Edge deployment moves AI power from big cloud servers to local devices. This guide compares four top open-source models you can run for free.
| Model | Maker | Parameters | License | Release Date |
|---|---|---|---|---|
| Gemma4 | Google DeepMind | 2B / 4B / 9B / 27B | Gemma Terms (commercial use allowed) | Early 2026 |
| Llama3-70B | Meta | 70 billion | Llama 3.1 Community License | July 2024 |
| Mistral Large 2 | Mistral AI | 123 billion | Mistral Research License / Commercial | July 2024 |
| DeepSeek V3.2 | DeepSeek AI | 236 billion (MoE, 21B active) | DeepSeek Model License | Expected 2025-2026 |
MParameter count matters, but active parameters and inference efficiency decide real edge performance. Mixture of Experts (MoE) models like DeepSeek V3.2 use fewer active parameters per token.
A shopkeeper in rural India runs Gemma4-2B on a $300 laptop. It answers customer questions in Hindi without internet. The model fits in 4GB RAM.
A German factory puts Mistral Large 2 on a local server. It analyzes machine logs without sending data to the cloud.
Smaller models with smart architecture often beat larger ones on limited hardware. Check active parameter count, not just total size.
| Model | Minimum RAM (4-bit) | Recommended RAM (8-bit) | Tokens/Second (RTX 4090) | Mobile Feasibility |
|---|---|---|---|---|
| Gemma4-2B | 2 GB | 4 GB | ~180 | Yes (smartphone) |
| Gemma4-9B | 6 GB | 10 GB | ~95 | High-end phone |
| Llama3-70B | 40 GB | 80 GB | ~25 | No |
| Mistral Large 2 | 70 GB | 128 GB | ~18 | No |
| DeepSeek V3.2 | 45 GB | 90 GB | ~30 | No |
Gemma4 clearly wins for low-resource edge scenarios. The 2B variant runs on phones. The 9B variant suits tablets and cheap laptops.
A nurse in Kenya carries a tablet with Gemma4-9B. It helps diagnose diseases where cell towers fail. The tablet cost $200.
A trucking company uses Llama3-70B on in-cabin computers. Each truck has a $1,500 mini-PC. The AI warns drivers about fatigue.
| Model | MMLU (reasoning) | HumanEval (coding) | MATH (math) | Multilingual | Context Window |
|---|---|---|---|---|---|
| Gemma4-9B | 72.3% | 62.5% | 55.8% | Strong (40+ languages) | 128K tokens |
| Llama3-70B | 78.5% | 81.2% | 68.4% | Good (8 languages) | 128K tokens |
| Mistral Large 2 | 84.2% | 92.1% | 76.3% | Excellent (80+ languages) | 128K tokens |
| DeepSeek V3.2 | 87.1% | 94.5% | 82.7% | Good (Chinese, English focus) | 128K tokens |
DeepSeek V3.2 leads on pure benchmark scores, but Mistral Large 2 offers the best balance for multilingual edge applications. Gemma4-9B punches above its weight for its tiny size.
Medical diagnosis needs different skills than code completion. A 90% coding score means nothing if you need Arabic speech-to-text.
| Model | Download Cost | Hosting Cost/Month (Edge Server) | Commercial Use | Modification Rights | Attribution Required |
|---|---|---|---|---|---|
| Gemma4 | Free | $50-200 (cloud backup) | Yes | Yes | No |
| Llama3-70B | Free | $300-800 | Yes (with conditions) | Yes | Yes |
| Mistral Large 2 | Free | $500-1,200 | Commercial license extra | Limited without payment | Yes |
| DeepSeek V3.2 | Free | $400-900 | Yes | Yes | Yes |
Licensing looks boring, but it protects your business. Meta's Llama license has a usage cap (700 million users). Mistral charges for commercial use at scale. Google Gemma4 and DeepSeek V3.2 impose fewer restrictions.
A startup builds 10,000 smart fridges with built-in AI. They pick Gemma4 because no lawyer review is needed. Shipment starts in weeks, not months.
A video game studio modifes Llama3-70B for NPC dialogue. They hit Meta's user cap and must renegotiate. Delays cost $500,000.
| Scenario | Best Model | Why It Fits | Hardware Example |
|---|---|---|---|
| Mobile app (offline first) | Gemma4-2B/4B | Runs on 2-4GB RAM, fast response | iPhone 15, Pixel 8 |
| Industrial IoT gateway | Gemma4-9B or Llama3-70B | Balanced power and accuracy | NVIDIA Jetson AGX |
| Regional data center | Mistral Large 2 | Multilingual, strong reasoning | 4x A100 server |
| Research/High-complexity tasks | DeepSeek V3.2 | Top benchmarks, MoE efficiency | 8x H100 cluster |
| Budget-constrained edge | Gemma4-4B | Free, smallest footprint, solid output | Raspberry Pi 5 |
Your hardware budget and network access narrow choices fast. No internet means Gemma4 or small Llama variants. Reliable broadband opens bigger models with cloud fallback.
List your RAM limit, power budget, and language needs first. Then find the model that fits. Do not force a 70B model into a phone.
Key Takeaways
| Key Point | What It Means | Action Item |
|---|---|---|
| Gemma4 dominates small-device edge | 2B to 9B variants run on phones and cheap hardware | Start with Gemma4-4B for any mobile or IoT project |
| Llama3-70B needs serious hardware | Requires 40GB+ RAM, best for dedicated edge servers | Budget $1,500+ per node for GPU and memory |
| Mistral Large 2 wins on multilingual | 80+ languages with native-quality handling | Choose for EU, African, or Indian deployments |
| DeepSeek V3.2 leads raw performance | Best benchmarks, butneeds expert setup for MoE | Use only if you have ML engineers and big hardware |
| License terms affect business risk | Meta and Mistral have usage limits or fees | Read licenses before shipping products; prefer Gemma or DeepSeek for freedom |
| All four are free to download | No upfront cost for any model | Test all candidates on your hardware before committing |