What does TOPS mean?
### 🔤 What does **TOPS** mean?
**TOPS** = **Tera Operations Per Second**
- 1 TOPS = **1 trillion (10¹²) operations per second**
It measures **how many basic computational operations** (like multiply, add, or multiply-accumulate) a chip can perform in one second.
> 📌 Think of it like "horsepower" for AI chips — a higher TOPS number means **higher raw AI processing power**.
---
### 💡 Why is TOPS Important?
AI models (especially deep learning) involve **massive amounts of math**, mostly:
- **Multiply-Accumulate (MAC)** operations
- Matrix multiplications
- Convolution layers
These are counted as "operations", and **TOPS tells you how fast a chip can handle them**.
✅ So, a chip with **30 TOPS** can do **30 trillion operations per second** — useful for running AI models quickly and efficiently.
---
### 🧮 Example: How Many Operations in a Neural Network?
Let’s say you’re running a simple image classification model (like MobileNet):
- One 3×3 convolution on a small image might require **~90,000 operations**
- A full forward pass could need **~500 million operations**
- At 30 TOPS:
→ 500 million ops ÷ 30 trillion ops/sec = **~0.017 seconds** (~17 ms)
→ That’s **fast enough for real-time inference**
---
### ⚠️ Important: TOPS is **Raw Performance**, Not Real-World Speed
While TOPS is useful, **it doesn’t tell the whole story**. Here’s why:
| Limitation | Explanation |
|----------|-------------|
| 🔹 **Operation Type Matters** | Is it INT8? FP16? Sparse? A chip may claim "100 TOPS" but only at low precision (e.g., INT8), not for more accurate FP16. |
| 🔹 **Memory Bandwidth** | If the chip can’t feed data fast enough, it sits idle — high TOPS but low actual performance. |
| 🔹 **Software Optimization** | Poor drivers or frameworks can waste hardware potential. |
| 🔹 **Sparsity & Compression** | Some chips use tricks (like skipping zero values) to boost "effective TOPS", but real gains vary. |
| 🔹 **Power & Thermal Limits** | A chip might deliver 50 TOPS only briefly before throttling due to heat. |
> 📉 Example:
> - Chip A: 50 TOPS (INT8), but poor memory → real performance = 30 effective TOPS
> - Chip B: 40 TOPS (INT8), great memory & software → real performance = 38 effective TOPS
> → **Chip B performs better**, even with lower TOPS!
---
### 🆚 Common TOPS by Device (2024)
| Device / Chip | NPU / AI Chip | TOPS (INT8 typical) | Use Case |
|---------------|----------------|------------------------|----------|
| **Apple A17 Pro (iPhone 15 Pro)** | Neural Engine | ~35 TOPS | On-device AI, photo enhancement |
| **Qualcomm Snapdragon 8 Gen 3** | Hexagon NPU | ~70 TOPS | Android phones, generative AI |
| **Huawei Ascend 310** | Da Vinci NPU | 16 TOPS | Edge AI, cameras, robotics |
| **Huawei Ascend 910C** | AI Accelerator | 256 TOPS | Data center training |
| **NVIDIA H100 (Tensor Core)** | GPU + AI cores | ~1,979 TOPS (with sparsity) | LLM training, supercomputing |
| **Google TPU v5p** | Custom AI chip | ~459 TOPS | Large-scale AI in Google Cloud |
| **Intel Lunar Lake NPU** | Integrated NPU | ~10 TOPS | AI PCs (Windows Studio Effects) |
| **AMD Ryzen AI (XDNA 2)** | Integrated NPU | ~16 TOPS | AI laptops |
| **MediaTek Dimensity 9300** | APU 790 | ~50 TOPS | High-end Android phones |
---
### 📝 Types of TOPS (Be Careful!)
Manufacturers sometimes report TOPS under **best-case conditions**:
| Type | Meaning |
|------|--------|
| **Peak TOPS** | Maximum theoretical performance (ideal conditions) |
| **Sustained TOPS** | Realistic long-term performance (more honest) |
| **INT8 TOPS** | Lower precision, higher number (common for edge chips) |
| **FP16/BF16 TOPS** | Higher precision, usually half the INT8 TOPS |
| **Sparse TOPS** | Uses sparsity (skipping zeros) — can double reported TOPS, but real gain depends on model |
> 🔎 Always check: **What precision? What workload? Sustained or peak?**
---
### ✅ Summary: What You Need to Know About TOPS
| ✅ Do | ❌ Don’t |
|------|---------|
| Use TOPS to compare **raw AI capability** between chips | Assume higher TOPS = better real-world performance |
| Compare TOPS at the **same precision** (e.g., INT8 vs INT8) | Ignore memory, power, and software |
| Look for **sustained** or **real-world benchmarks** | Trust marketing claims without context |
---
### 💬 Final Thought
> **TOPS is like "spec sheet horsepower" — helpful, but not the full picture.**
> The best AI chip isn’t always the one with the highest TOPS — it’s the one that **delivers performance efficiently, consistently, and with great software support**.
