Qwen3 30B A3B — Hardware Requirements & GPU Compatibility
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Qwen3 30B A3B is a Mixture of Experts (MoE) model from Alibaba Cloud's Qwen 3 series, with 30 billion total parameters and approximately 3 billion active parameters per forward pass. The MoE architecture delivers quality significantly above what a standard 3B dense model could achieve, while keeping per-token compute costs low. It supports hybrid thinking mode for flexible reasoning. The model requires VRAM proportional to its full 30B parameter count for weight loading, but its low active parameter count results in fast inference throughput. It is an efficient option for users who want quality beyond dense small models without the full cost of larger architectures. Released under the Apache 2.0 license.
1.2M downloads
864 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 30B
- Architecture
- Qwen3MoeForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-07-26
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Qwen3 30B A3B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 8.7 GB | 10.6 GB | 8.25 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_XS | 2.40 | 9.4 GB | 11.3 GB | 9.00 GB | Importance-weighted 2-bit, extra small |
| IQ2_S | 2.50 | 9.8 GB | 11.7 GB | 9.38 GB | Importance-weighted 2-bit, small |
| IQ2_M | 2.70 | 10.5 GB | 12.4 GB | 10.13 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 12.0 GB | 13.9 GB | 11.63 GB | Importance-weighted 3-bit |
| IQ3_XS | 3.30 | 12.8 GB | 14.7 GB | 12.38 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 13.2 GB | 15.1 GB | 12.75 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 13.5 GB | 15.4 GB | 13.13 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 13.9 GB | 15.8 GB | 13.50 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 15.0 GB | 16.9 GB | 14.63 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 15.4 GB | 17.3 GB | 15.00 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 15.8 GB | 17.7 GB | 15.37 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 16.5 GB | 18.4 GB | 16.13 GB | Importance-weighted 4-bit, compact |
| Q4_1 | 4.50 | 17.3 GB | 19.2 GB | 16.88 GB | 4-bit legacy quantization with offset |
| Q4_K_S | 4.50 | 17.3 GB | 19.2 GB | 16.88 GB | 4-bit small quantization |
| IQ4_NL | 4.50 | 17.3 GB | 19.2 GB | 16.88 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_M | 4.80 | 18.4 GB | 20.3 GB | 18.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 18.8 GB | 20.7 GB | 18.38 GB | 4-bit large quantization |
| Q5_K_S | 5.50 | 21.0 GB | 22.9 GB | 20.63 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 21.8 GB | 23.7 GB | 21.38 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 22.1 GB | 24.1 GB | 21.75 GB | 5-bit large quantization |
| Q6_K | 6.60 | 25.1 GB | 27.1 GB | 24.75 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 30.4 GB | 32.3 GB | 30.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 30B A3B?
Q4_K_M · 18.4 GBQwen3 30B A3B (Q4_K_M) requires 18.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 24+ GB is recommended. Using the full 41K context window can add up to 1.9 GB, bringing total usage to 20.3 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Qwen3 30B A3B?
Q4_K_M · 18.4 GB21 devices with unified memory can run Qwen3 30B A3B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Derivatives (5)
Frequently Asked Questions
- How much VRAM does Qwen3 30B A3B need?
Qwen3 30B A3B requires 18.4 GB of VRAM at Q4_K_M, or 30.4 GB at Q8_0. Full 41K context adds up to 1.9 GB (20.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 30B × 4.8 bits ÷ 8 = 18 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.3 GB (at full 41K context)
VRAM usage by quantization
Q4_K_M18.4 GBQ4_K_M + full context20.3 GB- Can NVIDIA GeForce RTX 4090 run Qwen3 30B A3B?
Yes, at Q5_K_L (22.1 GB) or lower. Higher quantizations like Q6_K (25.1 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen3 30B A3B?
For Qwen3 30B A3B, Q4_K_M (18.4 GB) offers the best balance of quality and VRAM usage. Q4_K_L (18.8 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 8.7 GB.
VRAM requirement by quantization
IQ2_XXS8.7 GB~53%Q2_K13.2 GB~75%Q3_K_L15.8 GB~86%Q4_K_M ★18.4 GB~89%Q4_K_L18.8 GB~90%Q8_030.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 30B A3B on a Mac?
Qwen3 30B A3B requires at least 8.7 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Qwen3 30B A3B locally?
Yes — Qwen3 30B A3B can run locally on consumer hardware. At Q4_K_M quantization it needs 18.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 30B A3B?
At Q4_K_M, Qwen3 30B A3B can reach ~158 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~36 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 18.4 × 0.55 = ~158 tok/s
Estimated speed at Q4_K_M (18.4 GB)
AMD Instinct MI300X~158 tok/sNVIDIA GeForce RTX 4090~36 tok/sNVIDIA H100 SXM~118 tok/sAMD Instinct MI250X~98 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 30B A3B?
At Q4_K_M, the download is about 18.00 GB. The full-precision Q8_0 version is 30.00 GB. The smallest option (IQ2_XXS) is 8.25 GB.