Compare
Phi-3.5 Mini Instruct vs Llama 3.1 8B Instruct
Same hardware, same arithmetic, side by side. Every memory figure is calculated from published model geometry rather than quoted from a marketing page.
Estimated
Side by side
On GeForce RTX 4090
| Property | Phi-3.5 Mini Instruct | Llama 3.1 8B Instruct |
|---|---|---|
| Organization | Microsoft | Meta |
| Parameters | 3.8B | 8B |
| Architecture | Phi3 · dense | Llama · dense |
| Context | 128k | 128k |
| Layers | 32 | 32 |
| Hidden size | 3,072 | 4,096 |
| KV heads | 32 of 32 | 8 of 32 |
| Licence | MIT | Llama 3.1 Community License |
| Commercial use | Yes | Yes |
| Modalities | text | text |
| Released | 2024-08-20 | 2024-07-23 |
| Recommended quantization | Q8_0 | Q8_0 |
| Memory needed | 7.3 GB | 9.5 GB |
| Estimated tok/s | 193 | 92 |
| Fit | Excellent fit | Excellent fit |
| Fine-tune here | Yes | Yes |
| MMLU (reported) | 69% | 69.4% |
| HUMANEVAL (reported) | — | 72.6% |
Benchmark rows are figures the model's authors published, not ModelLM measurements, and the two models may not have been evaluated under identical conditions.
Trade-offs
Phi-3.5 Mini Instruct
Strong reasoning at 3.8B with a 128k context — but full multi-head attention makes its KV cache expensive.
Strengths
- Very strong for its size
- MIT licensed
- 128k context
Limitations
- No grouped-query attention: KV cache grows fast
- Limited world knowledge
Llama 3.1 8B Instruct
The most widely supported open model there is. If a tool, adapter or tutorial exists, it was written for this one first.
Strengths
- Unmatched ecosystem support
- 128k context
- Very stable fine-tuning behaviour
Limitations
- Benchmarks now behind newer 7–9B models
- Community licence with an acceptable-use policy
Common comparisons