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SmolLM2 135M vs Qwen 2.5 0.5B
Which runs better on your device? Side-by-side comparison of specs, quantization sizes, and device compatibility.
SPECIFICATIONS
Type
Chat
Chat
Parameters
0.135B
0.5B
Context
8K
32K
Min tier
Low
Low
Runs on
47 / 47 devices
47 / 47 devices
QUANTIZATION SIZES
| Quant | SmolLM2 135M | Qwen 2.5 0.5B | |
|---|---|---|---|
| FP16 | 0.3 GB | 1.2 GB | SmolLM2 135M smaller |
| Q8 | 0.1 GB | 0.7 GB | SmolLM2 135M smaller |
| Q6 | — | 0.6 GB | |
| Q5 | — | 0.5 GB | |
| Q4 | 0.1 GB | 0.4 GB | SmolLM2 135M smaller |
| Q3 | — | 0.3 GB | |
| Q2 | — | 0.3 GB |
DEVICE COMPATIBILITY
| Device | SmolLM2 135M | Qwen 2.5 0.5B |
|---|---|---|
| 💻 MacBook Air M4 macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~100 tok/s |
| 💻 MacBook Air M3 macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~83 tok/s |
| 💻 MacBook Air M2 macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~83 tok/s |
| 💻 MacBook Pro M4 Pro macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~200 tok/s |
| 💻 MacBook Air M1 macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~57 tok/s |
| 💻 MacBook Air M1 macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~57 tok/s |
| 💻 MacBook Pro M1 macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~57 tok/s |
| 💻 MacBook Pro M1 Pro macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~167 tok/s |
| 💻 MacBook Pro M1 Pro macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~167 tok/s |
| 💻 MacBook Pro M1 Max macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~200 tok/s |
| 💻 MacBook Pro M1 Max macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~200 tok/s |
| 💻 MacBook Pro M2 Pro macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~167 tok/s |
| 💻 MacBook Pro M2 Pro macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~167 tok/s |
| 💻 MacBook Pro M2 Max macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~200 tok/s |
| 💻 MacBook Pro M2 Max macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~200 tok/s |
| 💻 MacBook Pro M3 Pro macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~125 tok/s |
| 💻 MacBook Pro M3 Pro macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~125 tok/s |
| 💻 MacBook Pro M3 Max macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~200 tok/s |
| 💻 MacBook Pro M3 Max macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~200 tok/s |
| 📱 iPhone 16 Pro iOS | Runs great FP16 · ~176 tok/s | Runs great FP16 · ~40 tok/s |
| 📱 iPhone 15 iOS | Runs great FP16 · ~109 tok/s | Runs great FP16 · ~25 tok/s |
| 📱 Galaxy S25 Ultra Android | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~45 tok/s |
| 📱 Galaxy S24 Android | Runs great FP16 · ~158 tok/s | Runs great FP16 · ~36 tok/s |
| 📱 Pixel 9 Pro Android | Runs great FP16 · ~176 tok/s | Runs great FP16 · ~40 tok/s |
| 🎮 Steam Deck OLED Linux | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~73 tok/s |
| 🖥️ Gaming PC (RTX 4070) Windows | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~200 tok/s |
| 🖥️ Gaming PC (RTX 3060) Windows | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~200 tok/s |
| 🖥️ Gaming PC (RTX 4080) Windows | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~200 tok/s |
| 🖥️ Gaming PC (RTX 4090) Windows | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~200 tok/s |
| 🤖 Atom 1 Linux | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~171 tok/s |
| 🤖 Atom 1 Linux | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~200 tok/s |
| 🤖 Atom 1 Linux | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~200 tok/s |
| 📱 iPad Pro M4 iOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~70 tok/s |
| 🖥️ Mac Mini M1 macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~57 tok/s |
| 🖥️ Mac Mini M1 macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~57 tok/s |
| 🖥️ Mac Mini M2 macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~83 tok/s |
| 🖥️ Mac Mini M2 Pro macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~167 tok/s |
| 🖥️ Mac Mini M2 Pro macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~167 tok/s |
| 🖥️ Mac Mini M4 macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~100 tok/s |
| 🖥️ Mac Mini M4 macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~100 tok/s |
| 🖥️ Mac Mini M4 Pro macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~200 tok/s |
| 🖥️ Mac Mini M4 Pro macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~200 tok/s |
| 🖥️ Mac Studio M4 Max macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~200 tok/s |
| 🖥️ Mac Pro M2 Ultra macOS | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~200 tok/s |
| 💻 Snapdragon X Elite Laptop Windows | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~113 tok/s |
| 📱 OnePlus 13 Android | Runs great FP16 · ~200 tok/s | Runs great FP16 · ~45 tok/s |
| 🍓 Raspberry Pi 5 Linux | Runs great FP16 · ~119 tok/s | Runs great FP16 · ~27 tok/s |
VERDICT
Both models run on 47 of 47 devices. Qwen 2.5 0.5B has a larger context window (32K vs 8K). Qwen 2.5 0.5B is the larger model and may produce better quality outputs, while SmolLM2 135M is lighter on resources. For memory-constrained devices, SmolLM2 135M is smaller at its lowest quant (0.1 GB vs 0.3 GB).