Qwen 2.5 72B Instruct

Positioning

The Qwen flagship in the dense-72B class. If you have a 4090 / 5090 / RTX 6000 Ada or are on Apple Silicon with 64 GB+ unified memory, this competes with Llama 3.3 70B as the best general open-weight model available.

Strengths

• Multilingual ceiling is the highest in the open 70B-class — Chinese, Korean, Japanese, German all near-frontier quality.
• Long-context behavior holds up well out to 64K in practice.
• Math and code are strong — better than Llama 3.1 70B base; close to Llama 3.3 70B.

Limitations

• Same VRAM constraints as Llama 3.3 70B — Q4 partial-offload on 24 GB.
• License caps at 100M MAU — review for scale deployments.
• Refusal behavior on geopolitical content can be limiting depending on use case.

Real-world performance on RTX 4090

• Q4_K_M (40 GB) — partial offload: 21–27 tok/s decode, TTFT ~400 ms
• Q5_K_M (47 GB) — heavier offload: 9–13 tok/s
• Q8_0 (72 GB) — workstation only

Should you run this locally?

Yes, for users who want the best multilingual local model and have the same hardware that runs Llama 3.3 70B. No, for English-only workloads where Llama 3.3 70B's instruction-following polish is preferable.

How it compares

• vs Llama 3.3 70B → coin flip on English; Qwen wins decisively on non-English. Pick by language mix.
• vs Llama 3.1 70B → Qwen 2.5 72B wins outright; Llama 3.1 70B is the previous-generation comparison.
• vs Qwen 2.5 32B → 72B is meaningfully smarter on hard tasks; 32B is faster and full-GPU. Pick by speed-vs-quality preference.
• vs DeepSeek R1 Distill Llama 70B → R1 Distill is dramatically better at reasoning; Qwen 2.5 72B wins at general chat and writing.

Run this yourself

ollama pull qwen2.5:72b-instruct-q4_K_M
ollama run qwen2.5:72b-instruct-q4_K_M

Settings: Q4_K_M GGUF, 8192 ctx, --n-gpu-layers 60 of 81, RTX 4090