Can you run Qwen 3 32B locally?
Qwen 3 family · April 2025 · 32.80B parameters · Hugging Face model card
Qwen 3 32B runs locally on a 24 GB GPU at Q4_K_M, which needs about 22.7 GB of VRAM with a 4k-token context. Qwen 3 32B is a dense 32.8-billion-parameter model with a native 40,960-token context, so longer contexts scale the KV cache to about 30.2 GB at 32k tokens.
Minimum: 24 GB VRAM GPU (RTX 3090, RTX 4090) at Q4_K_M, 4k context · Recommended: 32 GB+ VRAM (RTX PRO 6000 Blackwell 96GB, or 2× 24GB) at Q6_K–Q8_0 for 32k context
How much VRAM does Qwen 3 32B need at each quantization?
Qwen 3 32B needs 22.7 GB of VRAM at Q4_K_M. The table below lists weights-only size and total VRAM including overhead for each common quantization level with 4k and 32k token contexts.
| Quantization | Bits / weight | Weights only | Total + KV @4k ctx | Total + KV @32k ctx |
|---|---|---|---|---|
| Q4_K_M | 4.8 | 19.7 GB | 22.7 GB | 30.2 GB |
| Q5_K_M | 5.7 | 23.4 GB | 26.8 GB | 34.3 GB |
| Q6_K | 6.6 | 27.1 GB | 30.8 GB | 38.4 GB |
| Q8_0 | 8.5 | 34.9 GB | 39.4 GB | 46.9 GB |
| FP16 | 16 | 65.6 GB | 73.2 GB | 80.8 GB |
Method: weights = parameters × bits-per-weight (Q4_K_M ≈ 4.8, Q5_K_M ≈ 5.7, Q6_K ≈ 6.6, Q8_0 ≈ 8.5, FP16 = 16), plus 10% loading overhead, plus KV cache from the verified architecture config (64 layers, 8 KV heads, 128 head dim). Source: Params 32.8B, 64 layers, 8 KV heads, head dim 128, native 40960 context — HF config.json (Qwen/Qwen3-32B).
Which GPUs can run Qwen 3 32B locally?
At Q4_K_M with a 4k context, Qwen 3 32B needs 22.7 GB of VRAM. The lists below are computed live from our GPU database and grouped by how much headroom the card has. Disclosure: CompareAIHardware.com participates in the Amazon Associates program and earns from qualifying purchases through links on this page. Affiliate relationships do not influence our recommendations.
Runs Qwen 3 32B comfortably (25%+ VRAM headroom)
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| AMD Radeon Pro W7800 | 32 GB | 576 GB/s | Pro GPU | Check price |
| GeForce RTX 5090 | 32 GB | 1792 GB/s | Consumer GPU | Check price |
| NVIDIA RTX 5000 Ada | 32 GB | 576 GB/s | Pro GPU | Check price |
| NVIDIA RTX PRO 4500 Blackwell | 32 GB | 896 GB/s | Pro GPU | Check price |
| NVIDIA A100 40GB SXMEOL | 40 GB | 1555 GB/s | Data Center GPU | |
| AMD Radeon Pro W7900 | 48 GB | 864 GB/s | Pro GPU | Check price |
| NVIDIA RTX PRO 5000 Blackwell | 48 GB | 1344 GB/s | Pro GPU | |
| RTX 6000 Ada | 48 GB | 960 GB/s | Pro GPU | Check price |
| RTX A6000 | 48 GB | 768 GB/s | Pro GPU | Check price |
| H100 SXM | 80 GB | 3350 GB/s | Data Center GPU | |
| NVIDIA A100 80GB SXM | 80 GB | 2039 GB/s | Data Center GPU | |
| NVIDIA H100 PCIe 80GB | 80 GB | 2039 GB/s | Data Center GPU | |
| NVIDIA RTX PRO 6000 Blackwell | 96 GB | 1792 GB/s | Pro GPU | Check price |
| H200 SXM | 141 GB | 4800 GB/s | Data Center GPU | |
| AMD Instinct MI300X | 192 GB | 5300 GB/s | Data Center GPU | |
| NVIDIA B200 | 192 GB | 8000 GB/s | Data Center GPU | |
| AMD Instinct MI325X | 256 GB | 6000 GB/s | Data Center GPU | |
| AMD Instinct MI355X | 288 GB | 8000 GB/s | Data Center GPU | |
| NVIDIA B300 (Blackwell Ultra) | 288 GB | 8000 GB/s | Data Center GPU |
Minimum GPUs that fit Qwen 3 32B at Q4_K_M
These GPUs hold the model but leave little headroom — keep contexts short.
