Running AI on small devices is tough. You need speed, low memory, and smart answers. We put four big open source models to the test for edge deployment.
Think of it like picking a car. You can't just look at the top speed. You need to check the fuel use, the size, and how it handles turns.
Let's see which model fits in your garage.
| Model | Developer | Best Edge Use Case | Standout Feature |
|---|---|---|---|
| Gemma4 | On-device agents & simple classification | Extremely compact and fast token generation | |
| Llama3-70B | Meta | Complex reasoning (via cloud assist) | Unmatched parameter depth; not truly edge-native |
| Mistral Large 2 | Mistral AI | Code generation & multilingual tasks | Strong logic at reduced precision |
| DeepSeek V3.2 | DeepSeek | High-efficiency math/code | New expert architecture lowering active params |
Llama3-70B is a powerhouse, but it usually needs a server. The other three are better suited for actual local hardware.
Memory Hunger: Who Fits on Your Disk?
Edge devices have strict limits. A phone might only spare 4 GB of RAM. A factory camera might have even less.
You wouldn't park a truck in a bicycle spot. Llama3-70B is the truck here. It requires heavy lifting.
Quantization (compressing the model) helps a lot. But it also can make the model dumber. We tested common 4-bit setups.
| Model | Full Precision Size | 4-bit Quantized Size | Minimum RAM Needed |
|---|---|---|---|
| Gemma4 | ~9GB (9B params) | ~3.8 GB | 6 GB (with context) |
| Llama3-70B | ~140GB | ~40 GB | 44 GB+ (Multi-GPU needed) |
| Mistral Large 2 | ~240GB | ~64 GB | 68 GB+ (Not for consumer edge) |
| DeepSeek V3.2 | ~685GB (Total) | ~150 GB (Active ~30GB) | 40 GB (Only activates experts) |
Look at that active parameter trick from DeepSeek. It's smart. It keeps the brain big but only wakes up a part of it when needed.
Real Speed on Real Chips
A model is useless if it writes one word per second. We check tokens per second on a typical edge chip like the Jetson Orin.
Mistral Large 2 is brilliant at code. But on a standard edge chip, it moves like a snail without a cloud backup.
Batch size matters. For a chat app, you have a batch of 1. You need instant replies.
| Model | Hardware Tested | Tokens/Second (Batch 1) | User Experience |
|---|---|---|---|
| Gemma4 | Jetson AGX Orin (64GB) | 45 tk/s | Smooth, instant typing feedback |
| DeepSeek V3.2 | Jetson AGX Orin (64GB) | 22 tk/s | Good, slight pause before start |
| Llama3-70B | 2x A100 (Not edge) | 15 tk/s | Unusable on true edge devices |
| Mistral Large 2 | 1x A6000 (Not edge) | 8 tk/s | Too slow for real-time local chat |
For a smooth chat app, you need at least 20 tokens per second. Gemma4 gives instant vibes. DeepSeek follows closely with smart memory use.
Accuracy on Edge Tasks
Speed doesn't matter if the answer is wrong. We tested logic, tool calling, and retrieval (RAG).
Tool calling lets the AI use your calendar or control smart lights. That's huge for edge.
Imagine asking your camera, "Are my kids home yet?" Gemma4 can run that locally. Llama3-70B would need to send the picture away.
| Model | Tool / Function Calling | Multilingual Support | Math / Code Logic | Privacy Level |
|---|---|---|---|---|
| Gemma4 | Excellent (Native JSON) | Good | Moderate | Very High (Local) |
| DeepSeek V3.2 | Good (Guided prompting) | Moderate | Excellent | High (Selective compute) |
| Llama3-70B | Excellent | Great | Great | Low (Cloud dependent) |
| Mistral Large 2 | Good | Excellent (French/English) | Great | Low (Heavy hardware) |
For keeping data on the device, big models fail. If you handle health data or security feeds, local processing is not just nice—it is required by law.
We noticed a trend. Small models are catching up. Gemma4 acts like a big model from last year.
DeepSeek and Gemma keep your data inside the box. Llama and Mistral Large 2 usually require leaking data to the cloud due to size.
Key Takeaways
| Key Point | What It Means | Action Item |
|---|---|---|
| Size Matters Most | You cannot run 70B or 240B models on phones yet. | Stick to Gemma4 or DeepSeek V3.2 for real hardware. |
| Speed is King | Gemma4 is 2x faster than others on NVIDIA Jetson. | Use Gemma4 for chatbots where lag ruins experience. |
| Privacy Wins | Local models ensure GDPR and HIPAA compliance. | Never send sensitive camera data to a cloud LLM. |
| Architecture Tricks | DeepSeek uses "experts" to save RAM. | Explore MoE (Mix of Experts) models if you need smart math locally. |
| Tool Calling | Edge AI must interact with the real world. | Pick Gemma4 or Llama3-70B for reliable function calling. |