Happy Horse 1.0 is now on ModelsLab

Try Now
Skip to main content
Available now on ModelsLab · Language Model

Qwen3 Next 80B A3b Instruct Fp880B Power 3B Speed

Try Qwen3 Next 80B A3b Instruct Fp8API Documentation

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
API Documentation 
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

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.

Delivers 10x throughput over Qwen3-32B for contexts beyond 32K. Matches Qwen3-235B on benchmarks. Optimized for instruct tasks without thinking mode.

High-sparsity MoE with 512 experts activates minimal params. Multi-token prediction speeds inference. Stability tweaks ensure robust training.

Serves as Qwen3 Next 80B A3B Instruct FP8 alternative for long-context tasks. Outperforms denser models at lower cost. FP8 fits commodity GPUs.

Supports up to 262K tokens via hybrid Gated Attention and DeltaNet. Excels in ultra-long tasks. Recommend H100/H200 GPUs for deployment.

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 Fp8API Documentation