Qwen · Qwen3 · 32.8B parameters
Qwen3 32B VRAM requirements
Qwen3 32B has 64 layers and uses grouped-query attention (8 KV heads). At Q4_K_M the weights come to 18.6 GB, and the best quantisation that fits a 24 GB card is Q4_K_M.
Fits, but tight
21.5 GB of 21.8 GB · 99%This fits with almost nothing to spare. A background application claiming VRAM will push it over. Drop to the next quantisation down, or quantise the KV cache to Q8_0 — that halves the cache for no meaningful quality loss.
Every quantisation of Qwen3 32B on a RTX 4090
Highlighted row is the highest quality that still fits at 8K context.
| Quantisation | bpw | Weights | Total | Verdict | Max ctx | tok/s |
|---|---|---|---|---|---|---|
| FP16 / BF16 | 16.00 | 61.0 GB | 63.9 GB | Won't fit | — | 0.85 |
| INT8 / W8A8 | 8.50 | 32.1 GB | 34.9 GB | Won't fit | — | 2.52 |
| Q8_0 (GGUF) | 8.50 | 32.1 GB | 34.9 GB | Won't fit | — | 2.52 |
| FP8 (E4M3) | 8.00 | 30.5 GB | 33.3 GB | Won't fit | — | 2.83 |
| Q6_K | 6.56 | 25.0 GB | 27.9 GB | Won't fit | — | 4.94 |
| Q5_K_M | 5.67 | 21.6 GB | 24.5 GB | Won't fit | — | 9.34 |
| Q5_K_S | 5.52 | 21.1 GB | 23.9 GB | Won't fit | — | 10.4 |
| Q4_K_M | 4.85 | 18.6 GB | 21.5 GB | Fits, but tight | 9K | 30.5 |
| AWQ 4-bit | 4.25 | 18.3 GB | 21.2 GB | Fits, but tight | 10K | 30.9 |
| GPTQ 4-bit | 4.25 | 18.3 GB | 21.2 GB | Fits, but tight | 10K | 30.9 |
| MXFP4 | 4.25 | 18.3 GB | 21.2 GB | Fits, but tight | 10K | 30.9 |
| Q4_K_S | 4.58 | 17.6 GB | 20.5 GB | Fits, but tight | 13K | 32.0 |
| Q4_0 | 4.55 | 17.5 GB | 20.4 GB | Fits, but tight | 14K | 32.2 |
| IQ4_XS | 4.25 | 16.4 GB | 19.3 GB | Runs comfortably | 18K | 34.2 |
| Q3_K_M | 3.91 | 15.2 GB | 18.0 GB | Runs comfortably | 23K | 36.8 |
| IQ3_M | 3.70 | 14.4 GB | 17.3 GB | Runs comfortably | 26K | 38.6 |
| IQ3_XXS | 3.06 | 12.1 GB | 15.0 GB | Runs comfortably | 35K | 45.3 |
| Q2_K | 2.63 | 10.6 GB | 13.4 GB | Runs comfortably | 41K | 51.3 |
| IQ2_XXS | 2.06 | 8.5 GB | 11.3 GB | Runs comfortably | 50K | 62.2 |
| IQ1_M | 1.75 | 7.4 GB | 10.2 GB | Runs comfortably | 54K | 70.4 |
Qwen3 32B on each GPU
Q4_K_M weights at 8K context, single card, monitor attached.
| GPU | VRAM | GB/s | Verdict | Max ctx | tok/s |
|---|---|---|---|---|---|
| H100 SXM 80GB | 80 | 3350 | Runs comfortably | 128K | 114 |
| A100 80GB | 80 | 2039 | Runs comfortably | 128K | 63.5 |
| RTX 5090 | 32 | 1792 | Runs comfortably | 39K | 59.0 |
| RTX 4090 | 24 | 1008 | Fits, but tight | 9K | 30.5 |
| RTX 3090 | 24 | 936 | Fits, but tight | 9K | 29.5 |
| Radeon RX 7900 XTX | 24 | 960 | Fits, but tight | 9K | 27.6 |
| L40S | 48 | 864 | Runs comfortably | 99K | 26.2 |
| RTX A6000 | 48 | 768 | Runs comfortably | 99K | 24.3 |
| Mac Studio M3 Ultra 256GB | 256 | 819 | Runs comfortably | 128K | 23.9 |
| Mac Studio M4 Max 128GB | 128 | 546 | Runs comfortably | 128K | 17.8 |
| NVIDIA DGX Spark (GB10) | 128 | 273 | Runs comfortably | 128K | 9.17 |
| Mac Mini M4 Pro 48GB | 48 | 273 | Runs comfortably | 67K | 8.93 |
| Ryzen AI Max+ 395 128GB | 128 | 256 | Runs comfortably | 128K | 6.84 |
| RTX 5080 | 16 | 960 | Won't fit | — | 4.98 |
| RTX 5070 Ti | 16 | 896 | Won't fit | — | 4.95 |
| RTX 5060 Ti 16GB | 16 | 448 | Won't fit | — | 4.50 |
| RTX 4080 Super | 16 | 736 | Won't fit | — | 4.39 |
| RTX 4070 Ti Super | 16 | 672 | Won't fit | — | 4.34 |
| RTX 4060 Ti 16GB | 16 | 288 | Won't fit | — | 3.71 |
| RTX 5070 | 12 | 672 | Won't fit | — | 3.40 |
| RTX 3060 12GB | 12 | 360 | Won't fit | — | 3.06 |
| RTX 4070 Super | 12 | 504 | Won't fit | — | 3.02 |
| RTX 4070 | 12 | 504 | Won't fit | — | 3.02 |
| RTX 3080 10GB | 10 | 760 | Won't fit | — | 2.79 |
| Arc B580 | 12 | 456 | Won't fit | — | 2.54 |
Architecture
| Parameters | 32.8B |
| Layers | 64 |
| Hidden size | 5120 |
| Attention heads / KV heads | 64 / 8 |
| Head dimension | 128 |
| Vocabulary | 151,936 |
| Trained context | 128K |
| KV cache per 1K tokens | 0 GB |
| Hugging Face | Qwen/Qwen3-32B |
The Qwen family
Alibaba's series, and the broadest size ladder available — Qwen3 runs from 0.6B to 32B dense, plus 30B-A3B and 235B-A22B as mixture-of-experts. The 152k vocabulary makes the embedding table a large share of a small model's file. Qwen2.5-Coder is the same architecture trained for code.
Direct answers
See the verdictQwen3 32B on RTX 4090
See the verdictQwen3 32B on RTX 3090
See the verdictQwen3 32B on RTX 5080
See the verdictQwen3 32B on RTX 5070 Ti
See the verdictQwen3 32B on RTX 5070
See the verdictQwen3 32B on RTX 4070 Ti Super
See the verdictQwen3 32B on RTX 4070 Super
See the verdictQwen3 32B on RTX 4070
See the verdict