--- title: Qwen3.5 9B FP8 — Efficient Reasoning LLM | ModelsLab description: Run Qwen3.5 9B FP8 model via API for fast reasoning, coding, and vision tasks on low VRAM. Generate precise outputs now. url: https://modelslab.com/qwen35-9b-fp8 canonical: https://modelslab.com/qwen35-9b-fp8 type: website component: Seo/ModelPage generated_at: 2026-05-05T20:32:09.373833Z --- Available now on ModelsLab · Language Model Qwen3.5 9B Fp8 Reasoning Powers 9B Efficiency --- [Try Qwen3.5 9B Fp8](/models/together_ai/Qwen-Qwen3.5-9B-FP8) [API Documentation](https://docs.modelslab.com) 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](/models/together_ai/Qwen-Qwen3.5-9B-FP8). 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 ](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 Qwen3.5 9B Fp8 --- [Read the docs ](https://docs.modelslab.com) ### What is Qwen3.5 9B Fp8? 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. ### How does qwen3 5 9b fp8 improve efficiency? 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. ### What are Qwen3.5 9B FP8 model benchmarks? 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. ### Is Qwen3.5 9B FP8 API multimodal? Supports vision and video understanding natively. Handles reasoning, coding, and images efficiently. Use reasoning tokens for step-by-step thinking. ### Qwen3.5 9B FP8 alternative to larger models? 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. ### qwen3 5 9b fp8 api integration steps? 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 Fp8](/models/together_ai/Qwen-Qwen3.5-9B-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 - 2026-05-06*