Llama-3.1-70B-Instruct vs Phi-3.5-MoE-instruct
Llama-3.1-70B-Instruct (70.6B parameters) and Phi-3.5-MoE-instruct (41.9B parameters) side by side: the memory each needs at every precision, what it costs to run on a live GPU, and the context window, KV cache and license where they are published. Numbers are computed from the models' published specs; this page does not rank quality.
Side by side
| Fact | Llama-3.1-70B-Instruct | Phi-3.5-MoE-instruct |
|---|---|---|
| Parameters | 70.6B | 41.9B |
| Architecture | – | mixture of 16 experts, 2 active per token |
| Context length | – | 131,072 tokens |
| License | – | – |
| Published precision | BF16 | BF16 |
| VRAM needed, As published | 158 GB | 93.6 GB |
| VRAM needed, FP8 | 78.8 GB | 46.8 GB |
| VRAM needed, INT4 | 39.4 GB | 23.4 GB |
| Cheapest live fit, As published | RTX A5000 × 7 · $1.23/hr | RTX PRO 6000 · $1.38/hr |
| Cheapest live fit, FP8 | RTX PRO 6000 · $1.38/hr | L40 · $0.759/hr |
| Cheapest live fit, INT4 | RTX A6000 · $0.363/hr | RTX A5000 · $0.176/hr |
| KV cache per token (16-bit) | Not published for this architecture | Not published for this architecture |
| KV cache at 32k tokens | Not published for this architecture | Not published for this architecture |
| KV cache at 128k tokens | Not published for this architecture | Not published for this architecture |
VRAM is the weight size at each precision times a flat 1.2 overhead; see the methodology. The FP8 and INT4 rows need a quantized checkpoint or an engine that quantizes on load. The fit is the lowest-priced single GPU type that holds the model at that precision, or the lowest-priced multi-GPU set (up to 8) when none does. KV cache is for one sequence at 16-bit, computed from each model's config where the attention layout is known.
Which to pick
- Phi-3.5-MoE-instruct needs less VRAM at its published precision (93.6 GB against 158 GB), so it fits on a smaller GPU.
- Llama-3.1-70B-Instruct has the cheaper live GPU fit at its published precision ($1.23/hr against $1.38/hr).
These follow only from the facts in the table above. Whether either model does your task well is a separate question this page does not answer.
Keep reading
- Llama-3.1-70B-Instruct: full VRAM table and live GPU fit
- Phi-3.5-MoE-instruct: full VRAM table and live GPU fit
- The Llama model series
- The Phi model series
- All models that fit in 192 GB
- All models that fit in 96 GB
Other comparisons
- Llama-3.1-70B-Instruct vs Qwen-72B
- Llama-3.1-70B-Instruct vs Qwen3-Coder-Next
- Llama-3.1-70B-Instruct vs Qwen2.5-72B-Instruct
- Llama-3.1-70B-Instruct vs Qwen2.5-72B
- Phi-3.5-MoE-instruct vs Qwen-72B
- Phi-3.5-MoE-instruct vs Llama-3.3-70B-Instruct
- Phi-3.5-MoE-instruct vs Qwen3-Coder-Next
- Phi-3.5-MoE-instruct vs Qwen2.5-72B-Instruct
