Qwen3-30B-A3B GGUF size and VRAM requirements
MaziyarPanahi/Qwen3-30B-A3B-GGUF is a very large language model with 30.53 billion parameters, built on the qwen3moe architecture. It has been downloaded 242,114 times.
Qwen3-30B-A3B-GGUF is a Mixture-of-Experts model with 128 experts, of which 8 are active on each token. Routing only 8 of 128 experts makes it noticeably faster than a dense model of the same size — but every expert still has to be held in memory, so the numbers below are set by the full parameter count, not the active one. It is built from 48 transformer layers, a hidden size of 2,048, 32 attention heads. It uses grouped-query attention (32 query heads sharing 4 key/value heads), which already trims KV-cache memory compared with full multi-head attention.
To run MaziyarPanahi/Qwen3-30B-A3B-GGUF locally at a 4,096-token context, its quantized versions need between 11.66 GB (Q2_K, lowest quality) and 24.54 GB (Q6_K, highest quality) of memory, weights plus KV cache and a system margin included. Context length drives that memory directly: at 4,096 tokens the KV cache for Qwen3-30B-A3B-GGUF is about 0.38 GB, rising to roughly 3.75 GB at its full 40,960-token context. Shorter prompts free up memory for a higher-quality quantization.
For most users the best balance is Q5_K_M, needing about 21.41 GB. That means MaziyarPanahi/Qwen3-30B-A3B-GGUF fits entirely in the VRAM of a 12 GB GPU or larger, running fully on the GPU.
Available GGUF quantizations for MaziyarPanahi/Qwen3-30B-A3B-GGUF include Q2_K, Q3_K_M, Q3_K_L, Q4_K_M, Q5_K_M, Q6_K. The model supports a native context length of up to 40,960 tokens; a longer context grows the KV cache and the memory needed.
GGUF file size and memory by quantization
Compare real GGUF weight sizes, estimated KV cache and total memory for Q4, Q5, Q8 and every quantization published in this repository.
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| Q2_K | 2.95 | Low | 10.49 GB | 0.38 GB | 11.66 GB | 4.8 t/s | Offload |
| Q3_K_M | 3.85 | Fair | 13.7 GB | 0.38 GB | 14.88 GB | 3.6 t/s | Offload |
| Q3_K_L | 4.17 | Fair | 14.81 GB | 0.38 GB | 15.98 GB | 3.4 t/s | Offload |
| Q4_K_M | 4.86 | Good | 17.28 GB | 0.38 GB | 18.46 GB | 2.9 t/s | Offload |
| Q5_K_M | 5.69 | Very good | 20.23 GB | 0.38 GB | 21.41 GB | 2.5 t/s | Offload |
| Q6_K | 6.57 | Excellent | 23.37 GB | 0.38 GB | 24.54 GB | — | Insufficient |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
What kind of model is MaziyarPanahi/Qwen3-30B-A3B-GGUF?
MaziyarPanahi/Qwen3-30B-A3B-GGUF is a language model with 30.53 billion parameters, based on the qwen3moe architecture. It is distributed as GGUF files for local inference.
Is MaziyarPanahi/Qwen3-30B-A3B-GGUF a Mixture-of-Experts (MoE) model?
Yes. MaziyarPanahi/Qwen3-30B-A3B-GGUF is a Mixture-of-Experts model with 128 experts, of which 8 are activated per token. That makes it faster than a dense model of the same size, but all 128 experts must be loaded into memory, so the VRAM/RAM it needs is driven by the total parameter count, not the active one.
How much VRAM do you need to run MaziyarPanahi/Qwen3-30B-A3B-GGUF?
You need about 11.66 GB of VRAM to run MaziyarPanahi/Qwen3-30B-A3B-GGUF entirely on the GPU using the Q2_K quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run MaziyarPanahi/Qwen3-30B-A3B-GGUF on an 8 GB GPU?
Partially. MaziyarPanahi/Qwen3-30B-A3B-GGUF only fits on an 8 GB GPU by offloading part of it to system RAM (with Q5_K_M), which runs but is slower.
Can I run MaziyarPanahi/Qwen3-30B-A3B-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run MaziyarPanahi/Qwen3-30B-A3B-GGUF fully on the GPU using Q3_K_L (about 15.98 GB).
Can I run MaziyarPanahi/Qwen3-30B-A3B-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run MaziyarPanahi/Qwen3-30B-A3B-GGUF fully on the GPU using Q5_K_M (about 21.41 GB).
What context length does MaziyarPanahi/Qwen3-30B-A3B-GGUF support?
MaziyarPanahi/Qwen3-30B-A3B-GGUF supports a native context length of up to 40,960 tokens. A longer context grows the KV cache, so it increases the memory needed to run the model.
What is the best quantization for MaziyarPanahi/Qwen3-30B-A3B-GGUF?
For MaziyarPanahi/Qwen3-30B-A3B-GGUF, a strong default is Q4_K_M, which needs about 18.46 GB and keeps most of the quality while roughly halving the memory versus 8-bit. With VRAM to spare, Q5_K_M or Q6_K add a little more quality; if you are tight on memory, a smaller quantization still runs. Pick the highest quantization that fits your VRAM.