Qwen 2.5 32B Instruct

Positioning

Qwen 2.5 32B is the highest-quality model that fits comfortably on a single 24 GB consumer GPU at Q4 without offload. It's the right answer to "I have a 4090, what's my main daily-driver model?" if you don't want to deal with 70B partial-offload speeds.

Strengths

• 19 GB at Q4_K_M — runs full-GPU on 24 GB with 16K context, no offload, no compromise.
• 70–90 tok/s on a 4090 at Q4 — fastest "serious" model class.
• Capability is genuinely close to last year's 70Bs for most chat workloads.

Limitations

• Falls short of Llama 3.3 70B and Qwen 2.5 72B on the hardest reasoning tasks — the gap is real but smaller than parameter counts suggest.
• Same Qwen license cap at 100M MAU.
• Coding is decent but Qwen 2.5 Coder 32B is meaningfully better for that workload.

Real-world performance on RTX 4090

• Q4_K_M (19 GB): 70–88 tok/s decode, TTFT ~140 ms — full GPU, no offload
• Q5_K_M (22.6 GB): 58–72 tok/s — fits on 24 GB at reduced context
• Q8_0 (35 GB): 18–25 tok/s — partial offload only

Should you run this locally?

Yes, for RTX 3090 / 4090 / 5080 owners who want the best single-card experience without 70B's offload tradeoffs. The right default for serious local-AI work. No, for users on 16 GB or less — pick Qwen 2.5 14B. Or users who prioritize raw ceiling — pick Qwen 2.5 72B with offload.

How it compares

• vs Llama 3.3 70B Q4 → Llama 3.3 70B is better in absolute quality but ~3× slower (offload). Qwen 2.5 32B is the productivity pick; Llama 3.3 70B is the quality pick.
• vs Qwen 2.5 72B → 72B is materially smarter on hard tasks but partial-offloads on 24 GB. Same tradeoff as Llama 3.3 70B.
• vs Qwen 2.5 Coder 32B → for coding, always pick Coder. For general chat, 32B Instruct.
• vs QwQ 32B → QwQ is the reasoning specialist; Qwen 2.5 32B is the generalist. Different jobs.

Run this yourself

ollama pull qwen2.5:32b-instruct-q4_K_M
ollama run qwen2.5:32b-instruct-q4_K_M

Settings: Q4_K_M GGUF, 16384 ctx, full GPU offload, RTX 4090