Qwen3 32B Instruct
32BAlibaba Qwen3
Qwen3 dense 32B — successor to Qwen2.5-32B with stronger reasoning and thinking mode.
41K
Max Context
4
Quant Variants
GGUF Q4_K_M
Best Quality
97.5%
Accuracy Retained
Quantization Variants
Per-quant VRAM, quality loss, and inference speed on RTX 4090
Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports
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Running Qwen3 32B Instruct locally
At Q4_K_M and 4K of context, Qwen3 32B Instruct needs about 21.9 GB — 18.9 GB of weights, 1.00 GB of KV cache and a 2.0 GB activation buffer. The smallest cards in this index that clear that comfortably are 32 GB ones — 4 of them, from the RTX 5090 up — and 26 of the 78 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.
What longer context costs
Going from 4K to 32K adds about 7.00 GB, taking the total to 29.6 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 40K; holding all of it at Q4_K_M would need about 32 GB.
Which build to download
This index tracks 3 formats for it — GGUF, EXL2, AWQ — across 4 levels. Q4_K_M carries the lowest published perplexity loss at 2.5%. The fastest level measured here is EXL2 3.5bpw at 62 tok/s on an RTX 4090, batch 1. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.
Common questions
- How much VRAM does Qwen3 32B Instruct need?
- About 21.9 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 29.6 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
- Will Qwen3 32B Instruct run on a 32GB GPU?
- Yes — at Q4_K_M and 4K context it needs about 21.9 GB, which leaves 10.1 GB spare on a RTX 5090, or on any of the other 32 GB cards here (4 in all). That is the smallest size in this index that clears it comfortably; 26 of 78 do.
- Which quantization of Qwen3 32B Instruct should I use?
- Q4_K_M has the lowest published quality loss (2.5%), and Q4_K_M is the level most people run. All 4 levels in the index are Q3_K_M, Q4_K_M, EXL2 3.5bpw, AWQ INT4.
Where this model fits
Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.
- Runs comfortably on
- RTX 509032GB · +10.1Arc Pro B70 32G32GB · +10.1Radeon AI PRO R9700 32G32GB · +10.1Instinct MI100 32G32GB · +10.1Mac M3 Pro 36G36GB · +5.1
- Tight but possible
- RTX 309024GBRadeon RX 7900 XTX24GBRTX 409024GB
- Compared here
- GGUF vs AWQGGUF vs EXL2AWQ vs EXL2
- Try it yourself
- Size it in the calculatorGenerate the run command
How to actually run this
Deployment guides for this model and this class of hardware.
This page's figures change when the model or the runtime does.Last updated 2026-10-20 RSS → /feed.xml