**TOPS** = **Tera Operations Per Second**
- 1 TOPS = **1 trillion (10¹²) operations per second**
It measures **how many basic computational operations** (like multiply, add, or multiply-accumulate) a chip can perform in one second.
> 📌 Think of it like "horsepower" for AI chips — a higher TOPS number means **higher raw AI processing power**.
---
### 💡 Why is TOPS Important?
AI models (especially deep learning) involve **massive amounts of math**, mostly:
- **Multiply-Accumulate (MAC)** operations
- Matrix multiplications
- Convolution layers
These are counted as "operations", and **TOPS tells you how fast a chip can handle them**.
✅ So, a chip with **30 TOPS** can do **30 trillion operations per second** — useful for running AI models quickly and efficiently.
---
### 🧮 Example: How Many Operations in a Neural Network?
Let’s say you’re running a simple image classification model (like MobileNet):
- One 3×3 convolution on a small image might require **~90,000 operations**
- A full forward pass could need **~500 million operations**
- At 30 TOPS:
→ 500 million ops ÷ 30 trillion ops/sec = **~0.017 seconds** (~17 ms)
→ That’s **fast enough for real-time inference**
---
### ⚠️ Important: TOPS is **Raw Performance**, Not Real-World Speed
While TOPS is useful, **it doesn’t tell the whole story**. Here’s why:
| Limitation | Explanation |
|----------|-------------|
| 🔹 **Operation Type Matters** | Is it INT8? FP16? Sparse? A chip may claim "100 TOPS" but only at low precision (e.g., INT8), not for more accurate FP16. |
| 🔹 **Memory Bandwidth** | If the chip can’t feed data fast enough, it sits idle — high TOPS but low actual performance. |
| 🔹 **Software Optimization** | Poor drivers or frameworks can waste hardware potential. |
| 🔹 **Sparsity & Compression** | Some chips use tricks (like skipping zero values) to boost "effective TOPS", but real gains vary. |
| 🔹 **Power & Thermal Limits** | A chip might deliver 50 TOPS only briefly before throttling due to heat. |
> 📉 Example:
> - Chip A: 50 TOPS (INT8), but poor memory → real performance = 30 effective TOPS
> - Chip B: 40 TOPS (INT8), great memory & software → real performance = 38 effective TOPS
> → **Chip B performs better**, even with lower TOPS!
---
### 🆚 Common TOPS by Device (2024)
| Device / Chip | NPU / AI Chip | TOPS (INT8 typical) | Use Case |
|---------------|----------------|------------------------|----------|
| **Apple A17 Pro (iPhone 15 Pro)** | Neural Engine | ~35 TOPS | On-device AI, photo enhancement |
| **Qualcomm Snapdragon 8 Gen 3** | Hexagon NPU | ~70 TOPS | Android phones, generative AI |
| **Huawei Ascend 310** | Da Vinci NPU | 16 TOPS | Edge AI, cameras, robotics |
| **Huawei Ascend 910C** | AI Accelerator | 256 TOPS | Data center training |
| **NVIDIA H100 (Tensor Core)** | GPU + AI cores | ~1,979 TOPS (with sparsity) | LLM training, supercomputing |
| **Google TPU v5p** | Custom AI chip | ~459 TOPS | Large-scale AI in Google Cloud |
| **Intel Lunar Lake NPU** | Integrated NPU | ~10 TOPS | AI PCs (Windows Studio Effects) |
| **AMD Ryzen AI (XDNA 2)** | Integrated NPU | ~16 TOPS | AI laptops |
| **MediaTek Dimensity 9300** | APU 790 | ~50 TOPS | High-end Android phones |
---
### 📝 Types of TOPS (Be Careful!)
Manufacturers sometimes report TOPS under **best-case conditions**:
| Type | Meaning |
|------|--------|
| **Peak TOPS** | Maximum theoretical performance (ideal conditions) |
| **Sustained TOPS** | Realistic long-term performance (more honest) |
| **INT8 TOPS** | Lower precision, higher number (common for edge chips) |
| **FP16/BF16 TOPS** | Higher precision, usually half the INT8 TOPS |
| **Sparse TOPS** | Uses sparsity (skipping zeros) — can double reported TOPS, but real gain depends on model |
> 🔎 Always check: **What precision? What workload? Sustained or peak?**
---
### ✅ Summary: What You Need to Know About TOPS
| ✅ Do | ❌ Don’t |
|------|---------|
| Use TOPS to compare **raw AI capability** between chips | Assume higher TOPS = better real-world performance |
| Compare TOPS at the **same precision** (e.g., INT8 vs INT8) | Ignore memory, power, and software |
| Look for **sustained** or **real-world benchmarks** | Trust marketing claims without context |
---
### 💬 Final Thought
> **TOPS is like "spec sheet horsepower" — helpful, but not the full picture.**
> The best AI chip isn’t always the one with the highest TOPS — it’s the one that **delivers performance efficiently, consistently, and with great software support**.
