Qwen3 30B A3B Instruct 2507 — Hardware Requirements & GPU Compatibility
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Qwen3 30B A3B Instruct 2507 is a July 2025 updated mixture-of-experts model from Alibaba with 30 billion total parameters but only around 3 billion active during inference. This MoE architecture gives it a remarkably small memory and compute footprint relative to its total parameter count, letting users run a model with broad knowledge on mid-range hardware. The 2507 instruct refresh improves alignment and instruction-following quality over the original release. Because only a fraction of the weights are active at any given time, this model can often run on a single consumer GPU with 8 GB or more of VRAM when quantized, making it an excellent choice for users who want strong chat performance without heavyweight hardware.
774.9K downloads
816 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 30.5B
- Architecture
- Qwen3MoeForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-07-28
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Qwen3 30B A3B Instruct 2507 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 13.4 GB | 26.2 GB | 12.98 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 13.8 GB | 26.5 GB | 13.36 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 15.3 GB | 28.1 GB | 14.88 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 15.7 GB | 28.4 GB | 15.27 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 18.7 GB | 31.5 GB | 18.32 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 22.1 GB | 34.9 GB | 21.75 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 25.6 GB | 38.4 GB | 25.19 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 30.9 GB | 43.7 GB | 30.53 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 30B A3B Instruct 2507?
Q4_K_M · 18.7 GBQwen3 30B A3B Instruct 2507 (Q4_K_M) requires 18.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 25+ GB is recommended. Using the full 262K context window can add up to 12.8 GB, bringing total usage to 31.5 GB. 8 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 Instruct 2507?
Q4_K_M · 18.7 GB41 devices with unified memory can run Qwen3 30B A3B Instruct 2507, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Qwen3 30B A3B Instruct 2507
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Qwen3 30B A3B Instruct 2507 need?
Qwen3 30B A3B Instruct 2507 requires 18.7 GB of VRAM at Q4_K_M, or 61.5 GB at BF16. Full 262K context adds up to 12.8 GB (31.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 30.5B × 4.8 bits ÷ 8 = 18.3 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 13.2 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M18.7 GBQ4_K_M + full context31.5 GB- Can NVIDIA GeForce RTX 4090 run Qwen3 30B A3B Instruct 2507?
Yes, at Q5_K_L (22.5 GB) or lower. Higher quantizations like Q6_K (25.6 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen3 30B A3B Instruct 2507?
For Qwen3 30B A3B Instruct 2507, Q4_K_M (18.7 GB) offers the best balance of quality and VRAM usage. Q4_K_L (19.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 8.8 GB.
VRAM requirement by quantization
IQ2_XXS8.8 GBIQ3_S13.4 GBQ3_K_L16.1 GBQ4_K_M ★18.7 GBQ4_K_L19.1 GBBF1661.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 30B A3B Instruct 2507 on a Mac?
Qwen3 30B A3B Instruct 2507 requires at least 8.8 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 Instruct 2507 locally?
Yes — Qwen3 30B A3B Instruct 2507 can run locally on consumer hardware. At Q4_K_M quantization it needs 18.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 30B A3B Instruct 2507?
At Q4_K_M, Qwen3 30B A3B Instruct 2507 can reach ~235 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~35 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 18.7 × 0.65 = ~278 tok/s
Estimated speed at Q4_K_M (18.7 GB)
~278 tok/s~35 tok/s~278 tok/s~235 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 Instruct 2507?
At Q4_K_M, the download is about 18.32 GB. The full-precision BF16 version is 61.06 GB. The smallest option (IQ2_XXS) is 8.40 GB.
- Which GPUs can run Qwen3 30B A3B Instruct 2507?
8 consumer GPUs can run Qwen3 30B A3B Instruct 2507 at Q4_K_M (18.7 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Qwen3 30B A3B Instruct 2507?
41 devices with unified memory can run Qwen3 30B A3B Instruct 2507 at Q4_K_M (18.7 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.