Table of Contents
Quick Verdict
Two 70B-class dense transformers walk into a benchmark. One emerged from Meta’s training pipeline, the other from Alibaba’s. On paper they look nearly identical — 70B versus 72B parameters, both dense architectures, similar VRAM demands. But in chatbot workloads on a dedicated GPU server, the differences matter.
Qwen 72B scores 8.4 on multi-turn evaluation compared to LLaMA 3 70B’s 8.2, reflecting slightly better conversational coherence across extended dialogues. LLaMA 3 70B counters with a 14 ms faster time-to-first-token (51 ms versus 65 ms), making the initial response feel more immediate. Both are excellent chatbot backbones — the question is whether your users notice quality nuance or speed more.
Complete data below. Browse additional comparisons at the GPU comparisons hub.
Specs Comparison
The standout spec difference is context length. Qwen 72B supports 128K tokens natively — sixteen times LLaMA 3 70B’s 8K window. For chatbots that need to remember hours of conversation or process uploaded documents mid-chat, this is a decisive advantage.
| Specification | LLaMA 3 70B | Qwen 72B |
|---|---|---|
| Parameters | 70B | 72B |
| Architecture | Dense Transformer | Dense Transformer |
| Context Length | 8K | 128K |
| VRAM (FP16) | 140 GB | 145 GB |
| VRAM (INT4) | 40 GB | 42 GB |
| Licence | Meta Community | Qwen |
Deployment guides: LLaMA 3 70B VRAM requirements and Qwen 72B VRAM requirements.
Chatbot Performance Benchmark
Tested on an NVIDIA RTX 3090 with vLLM, INT4 quantisation, and continuous batching. Conversations simulated customer support, product recommendation, and general Q&A with 3-10 turns per session. Live data at our tokens-per-second benchmark.
| Model (INT4) | TTFT (ms) | Generation tok/s | Multi-turn Score | VRAM Used |
|---|---|---|---|---|
| LLaMA 3 70B | 51 | 31 | 8.2 | 40 GB |
| Qwen 72B | 65 | 33 | 8.4 | 42 GB |
Qwen 72B’s generation speed is marginally faster at 33 tok/s versus 31 tok/s, but its higher TTFT means the user waits slightly longer before text starts streaming. In practice, most users perceive streamed output as responsive regardless of the 14 ms TTFT gap. Refer to our best GPU for LLM inference guide for hardware analysis.
See also: LLaMA 3 70B vs Qwen 72B for Code Generation for a related comparison.
See also: LLaMA 3 70B vs Mixtral 8x7B for Chatbot / Conversational AI for a related comparison.
Cost Analysis
With VRAM within 2 GB of each other, these models land on essentially identical hardware. The cost differentiation comes from throughput and monthly server pricing.
| Cost Factor | LLaMA 3 70B | Qwen 72B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 40 GB | 42 GB |
| Est. Monthly Server Cost | £163 | £177 |
| Throughput Advantage | 13% faster | 8% cheaper/tok |
Run your traffic projections through the cost-per-million-tokens calculator to compare total ownership cost.
Recommendation
Choose Qwen 72B if your chatbot requires a massive context window for document-grounded conversations, or if your user base includes significant non-English speakers — Qwen’s multilingual training data gives it an edge in Chinese, Japanese, and Korean conversations.
Choose LLaMA 3 70B if your chatbot is English-focused and you want the snappiest initial response. Its lower TTFT and strong ecosystem support (fine-tuning tools, LoRA adapters, community checkpoints) reduce time to production.
Both models run well at INT4 on dedicated GPU hosting with vLLM, delivering production-grade conversational quality.
Deploy the Winner
Run LLaMA 3 70B or Qwen 72B on bare-metal GPU servers with full root access, no shared resources, and no token limits.
Browse GPU Servers
