Can you run Qwen3-Coder-30B-A3B-Instruct locally?
Qwen3-Coder family · 2025 · 30.5B parameters (3.3B activated per token) · Hugging Face model card
Qwen3-Coder-30B-A3B-Instruct is a mixture-of-experts coding model with 3.3B activated parameters per token and 30.5B total open weights (official card: 30.5B in total and 3.3B activated; HF safetensors.total=30,532,122,624). Native context is 262,144 tokens, apache-2.0. Local VRAM fit uses the 3.3B active count, not the 30B dense-equivalent: roughly 8 GB at Q4-class (calculated). Weights still occupy ~30B-class disk. Measured speeds: not yet published here.
Minimum: 8 GB+ memory (Q4-class on 3.3B active, calculated) · Recommended: 8 GB+ for comfortable context headroom (Q4-class on 3.3B active, calculated)
How much VRAM does Qwen3-Coder-30B-A3B-Instruct need at each quantization?
Qwen3-Coder-30B-A3B-Instruct needs 2.6 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 | 2.0 GB | 2.6 GB | 5.4 GB |
| Q5_K_M | 5.7 | 2.4 GB | 3.0 GB | 5.8 GB |
| Q6_K | 6.6 | 2.7 GB | 3.4 GB | 6.2 GB |
| Q8_0 | 8.5 | 3.5 GB | 4.3 GB | 7.1 GB |
| FP16 | 16 | 6.6 GB | 7.7 GB | 10.5 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 (48 layers, 4 KV heads, 128 head dim). Source: HF-verified 2026-09-22 via api (gated=false private=false params=30.53B safetensors.total=30532122624 downloadable_safetensors_sum=61066575656 ctx=262144 license=apache-2.0 repo=Qwen/Qwen3-Coder-30B-A3B-Instruct@b2cff646eb4bb1d68355c01b18ae02e7cf42d120 layers=48 kv_heads=4). MoE: num_experts=128 num_experts_per_tok=8 active_params_b=3.3 (vendor card; VRAM fit uses active, not dense-equivalent). VRAM figures are documented calculations, not measurements. No tok/s invented.
Which GPUs can run Qwen3-Coder-30B-A3B-Instruct locally?
At Q4_K_M with a 4k context, Qwen3-Coder-30B-A3B-Instruct needs 2.6 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 Qwen3-Coder-30B-A3B-Instruct comfortably (25%+ VRAM headroom)
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| Radeon RX 9050 (4GB OEM) | 4 GB | — | Consumer GPU | |
| Arc A580 | 8 GB | 512 GB/s | Consumer GPU | Check price |
| Arc A750 | 8 GB | 512 GB/s | Consumer GPU | Check price |
| GeForce RTX 3070EOL | 8 GB | 448 GB/s | Consumer GPU | Check price |
| GeForce RTX 4060 | 8 GB | 272 GB/s | Consumer GPU | Check price |
| GeForce RTX 4060 Ti 8GB | 8 GB | 288 GB/s | Consumer GPU | Check price |
| GeForce RTX 5050 | 8 GB | 320 GB/s | Consumer GPU | Check price |
| GeForce RTX 5060 | 8 GB | 448 GB/s | Consumer GPU | Check price |
| Radeon RX 7600 | 8 GB | 288 GB/s | Consumer GPU | Check price |
| Radeon RX 9050 | 8 GB | 288 GB/s | Consumer GPU | |
| Radeon RX 9060 XT 8GB | 8 GB | 320 GB/s | Consumer GPU | |
| Arc B570 | 10 GB | 380 GB/s | Consumer GPU | Check price |
| GeForce RTX 3080 10GBEOL | 10 GB | 760 GB/s | Consumer GPU | Check price |
| GeForce RTX 2080 TiEOL | 11 GB | 616 GB/s | Consumer GPU | Check price |
| Arc B580 | 12 GB | 456 GB/s | Consumer GPU | Check price |
| GeForce RTX 3060 12GB | 12 GB | 360 GB/s | Consumer GPU | Check price |
| GeForce RTX 3080 TiEOL | 12 GB | 912 GB/s | Consumer GPU | Check price |
| GeForce RTX 4070EOL | 12 GB | 504 GB/s | Consumer GPU | Check price |
| GeForce RTX 4070 SUPEREOL | 12 GB | 504 GB/s | Consumer GPU | Check price |
| GeForce RTX 5070 | 12 GB | 672 GB/s | Consumer GPU | Check price |
| Radeon RX 7700 XT | 12 GB | 432 GB/s | Consumer GPU | Check price |
| Radeon RX 9070 GRE | 12 GB | 432 GB/s | Consumer GPU | |
| Arc A770 16GB | 16 GB | 560 GB/s | Consumer GPU | Check price |
| GeForce RTX 4060 Ti 16GB | 16 GB | 288 GB/s | Consumer GPU | Check price |
| GeForce RTX 4070 Ti SUPEREOL | 16 GB | 672 GB/s | Consumer GPU | Check price |
| GeForce RTX 4080EOL | 16 GB | 716 GB/s | Consumer GPU | Check price |