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| GeForce RTX 3090EOL | 24 GB | 936 GB/s | Consumer GPU | Check price |
| GeForce RTX 3090 TiEOL | 24 GB | 1008 GB/s | Consumer GPU | Check price |
| GeForce RTX 4090 | 24 GB | 1008 GB/s | Consumer GPU | Check price |
| NVIDIA RTX PRO 4000 Blackwell | 24 GB | 672 GB/s | Pro GPU | Check price |
| Radeon RX 7900 XTX | 24 GB | 960 GB/s | Consumer GPU | Check price |
Needs 2+ GPUs to run Qwen 3 32B
One of these cards is too small on its own, but a pair (tensor or pipeline parallel, ~90% efficiency) covers the 22.7 GB requirement. See our multi-GPU guide for setup.
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| Arc A770 16GB | 16 GB ×2 | 560 GB/s | Consumer GPU | Check price |
| GeForce RTX 4060 Ti 16GB | 16 GB ×2 | 288 GB/s | Consumer GPU | Check price |
| GeForce RTX 4070 Ti SUPEREOL | 16 GB ×2 | 672 GB/s | Consumer GPU | Check price |
| GeForce RTX 4080EOL | 16 GB ×2 | 716 GB/s | Consumer GPU | Check price |
| GeForce RTX 4080 SUPER | 16 GB ×2 | 736 GB/s | Consumer GPU | Check price |
| GeForce RTX 5060 Ti 16GB | 16 GB ×2 | 448 GB/s | Consumer GPU | Check price |
| GeForce RTX 5070 Ti | 16 GB ×2 | 896 GB/s | Consumer GPU | Check price |
| GeForce RTX 5080 | 16 GB ×2 | 960 GB/s | Consumer GPU | Check price |
| NVIDIA RTX PRO 2000 Blackwell | 16 GB ×2 | 288 GB/s | Pro GPU | Check price |
| Radeon RX 6800 XTEOL | 16 GB ×2 | 512 GB/s | Consumer GPU | Check price |
| Radeon RX 7600 XT | 16 GB ×2 | 288 GB/s | Consumer GPU | Check price |
| Radeon RX 7800 XT | 16 GB ×2 | 624 GB/s | Consumer GPU | Check price |
| Radeon RX 9070 | 16 GB ×2 | 640 GB/s | Consumer GPU | Check price |
| Radeon RX 9070 XT | 16 GB ×2 | 640 GB/s | Consumer GPU | Check price |
| Radeon RX 7900 XT | 20 GB ×2 | 800 GB/s | Consumer GPU | Check price |
Can a Mac run Qwen 3 32B?
Yes — these Apple Silicon machines fit Qwen 3 32B at Q4_K_M, since macOS lets the GPU use about 75% of unified memory. Generation speed is bound by memory bandwidth, so the GB/s column matters as much as capacity.
| Apple Silicon | Unified memory | Usable by GPU (~75%) | Bandwidth |
|---|---|---|---|
| Apple M4 | 32 GB | 24 GB | 120 GB/s |
| Apple M3 Pro | 36 GB | 27 GB | 300 GB/s |
| Apple M4 Pro | 64 GB | 48 GB | 273 GB/s |
| Apple M3 Max | 128 GB | 96 GB | 400 GB/s |
| M4 Max (MacBook Pro) | 128 GB | 96 GB | 546 GB/s |
| M3 Ultra (Mac Studio) | 512 GB | 384 GB | 819 GB/s |
Can Qwen 3 32B run on mini PCs, Jetson, or NPU devices?
These edge and NPU devices from our database have enough memory for Qwen 3 32B at Q4_K_M. Their memory bandwidth is far below discrete GPUs, so expect a fraction of desktop generation speed.
| Device | Memory | Bandwidth | Type |
|---|---|---|---|
| Intel Core Ultra 9 288V (Lunar Lake) | 32 GB | 136 GB/s | NPU Chip |
| Jetson AGX Orin 32GB | 32 GB | 204 GB/s | Edge Compute Device |
| Jetson AGX Orin 64GB | 64 GB | 204 GB/s | Edge Compute Device |
| Snapdragon X Elite (X1E-84-100) | 64 GB | 135 GB/s | NPU Chip |
| AMD Ryzen AI 9 HX 370 (Strix Point) | 96 GB | 120 GB/s | NPU Chip |
Which pre-built systems can run Qwen 3 32B?
These mini PCs and workstations from our database fit Qwen 3 32B at Q4_K_M. Usable-memory figures are conservative: Windows shares about half of system RAM with the GPU by default (Linux can expose more), macOS lets Apple Silicon GPUs use about 75% of unified memory, and Linux unified-memory systems such as GB10 expose roughly 90%. Generation speed is bound by memory bandwidth, so compare the GB/s column before buying.
