---
title: Qwen 3.5 Optimized with FlashHead
description: Discover how Qwen 3.5, optimized with FlashHead, achieves faster inference speeds without sacrificing quality, making it ideal for edge computing.
---

[Blog | Embedl ](https://www.embedl.com/knowledge)

# [Qwen 3.5 Optimized with FlashHead](https://www.embedl.com/knowledge/qwen-3.5-optimized-with-flashhead)

 Written by [Embedl](https://www.embedl.com/knowledge/author/embedl) | May 20, 2026 8:10:10 AM

Qwen 3.5 is a new generation of large language models designed for high-quality reasoning and multimodal tasks.

FlashHead is Embedl’s **drop-in replacement for the output (lm) head** in language models. It reduces the cost of the final layer without retraining, improving inference speed while preserving model quality.

This post shows how the combination performs in practice, and why it works.

## **Results**

We applied FlashHead across the Qwen 3.5 family (0.8B → 27B) and validated accuracy on lm-eval benchmarks as well as on-device performance across multiple devices.

*Screenshots from [https://github.com/embedl/Edge-Inference-Benchmarks](https://github.com/embedl/Edge-Inference-Benchmarks)

### **What actually changes**

- **Up to 1.4× faster inference**
- **Largest gains on smaller and mid-size models (0.8B–9B)**
- **Diminishing but still meaningful gains at larger sizes (≥27B)**
- **No measurable regression in quality across evals**

### **Where the speedup comes from**

On edge hardware (Jetson Nano Super, Jetson AGX Orin, Jetson AGX Thor), the LM head is often a disproportionate bottleneck:

- Large vocabulary projection
- Memory-bound operations
- Poor hardware utilization

FlashHead targets exactly this.

### **Why this matters for Qwen 3.5**

Qwen 3.5 already pushes efficiency hard:

- Small models punching above their size (e.g. 9B competing with much larger models on reasoning benchmarks)
- Strong multimodal capability in a relatively compact footprint

That means:

- You’re *more likely* to be bottlenecked on inference overheads (like the LM head)
- Optimizing that layer gives immediate, visible gains

### **Benchmarks and raw results**

📊[https://github.com/embedl/Edge-Inference-Benchmarks](https://github.com/embedl/Edge-Inference-Benchmarks)

Prebuilt models  
 🤗[https://huggingface.co/collections/embedl/qwen35](https://huggingface.co/collections/embedl/qwen35?utm_source=chatgpt.com)

## **Summary**

- FlashHead is a **drop-in latency optimization** for LLMs
- Qwen 3.5 + FlashHead delivers **faster inference with unchanged quality**

If you're running models on edge or latency-constrained setups, this is one of the simplest wins available.

## **Try it out**

FlashHead models for Qwen 3.5 are available now:

🤗[https://huggingface.co/collections/embedl/qwen35](https://huggingface.co/collections/embedl/qwen35)

Install FlashHead:

pip install flash-head

Code and integration:  
[https://github.com/embedl/flash-head](https://github.com/embedl/flash-head)

[View full post](https://www.embedl.com/knowledge/qwen-3.5-optimized-with-flashhead)

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