llm models
for Edge & OSS teams
Phi-3 Medium vs Llama 3 (70B): which is better for Edge & OSS teams?
TL;DR for Edge & OSS teams
Phi-3 Medium excels for lightweight, cost-efficient deployments, while Llama 3 70B delivers maximum quality if you can host a large model.
Key Differences
| Feature | Phi-3 Medium | Llama 3 (70B) |
|---|---|---|
| Model size (14B vs 70B) | Phi-3 Medium approach | Llama 3 (70B) approach |
| Context window (128k vs 8k) | Phi-3 Medium approach | Llama 3 (70B) approach |
| Compute requirements | Phi-3 Medium approach | Llama 3 (70B) approach |
| License & distribution | Phi-3 Medium approach | Llama 3 (70B) approach |
| Reasoning quality | Phi-3 Medium approach | Llama 3 (70B) approach |
| Hosting flexibility | Phi-3 Medium approach | Llama 3 (70B) approach |
Pricing Snapshot
Phi-3 Medium: open weights; Azure managed inference is billed per million tokens and remains well below GPT-4 pricing (2025-10-13); Llama 3 70B: open weights; expect ~$3-$5/hr for a 2x L40S-class setup (2025-10-13)
Last reviewed: 2025-10-13
Phi-3 Medium
Choose Phi-3 Medium if:
- You need low-cost edge deployment
- You run on limited GPU memory
- You want 128k context for documents
Pros
- + Open weights with commercial license
- + 128k context window
- + Optimized for edge GPUs and ONNX
- + Strong code and reasoning for a 14B model
- + Low per-token cost on Azure AI
Cons
- - No multimodal support
- - Smaller community than Llama
- - Requires quantization for consumer GPUs
- - Lower peak reasoning vs GPT-4 class
Llama 3 (70B)
Choose Llama 3 (70B) if:
- You chase highest accuracy
- You can host 70B weights
- You want the largest OSS community
Pros
- + Open weights & permissive license
- + Strong reasoning & coding
- + Fine-tuning & quantization support
- + Active open-source community
- + Good English performance
Cons
- - Shorter context (~8k)
- - No multimodal capability
- - Requires significant compute to host
- - Limited non-English performance
Also Consider
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