| System | Type | Memory | Usable for AI | Bandwidth | GPUs |
|---|---|---|---|---|---|
| Mac mini M4 Max | Mini PC | 48 GB | 24 GB | 546 GB/s | — |
| Mac mini M4 Pro | Mini PC | 48 GB | 24 GB | 273 GB/s | — |
| ASRock DeskMeet X600 (Ryzen 7 7600) | Mini PC | 64 GB | 32 GB | 83 GB/s | — |
| BIZON G3000 G2 (1x RTX 5090) | Workstation | 32 GB | 32 GB | 1792 GB/s | 1 |
| Beelink SER9 Pro (Ryzen AI 9 HX 370, 64GB) | Mini PC | 64 GB | 32 GB | 120 GB/s | — |
| Custom RTX 5090 AI Workstation (Value Build) | Workstation | 32 GB | 32 GB | 1792 GB/s | 1 |
| HP Elite Mini 800 G9 (Core i7-13700T) | Mini PC | 64 GB | 32 GB | 83 GB/s | — |
| HP OMEN 45L RTX 5090 Desktop | Workstation | 32 GB | 32 GB | — | 1 |
| Intel NUC 13 Pro (Core i7-1360P) | Mini PC | 64 GB | 32 GB | 83 GB/s | — |
| MinisForum UM780 XTX (Ryzen 7 7840HS) | Mini PC | 64 GB | 32 GB | 89 GB/s | — |
| MinisForum UM890 Pro (Ryzen 9 8945HS) | Mini PC | 64 GB | 32 GB | 89 GB/s | — |
| Puget Systems Datum (1x RTX 5090) | Workstation | 32 GB | 32 GB | 1792 GB/s | 1 |
| Sentinel RTX 5090 Tower Workstation | Workstation | 32 GB | 32 GB | — | 1 |
| ASRock NUC BOX-255H (Core Ultra 7 255H) | Mini PC | 96 GB | 48 GB | 102 GB/s | — |
| Apple Mac Studio M2 Ultra (64GB) | Workstation | 64 GB | 48 GB | 800 GB/s | 1 |
| Custom Dual RTX 4090 Training Workstation | Workstation | 48 GB | 48 GB | 1008 GB/s | 2 |
| Dell Precision 7960 Tower (1x RTX 6000 Ada) | Workstation | 48 GB | 48 GB | 960 GB/s | 1 |
| HP Z8 Fury G5 (1x RTX 6000 Ada) | Workstation | 48 GB | 48 GB | 960 GB/s | 1 |
| AMD Ryzen AI Halo Developer Platform (Max+ 395) | Mini PC | 128 GB | 64 GB | 256 GB/s | — |
| ArsenalPC MES2X Dual RTX 5090 AI Workstation | Workstation | 64 GB | 64 GB | — | 2 |
| GMKtec EVO-X2 (Ryzen AI Max+ 395) | Mini PC | 128 GB | 64 GB | 256 GB/s | — |
| Mac mini M4 Max (128GB) | Mini PC | 128 GB | 64 GB | 546 GB/s | — |
| MinisForum MS-S1 MAX (Ryzen AI Max+ 395) | Mini PC | 128 GB | 64 GB | 256 GB/s | — |
| BOXX APEXX 8R (1x RTX PRO 6000 Blackwell) | Workstation | 96 GB | 96 GB | 1792 GB/s | 1 |
| GEEKOM A9 Mega AI Workstation | Workstation | 128 GB | 96 GB | — | 1 |
| NOVATECH RTX PRO 6000 AI Workstation | Workstation | 96 GB | 96 GB | — | 1 |
| System76 Thelio Major (1x RTX PRO 6000 Blackwell) | Workstation | 96 GB | 96 GB | 1792 GB/s | 1 |
| System76 Thelio Major (2x RTX 6000 Ada) | Workstation | 96 GB | 96 GB | 960 GB/s | 2 |
| Lenovo ThinkStation PGX | Workstation | 128 GB | 115.2 GB | 273 GB/s | 1 |
| NVIDIA DGX Spark | Workstation | 128 GB | 115.2 GB | 273 GB/s | 1 |
| BIZON G3000 G2 (4x RTX 5090) | Workstation | 128 GB | 128 GB | 1792 GB/s | 4 |
| Apple Mac Studio M2 Ultra (192GB) | Workstation | 192 GB | 144 GB | 800 GB/s | 1 |
| HP Z8 Fury G5 (4x RTX 6000 Ada) | Workstation | 192 GB | 192 GB | 960 GB/s | 4 |
Frequently asked questions
Can an RTX 4090 run Qwen 3 32B?
Yes. Qwen 3 32B needs about 22.7 GB of VRAM at Q4_K_M with a 4k context, which fits within the RTX 4090’s 24 GB. Budget contexts above roughly 12k tokens push the total past 24 GB, so enable YaRN context extension only on larger GPUs.
How much VRAM does Qwen 3 32B need at Q4?
Qwen 3 32B needs about 22.7 GB of VRAM at Q4_K_M with a 4k context: 19.68 GB of weights plus 10% overhead plus a 1.07 GB KV cache.
Does Qwen 3 32B support a 128k context locally?
Qwen 3 32B natively supports 40,960 tokens and extends to about 131k tokens with YaRN, per the Qwen 3 model card. The full extended-context KV cache alone needs about 43 GB of VRAM at FP16, so 128k contexts require 80 GB-class GPUs or KV-cache quantization.