| GeForce RTX 4080 SUPER | 16 GB | 736 GB/s | Consumer GPU | Check price |
| GeForce RTX 5060 Ti 16GB | 16 GB | 448 GB/s | Consumer GPU | Check price |
| GeForce RTX 5070 Ti | 16 GB | 896 GB/s | Consumer GPU | Check price |
| GeForce RTX 5080 | 16 GB | 960 GB/s | Consumer GPU | Check price |
| Intel Arc Pro B50 | 16 GB | 224 GB/s | Pro GPU | |
| NVIDIA RTX PRO 2000 Blackwell | 16 GB | 288 GB/s | Pro GPU | Check price |
| Radeon RX 6800 XTEOL | 16 GB | 512 GB/s | Consumer GPU | Check price |
| Radeon RX 7600 XT | 16 GB | 288 GB/s | Consumer GPU | Check price |
| Radeon RX 7800 XT | 16 GB | 624 GB/s | Consumer GPU | Check price |
| Radeon RX 9060 XT (16GB) | 16 GB | 320 GB/s | Consumer GPU | |
| Radeon RX 9060 XT 16GB | 16 GB | 320 GB/s | Consumer GPU | |
| Radeon RX 9060 XT LP (16GB) | 16 GB | 320 GB/s | Consumer GPU | |
| Radeon RX 9070 | 16 GB | 640 GB/s | Consumer GPU | Check price |
| Radeon RX 9070 XT | 16 GB | 640 GB/s | Consumer GPU | Check price |
| Radeon RX 7900 XT | 20 GB | 800 GB/s | Consumer GPU | Check price |
| 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 |
| Intel Arc Pro B60 | 24 GB | 456 GB/s | Pro GPU | |
| 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 |
| AMD Radeon PRO W6800 | 32 GB | 512 GB/s | Pro GPU | |
| 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 |
| Intel Arc Pro B65 | 32 GB | 608 GB/s | Pro GPU | |
| Intel Arc Pro B70 | 32 GB | 608 GB/s | Pro GPU | |
| 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 Dual Slot | 48 GB | 864 GB/s | Pro 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 |
| NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition | 96 GB | — | Pro GPU | |
| NVIDIA RTX PRO 6000 Blackwell Server Edition | 96 GB | — | Pro GPU | |
| 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 MI350X | 288 GB | 8000 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 | |
| AMD Instinct MI430X | 432 GB | 23300 GB/s | Data Center GPU | |
| AMD Instinct MI455X | 432 GB | 23300 GB/s | Data Center GPU |
Can a Mac run Qwen3-Coder-30B-A3B-Instruct?
Yes — these Apple Silicon machines fit Qwen3-Coder-30B-A3B-Instruct 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 M3 | 24 GB | 18 GB | 150 GB/s |
| Apple M4 | 32 GB | 24 GB | 120 GB/s |
| M6 (Mac mini) | 32 GB | 24 GB | 170 GB/s |
| Apple M3 Pro | 36 GB | 27 GB | 300 GB/s |
| Apple M4 Pro | 64 GB | 48 GB | 273 GB/s |
| Apple M5 Pro | 64 GB | 48 GB | 307 GB/s |
| M5 Pro (Mac mini) | 64 GB | 48 GB | 307 GB/s |
| Apple M3 Max | 128 GB | 96 GB | 400 GB/s |
| Apple M5 Max | 128 GB | 96 GB | 614 GB/s |
| M4 Max (MacBook Pro) | 128 GB | 96 GB | 546 GB/s |
| M5 Max (Mac Studio) | 128 GB | 96 GB | — |
| M5 Ultra (Mac Studio) | 512 GB | 384 GB | 1200 GB/s |
Can Qwen3-Coder-30B-A3B-Instruct run on mini PCs, Jetson, or NPU devices?
These edge and NPU devices from our database have enough memory for Qwen3-Coder-30B-A3B-Instruct at Q4_K_M. Their memory bandwidth is far below discrete GPUs, so expect a fraction of desktop generation speed.
| Device | Memory | Bandwidth | Type |
|---|---|---|---|
| Jetson Orin Nano 8GB Super (Dev Kit) | 8 GB | 102 GB/s | Edge Compute Device |
| NVIDIA Jetson Orin Nano 2 | 8 GB | — | Edge Compute Device |
| Jetson Orin NX 16GB Super | 16 GB | 102 GB/s | Edge Compute Device |
| 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 |
| NVIDIA Jetson AGX Thor Developer Kit | 128 GB | — | Edge Compute Device |
| NVIDIA RTX Spark (superchip) | 128 GB | — | NPU Chip |
| NVIDIA RTX Spark N1X (desktop superchip) | 128 GB | — | NPU Chip |
Which pre-built systems can run Qwen3-Coder-30B-A3B-Instruct?
These mini PCs and workstations from our database fit Qwen3-Coder-30B-A3B-Instruct 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.
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