# Qwen 2.5-32B MoE on RTX 3090: Performance Report > Source: > Published: 2026-07-25 09:45:52+00:00 # Qwen 2.5-32B MoE on RTX 3090: Performance Report Running the Qwen 2.5-32B MoE (which only activates about 3B parameters per token) on a single RTX 3090 is a surprisingly efficient way to get high-reasoning capabilities without hitting the VRAM wall. Since it's a Mixture-of-Experts model, you get the intelligence of a larger model but the inference speed of a much smaller one. If you are looking for a real-world AI workflow that doesn't require an A100 cluster, this MoE architecture is the sweet spot. You get the "brain" of a 30B+ model but the latency of a tiny model. It's a massive win for local deployment on consumer hardware. Here is the performance breakdown based on my local tests: **VRAM Usage:** Around 18-22GB depending on the quantization level (4-bit/8-bit) and context window size.**Tokens per second:** Consistently hitting high speeds due to the low active parameter count, making it feel as snappy as a 7B model.**Reasoning Quality:** Significantly outperforms standard 7B or 14B dense models in coding and logic tasks.**Stability:** Stable on 24GB cards, leaving just enough room for a decent KV cache. If you are looking for a real-world AI workflow that doesn't require an A100 cluster, this MoE architecture is the sweet spot. You get the "brain" of a 30B+ model but the latency of a tiny model. It's a massive win for local deployment on consumer hardware. [Next Sectional vs Loveseat: Which Seating Wins? →](/en/threads/3121/) ## All Replies （3） R C Still sounds like a nightmare to set up. Not worth the tinkering for marginal gains. 0 J Had a similar run with a different MoE; the VRAM savings are actually legit. 0