--- title: Qwen3 Next 80B A3B Instruct FP8 — Efficient LLM | Model... description: Deploy Qwen3 Next 80B A3B Instruct FP8 for 262K context and 3B active params. Generate complex responses via API now. url: https://modelslab-frontend-v2-927501783998.us-east4.run.app/qwen3-next-80b-a3b-instruct-fp8 canonical: https://modelslab-frontend-v2-927501783998.us-east4.run.app/qwen3-next-80b-a3b-instruct-fp8 type: website component: Seo/ModelPage generated_at: 2026-05-13T10:52:54.751145Z --- Available now on ModelsLab · Language Model Qwen3 Next 80B A3b Instruct Fp8 80B Power 3B Speed --- [Try Qwen3 Next 80B A3b Instruct Fp8](/models/together_ai/Qwen-Qwen3-Next-80B-A3B-Instruct-FP8) [API Documentation](https://docs.modelslab.com) 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](/models/together_ai/Qwen-Qwen3-Next-80B-A3B-Instruct-FP8). 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 [API Documentation ](https://docs.modelslab.com) PythonJavaScriptcURL Copy ```

import requests

response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

``` FAQ Common questions about Qwen3 Next 80B A3b Instruct Fp8 --- [Read the docs ](https://docs.modelslab.com) ### What is Qwen3 Next 80B A3B Instruct FP8? Qwen3 Next 80B A3B Instruct FP8 is an 80B param MoE LLM activating 3B per token. It uses hybrid attention for 262K context. FP8 reduces memory needs. ### How does Qwen3 Next 80B A3B Instruct FP8 API perform? Delivers 10x throughput over Qwen3-32B for contexts beyond 32K. Matches Qwen3-235B on benchmarks. Optimized for instruct tasks without thinking mode. ### What makes Qwen3 Next 80B A3B Instruct FP8 model efficient? High-sparsity MoE with 512 experts activates minimal params. Multi-token prediction speeds inference. Stability tweaks ensure robust training. ### Is Qwen3 Next 80B A3B Instruct FP8 a good alternative? Serves as Qwen3 Next 80B A3B Instruct FP8 alternative for long-context tasks. Outperforms denser models at lower cost. FP8 fits commodity GPUs. ### What context length supports qwen3 next 80b a3b instruct fp8? Supports up to 262K tokens via hybrid Gated Attention and DeltaNet. Excels in ultra-long tasks. Recommend H100/H200 GPUs for deployment. ### How to use qwen3 next 80b a3b instruct fp8 API? Access via LLM endpoints like sglang or vLLM. Instruct mode only, no think tags. Integrates for high-throughput generation. Ready to create? --- Start generating with Qwen3 Next 80B A3b Instruct Fp8 on ModelsLab. [Try Qwen3 Next 80B A3b Instruct Fp8](/models/together_ai/Qwen-Qwen3-Next-80B-A3B-Instruct-FP8) [API Documentation](https://docs.modelslab.com) --- *This markdown version is optimized for AI agents and LLMs.* **Links:** - [Website](https://modelslab.com) - [API Documentation](https://docs.modelslab.com) - [Blog](https://modelslab.com/blog) --- *Generated by ModelsLab - 2026-05-13*