next 80B A3b Instruct Fp8 can create
Copy any prompt below and try it yourself in the playground.
Code Review
“Review this Python function for efficiency and suggest optimizations: def fibonacci(n): if n <= 1: return n else: return fibonacci(n-1) + fibonacci(n-2)”
Tech Summary
“Summarize key advancements in hybrid attention mechanisms for LLMs like Qwen3 Next 80B A3B Instruct FP8.”
Data Analysis
“Analyze this dataset on renewable energy trends from 2020-2025 and predict 2030 growth based on patterns.”
Doc Translation
“Translate this technical spec sheet on GPU architectures from English to Spanish, preserving all terms.”
For Developers
A few lines of code.
Inference. Three lines.
ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.
- Serverless: scales to zero, scales to millions
- Pay per token, no minimums
- Python and JavaScript SDKs, plus REST API
import requestsresponse = requests.post("https://modelslab.com/api/v7/llm/chat/completions",json={"key": "YOUR_API_KEY","prompt": "","model_id": ""})print(response.json())
Ready to create?
Start generating with Qwen3 Next 80B A3b Instruct Fp8 on ModelsLab.