Llama 3.3 70B Instruct vs Qwen 2.5 72B Instruct
Side-by-side VRAM requirements, benchmark scores, and GPU compatibility for local AI inference.
Quick verdict
Llama 3.3 70B Instruct is more hardware-efficient — it needs 42.2 GB at Q4_K_M vs 43.3 GB for Qwen 2.5 72B Instruct, fitting on 38 GPUs natively.
VRAM at each quantization (8k context)
| Quant | Llama 3.3 70B Instruct | Qwen 2.5 72B Instruct | Diff |
|---|---|---|---|
| FP16 | 159.8 GB | 164.3 GB | -3% |
| Q8 | 81.4 GB | 83.6 GB | -3% |
| Q6_K | 61.8 GB | 63.5 GB | -3% |
| Q5_K_M | 52.0 GB | 53.4 GB | -3% |
| Q4_K_M | 42.2 GB | 43.3 GB | -3% |
| Q3_K_M | 34.4 GB | 35.3 GB | -3% |
| Q2_K | 26.5 GB | 27.2 GB | -2% |
Diff is Llama 3.3 70B Instruct relative to Qwen 2.5 72B Instruct. Green = lower VRAM (fits more GPUs).
Model specifications
| Spec | Llama 3.3 70B Instruct | Qwen 2.5 72B Instruct |
|---|---|---|
| Org | Meta | Alibaba |
| Parameters | 70B | 72B |
| Architecture | Dense | Dense |
| Context | 125k tokens | 125k tokens |
| Modalities | text | text |
| License | Llama 3.3 Community | Qwen |
| Commercial | Yes | Yes |
| Released | 2024-12-06 | 2024-09-19 |
| GPUs (native) | 38 / 67 | 38 / 67 |
Benchmark scores
| Benchmark | Llama 3.3 70B Instruct | Qwen 2.5 72B Instruct |
|---|---|---|
| MMLU-Pro | 68.9 | 58.1 |
| GPQA | 50.5 | 49.0 |
| IFEval | 92.1 | 86.4 |
| MATH | 77.0 | 83.1 |
| HumanEval | 88.4 | 86.6 |
| Arena ELO | 1256.0 | 1259.0 |
Green = higher score (better). — = not yet available.
GPUs that run only Llama 3.3 70B Instruct(0)
Every GPU that runs Llama 3.3 70B Instruct also runs Qwen 2.5 72B Instruct.
GPUs that run only Qwen 2.5 72B Instruct(0)
Every GPU that runs Qwen 2.5 72B Instruct also runs Llama 3.3 70B Instruct.
GPUs that run both natively(38)
- NVIDIA RTX 509032 GB
- NVIDIA H100 80GB80 GB
- NVIDIA A100 80GB80 GB
- NVIDIA A100 40GB40 GB
- NVIDIA L40S48 GB
- NVIDIA RTX A600048 GB
- NVIDIA RTX 6000 Ada48 GB
- NVIDIA DGX Spark (128GB)128 GB
- AMD Instinct MI300X192 GB
- AMD Strix Halo (128GB)128 GB
- AMD Strix Halo (96GB)96 GB
- AMD Strix Halo (64GB)64 GB
- +26 more GPUs run both
Which should you use?
Choose Llama 3.3 70B Instruct if:
- • You have limited VRAM — it's a smaller model needing 42.2 GB vs 43.3 GB
- • Benchmark quality matters — scores 68.9 vs 58.1 on MMLU-Pro
Choose Qwen 2.5 72B Instruct if:
- • You want maximum capability and have a 44 GB+ GPU
Frequently asked questions
- Which is better, Llama 3.3 70B Instruct or Qwen 2.5 72B Instruct?
- Llama 3.3 70B Instruct has 70B parameters vs 72B for Qwen 2.5 72B Instruct, so Qwen 2.5 72B Instruct is the larger model. Llama 3.3 70B Instruct is more hardware-efficient, needing 42.2 GB at Q4_K_M vs 43.3 GB. On MMLU-Pro, Llama 3.3 70B Instruct scores higher (68.9 vs 58.1).
- How much VRAM does Llama 3.3 70B Instruct need vs Qwen 2.5 72B Instruct?
- At Q4_K_M quantization with 8k context, Llama 3.3 70B Instruct needs approximately 42.2 GB of VRAM, while Qwen 2.5 72B Instruct needs 43.3 GB. At FP16, Llama 3.3 70B Instruct requires 159.8 GB vs 164.3 GB for Qwen 2.5 72B Instruct.
- Can you run Llama 3.3 70B Instruct on the same GPUs as Qwen 2.5 72B Instruct?
- Yes, 38 GPUs can run both natively in VRAM, including NVIDIA RTX 5090, NVIDIA H100 80GB, NVIDIA A100 80GB. However, no GPU can run Llama 3.3 70B Instruct without also fitting Qwen 2.5 72B Instruct, and no GPU can run Qwen 2.5 72B Instruct without also fitting Llama 3.3 70B Instruct.
- What is the difference between Llama 3.3 70B Instruct and Qwen 2.5 72B Instruct?
- Llama 3.3 70B Instruct has 70B parameters (dense) with a 125k context window. Qwen 2.5 72B Instruct has 72B parameters (dense) with a 125k context window. Licensing differs: Llama 3.3 70B Instruct is Llama 3.3 Community while Qwen 2.5 72B Instruct is Qwen.
- Which model fits in 24 GB of VRAM, Llama 3.3 70B Instruct or Qwen 2.5 72B Instruct?
- Neither fits in 24 GB at Q4_K_M — Llama 3.3 70B Instruct needs 42.2 GB and Qwen 2.5 72B Instruct needs 43.3 GB. Both require at least a 48 GB GPU.