Run lmstudio-community/Qwen3.5-9B-GGUF locally
lmstudio-community/Qwen3.5-9B-GGUF is a large language model with 8.95 billion parameters, built on the qwen35 architecture. It is released under the apache-2.0 license and has been downloaded 377,333 times.
To run lmstudio-community/Qwen3.5-9B-GGUF locally at a 4,096-token context, its quantized versions need between 2.13 GB (BF16, lowest quality) and 10.15 GB (Q8_0, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q4_K_M, needing about 6.52 GB. That means lmstudio-community/Qwen3.5-9B-GGUF fits entirely in the VRAM of a 6 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| BF16 | 0.82 | Very low | 0.86 GB | 0.47 GB | 2.13 GB | 466.0 t/s | Fits in VRAM |
| Q4_K_M | 5.03 | Very good | 5.24 GB | 0.47 GB | 6.52 GB | 76.3 t/s | Fits in VRAM |
| Q6_K | 6.58 | Excellent | 6.85 GB | 0.47 GB | 8.13 GB | 7.3 t/s | Offload |
| Q8_0 | 8.51 | Excellent | 8.87 GB | 0.47 GB | 10.15 GB | 5.6 t/s | Offload |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
How much VRAM do you need to run lmstudio-community/Qwen3.5-9B-GGUF?
You need about 2.13 GB of VRAM to run lmstudio-community/Qwen3.5-9B-GGUF entirely on the GPU using the BF16 quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run lmstudio-community/Qwen3.5-9B-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run lmstudio-community/Qwen3.5-9B-GGUF fully on the GPU using Q4_K_M (about 6.52 GB).
Can I run lmstudio-community/Qwen3.5-9B-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run lmstudio-community/Qwen3.5-9B-GGUF fully on the GPU using Q8_0 (about 10.15 GB).
Can I run lmstudio-community/Qwen3.5-9B-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run lmstudio-community/Qwen3.5-9B-GGUF fully on the GPU using Q8_0 (about 10.15 GB).
What is the best quantization for lmstudio-community/Qwen3.5-9B-GGUF?
If memory allows, higher bits-per-weight means better quality. A common sweet spot is a Q4_K_M or Q5_K_M quantization, which keeps most of the quality while roughly halving the memory versus 8-bit. Pick the highest quantization that still fits in your VRAM.