Table of Contents
Quick Verdict
Mixture of Experts versus dense transformer is one of the most interesting architectural debates in self-hosted AI, and this chatbot benchmark crystallises the tradeoff. Mixtral 8x7B generates tokens at 45 tok/s while using just 26 GB of VRAM. Qwen 72B generates at 34 tok/s but scores 8.4 on multi-turn evaluation versus Mixtral’s 7.7 — a gap that users notice in conversations requiring nuance and context tracking.
On a dedicated GPU server, Mixtral is the speed champion. Qwen 72B is the quality champion. Neither is universally better — your chatbot’s use case determines the winner.
Full benchmark data below. More matchups at the GPU comparisons hub.
Specs Comparison
These models occupy radically different design philosophies. Mixtral routes tokens through only 2 of 8 expert modules per layer, activating 12.9B parameters out of 46.7B total. Qwen 72B fires all 72B parameters on every token. The result: Mixtral is faster per token, Qwen is more capable per token.
| Specification | Mixtral 8x7B | Qwen 72B |
|---|---|---|
| Parameters | 46.7B (12.9B active) | 72B |
| Architecture | Mixture of Experts | Dense Transformer |
| Context Length | 32K | 128K |
| VRAM (FP16) | 93 GB | 145 GB |
| VRAM (INT4) | 26 GB | 42 GB |
| Licence | Apache 2.0 | Qwen |
VRAM planning: Mixtral 8x7B VRAM requirements and Qwen 72B VRAM requirements.
Chatbot Performance Benchmark
Tested on an NVIDIA RTX 3090 with vLLM, INT4 quantisation, and continuous batching. Conversations spanned customer support, product Q&A, and general knowledge with 3-8 turns per session. Live data at our tokens-per-second benchmark.
| Model (INT4) | TTFT (ms) | Generation tok/s | Multi-turn Score | VRAM Used |
|---|---|---|---|---|
| Mixtral 8x7B | 65 | 45 | 7.7 | 26 GB |
| Qwen 72B | 47 | 34 | 8.4 | 42 GB |
The 0.7-point multi-turn score gap is significant in practice. It manifests as better coherence in long conversations, more accurate follow-up responses, and fewer factual contradictions across turns. For support chatbots handling complex queries over multiple exchanges, that quality difference reduces escalation rates. See our best GPU for LLM inference guide.
See also: Mixtral 8x7B 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
Mixtral’s 16 GB VRAM advantage at INT4 means it fits comfortably on a single 24 GB card, while Qwen 72B’s 42 GB footprint demands multi-GPU or larger hardware. This is the biggest cost differentiator.
| Cost Factor | Mixtral 8x7B | Qwen 72B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 26 GB | 42 GB |
| Est. Monthly Server Cost | £138 | £153 |
| Throughput Advantage | 7% faster | 7% cheaper/tok |
Calculate with our cost-per-million-tokens calculator.
Recommendation
Choose Mixtral 8x7B if you need a fast, memory-efficient chatbot and your conversations are relatively straightforward — FAQ bots, simple product recommendations, or first-line support triage where speed matters more than nuance.
Choose Qwen 72B if your chatbot handles complex, multi-turn conversations where coherence and reasoning quality directly impact business outcomes. Its 128K context window also makes it the only option if your chat sessions reference extensive prior context.
Deploy on dedicated GPU hosting for predictable performance.
Deploy the Winner
Run Mixtral 8x7B or Qwen 72B on bare-metal GPU servers with full root access, no shared resources, and no token limits.
Browse GPU Servers
