Qwen3 VL 30B A3B Instruct — Hardware Requirements & GPU Compatibility
Vision
Qwen3 VL 30B A3B Instruct is Alibaba's 31-billion-parameter mixture-of-experts vision-language model in the Qwen 3 lineup, with about 3 billion parameters active per token (the A3B in its name). Because only the active experts run for each token, inference is faster than a similarly sized dense model, while all the weights still need to fit in memory. It handles images and video alongside text, supporting visual question answering, document and chart reading, and multi-image reasoning. At this size, local inference calls for quantization and a single high-end consumer GPU. The model supports a 262K token context window, suited to long documents, video transcripts, or extended visual conversations. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use, and was published in September 2025 as part of Qwen's third-generation vision-language family.
457.6K downloads
604 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 31.1B
- Architecture
- Qwen3VLMoeForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-09-30
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3 VL 30B A3B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 13.6 GB | 26.4 GB | 13.21 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 15.6 GB | 28.3 GB | 15.15 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 19.0 GB | 31.8 GB | 18.64 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 22.5 GB | 35.3 GB | 22.14 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 26.0 GB | 38.8 GB | 25.63 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 31.5 GB | 44.3 GB | 31.07 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 62.5 GB | 75.3 GB | 62.14 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Qwen3 VL 30B A3B Instruct?
Q4_K_M · 19.0 GBQwen3 VL 30B A3B Instruct (Q4_K_M) requires 19.0 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 31.8 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 VL 30B A3B Instruct?
Q4_K_M · 19.0 GB41 devices with unified memory can run Qwen3 VL 30B A3B Instruct, 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 VL 30B A3B Instruct
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 VL 30B A3B Instruct need?
Qwen3 VL 30B A3B Instruct requires 19.0 GB of VRAM at Q4_K_M, or 62.5 GB at BF16. Full 262K context adds up to 12.8 GB (31.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 31.1B × 4.8 bits ÷ 8 = 18.6 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_M19.0 GBQ4_K_M + full context31.8 GB- Can NVIDIA GeForce RTX 4090 run Qwen3 VL 30B A3B Instruct?
Yes, at Q5_K_M (22.5 GB) or lower. Higher quantizations like Q6_K (26.0 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen3 VL 30B A3B Instruct?
For Qwen3 VL 30B A3B Instruct, Q4_K_M (19.0 GB) offers the best balance of quality and VRAM usage. Q5_K_M (22.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 13.6 GB.
VRAM requirement by quantization
Q2_K13.6 GBQ4_K_M ★19.0 GBQ5_K_M22.5 GBQ6_K26.0 GBQ8_031.5 GBBF1662.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 VL 30B A3B Instruct on a Mac?
Qwen3 VL 30B A3B Instruct requires at least 13.6 GB at Q2_K, 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 VL 30B A3B Instruct locally?
Yes — Qwen3 VL 30B A3B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 19.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 VL 30B A3B Instruct?
At Q4_K_M, Qwen3 VL 30B A3B Instruct can reach ~99 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~159 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 ÷ 19.0 × 0.65 = ~323 tok/s
Estimated speed at Q4_K_M (19.0 GB)
~323 tok/s~159 tok/s~323 tok/s~295 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 VL 30B A3B Instruct?
At Q4_K_M, the download is about 18.64 GB. The full-precision BF16 version is 62.14 GB. The smallest option (Q2_K) is 13.21 GB.
- Which GPUs can run Qwen3 VL 30B A3B Instruct?
8 consumer GPUs can run Qwen3 VL 30B A3B Instruct at Q4_K_M (19.0 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 VL 30B A3B Instruct?
41 devices with unified memory can run Qwen3 VL 30B A3B Instruct at Q4_K_M (19.0 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.