Qwen3-Next-80B-A3B-Instruct GGUF size and VRAM requirements
Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF is a very large instruction-tuned chat model with 79.67 billion parameters, built on the qwen3next architecture. It is released under the apache-2.0 license and has been downloaded 23,980 times.
To run Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 46.07 GB (Q4_K_M, lowest quality) and 149.49 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included.
Available GGUF quantizations for Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF include Q4_K_M, Q5_0, Q5_K_M, Q6_K, Q8_0, GGUF. The model supports a native context length of up to 262,144 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 |
|---|---|---|---|---|---|---|---|
| Q4_K_M | 4.86 | Good | 45.09 GB | 0.19 GB | 46.07 GB | — | Insufficient |
| Q5_0 | 5.52 | Very good | 51.22 GB | 0.19 GB | 52.21 GB | — | Insufficient |
| Q5_K_M | 5.69 | Very good | 52.82 GB | 0.19 GB | 53.8 GB | — | Insufficient |
| Q6_K | 6.58 | Excellent | 61.03 GB | 0.19 GB | 62.02 GB | — | Insufficient |
| Q8_0 | 8.52 | Excellent | 78.99 GB | 0.19 GB | 79.97 GB | — | Insufficient |
| GGUF | 16.01 | Excellent | 148.51 GB | 0.19 GB | 149.49 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 Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF?
Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF is an instruction-tuned chat model with 79.67 billion parameters, based on the qwen3next architecture. It is released under the apache-2.0 license and distributed as GGUF files for local inference.
How much VRAM do you need to run Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF?
You need about 46.07 GB of VRAM to run Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF entirely on the GPU using the Q4_K_M quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF on an 8 GB GPU?
No. Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF does not fit on an 8 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF on a 16 GB GPU?
Partially. Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF only fits on a 16 GB GPU by offloading part of it to system RAM (with Q4_K_M), which runs but is slower.
Can I run Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF on a 24 GB GPU?
Partially. Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF only fits on a 24 GB GPU by offloading part of it to system RAM (with Q6_K), which runs but is slower.
What context length does Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF support?
Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF supports a native context length of up to 262,144 tokens. A longer context grows the KV cache, so it increases the memory needed to run the model.
What is the best quantization for Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF?
For Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF, a strong default is Q4_K_M, which needs about 46.07 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.