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Qwen3.5 9B Fp8Reasoning Powers 9B Efficiency

Try Qwen3.5 9B Fp8API Documentation

Deploy Qwen3.5 9B FP8 Now

FP8 Compression

Cuts VRAM Usage

FP8 (F8_E4M3) reduces memory footprint while preserving output quality for Qwen3.5 9B FP8.

Top Benchmarks

Beats 120B Models

Qwen3.5 9B FP8 scores 81.7 on GPQA Diamond, outperforming larger models in reasoning.

Multimodal Native

Handles Vision Tasks

Processes images and video with strong reasoning and coding via Qwen3.5 9B FP8 API.

Examples

See what Qwen3.5 9B Fp8 can create

Copy any prompt below and try it yourself in the playground.

Code Refactor

“Refactor this Python function to optimize for speed and readability: def calculate_fib(n): if n <= 1: return n; return calculate_fib(n-1) + calculate_fib(n-2). Use memoization and handle large n up to 1000.”

Math Proof

“Prove that the sum of the first n odd numbers equals n squared. Provide step-by-step reasoning with examples for n=1 to 5, then generalize.”

Data Analysis

“Analyze this dataset: sales = [120, 150, 130, 170, 140]. Compute mean, median, standard deviation, and forecast next month's sales using linear regression.”

Logic Puzzle

“Three boxes: one gold, one silver, one mixed. Gold says truth, silver lies, mixed random. 'Gold' box says 'Silver has prize'. Which has prize? Explain chain of logic.”

For Developers

A few lines of code.
Inference. FP8 Speed.

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 Qwen3.5 9B Fp8

Read the docs

Qwen3.5 9B Fp8 is an FP8-compressed 9B parameter LLM with BF16 hybrid precision. It cuts VRAM use and boosts throughput. Maintains strong reasoning from base Qwen3.5 9B model.

FP8 (F8_E4M3) compression reduces memory while keeping quality. Runs on consumer hardware with 6GB in 4-bit mode. Ideal for Qwen3.5 9B FP8 API deployments.

Scores 81.7 on GPQA Diamond, beating GPT-OSS-120B. Hits 69.2% on MMMU-Pro for multimodal reasoning. Tops sub-10B intelligence index at 32.

Supports vision and video understanding natively. Handles reasoning, coding, and images efficiently. Use reasoning tokens for step-by-step thinking.

Matches or exceeds 120B models in key tasks at 9B scale. FP8 version optimizes for edge deployment over BF16. Open-source under Apache 2.0.

Call endpoint with messages array and reasoning param. Preserve full context for continuations. FP8 ensures low-latency responses on standard GPUs.

Ready to create?

Start generating with Qwen3.5 9B Fp8 on ModelsLab.

Try Qwen3.5 9B Fp8API Documentation