Qwen3-Coder-Next GGUF size and VRAM requirements
unsloth/Qwen3-Coder-Next-GGUF is a very large code-focused language model with 79.67 billion parameters, built on the qwen3next architecture. It is released under the apache-2.0 license and has been downloaded 179,510 times.
Qwen3-Coder-Next-GGUF is a Mixture-of-Experts model with 512 experts, of which 10 are active on each token. Routing only 10 of 512 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, 16 attention heads. It uses grouped-query attention (16 query heads sharing 2 key/value heads), which already trims KV-cache memory compared with full multi-head attention.
To run unsloth/Qwen3-Coder-Next-GGUF locally at a 4,096-token context, its quantized versions need between 18.82 GB (TQ1_0, lowest quality) and 149.68 GB (BF16, 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-Coder-Next-GGUF is about 0.38 GB, rising to roughly 24.0 GB at its full 262,144-token context. Shorter prompts free up memory for a higher-quality quantization.
For most users the best balance is IQ2_XXS, needing about 22.89 GB. That means unsloth/Qwen3-Coder-Next-GGUF fits entirely in the VRAM of a 24 GB GPU or larger, running fully on the GPU.
Available GGUF quantizations for unsloth/Qwen3-Coder-Next-GGUF include TQ1_0, IQ1_S, IQ1_M, IQ2_XXS, IQ2_M, Q2_K_XL, IQ3_XXS, Q2_K, Q2_K_L, IQ3_S, Q3_K_S, Q3_K_XL, Q3_K_M, IQ4_XS, IQ4_NL, Q4_0, Q4_K_S, MXFP4, Q4_K_M, Q4_K_XL, Q4_1, Q5_K_S, Q5_K_M, Q5_K_XL, Q6_K, Q6_K_XL, Q8_0, Q8_K_XL, BF16. 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 |
|---|---|---|---|---|---|---|---|
| TQ1_0 | 1.9 | Very low | 17.64 GB | 0.38 GB | 18.82 GB | 2.8 t/s | Offload |
| IQ1_S | 2.16 | Very low | 20.03 GB | 0.38 GB | 21.21 GB | 2.5 t/s | Offload |
| IQ1_M | 2.18 | Very low | 20.21 GB | 0.38 GB | 21.39 GB | 2.5 t/s | Offload |
| IQ2_XXS | 2.34 | Very low | 21.71 GB | 0.38 GB | 22.89 GB | 2.3 t/s | Offload |
| IQ2_M | 2.51 | Very low | 23.25 GB | 0.38 GB | 24.42 GB | — | Insufficient |
| Q2_K_XL | 2.69 | Low | 24.92 GB | 0.38 GB | 26.1 GB | — | Insufficient |
| IQ3_XXS | 2.86 | Low | 26.53 GB | 0.38 GB | 27.7 GB | — | Insufficient |
| Q2_K | 2.93 | Low | 27.22 GB | 0.38 GB | 28.39 GB | — | Insufficient |
| Q2_K_L | 2.94 | Low | 27.29 GB | 0.38 GB | 28.46 GB | — | Insufficient |
| IQ3_S | 2.98 | Low | 27.65 GB | 0.38 GB | 28.83 GB | — | Insufficient |
| Q3_K_S | 3.47 | Fair | 32.23 GB | 0.38 GB | 33.4 GB | — | Insufficient |
| Q3_K_XL | 3.64 | Fair | 33.79 GB | 0.38 GB | 34.97 GB | — | Insufficient |
| Q3_K_M | 3.85 | Fair | 35.69 GB | 0.38 GB | 36.87 GB | — | Insufficient |
| IQ4_XS | 4.29 | Good | 39.75 GB | 0.38 GB | 40.92 GB | — | Insufficient |
| IQ4_NL | 4.53 | Good | 42.03 GB | 0.38 GB | 43.21 GB | — | Insufficient |
| Q4_0 | 4.55 | Good | 42.22 GB | 0.38 GB | 43.39 GB | — | Insufficient |
| Q4_K_S | 4.63 | Good | 42.92 GB | 0.38 GB | 44.09 GB | — | Insufficient |
| MXFP4 | 4.82 | Good | 44.73 GB | 0.38 GB | 45.91 GB | — | Insufficient |
| Q4_K_M | 4.95 | Good | 45.92 GB | 0.38 GB | 47.09 GB | — | Insufficient |
| Q4_K_XL | 4.98 | Good | 46.2 GB | 0.38 GB | 47.38 GB | — | Insufficient |
| Q4_1 | 5.03 | Very good | 46.62 GB | 0.38 GB | 47.79 GB | — | Insufficient |
| Q5_K_S | 5.6 | Very good | 51.99 GB | 0.38 GB | 53.16 GB | — | Insufficient |
| Q5_K_M | 5.95 | Very good | 55.17 GB | 0.38 GB | 56.35 GB | — | Insufficient |
| Q5_K_XL | 5.98 | Very good | 55.45 GB | 0.38 GB | 56.63 GB | — | Insufficient |
| Q6_K | 6.61 | Excellent | 61.27 GB | 0.38 GB | 62.45 GB | — | Insufficient |
| Q6_K_XL | 7.34 | Excellent | 68.1 GB | 0.38 GB | 69.27 GB | — | Insufficient |
| Q8_0 | 8.52 | Excellent | 78.99 GB | 0.38 GB | 80.16 GB | — | Insufficient |
| Q8_K_XL | 8.67 | Excellent | 80.42 GB | 0.38 GB | 81.59 GB | — | Insufficient |
| BF16 | 16.01 | Excellent | 148.51 GB | 0.38 GB | 149.68 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 unsloth/Qwen3-Coder-Next-GGUF?
unsloth/Qwen3-Coder-Next-GGUF is a code-focused language 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.
Is unsloth/Qwen3-Coder-Next-GGUF a Mixture-of-Experts (MoE) model?
Yes. unsloth/Qwen3-Coder-Next-GGUF is a Mixture-of-Experts model with 512 experts, of which 10 are activated per token. That makes it faster than a dense model of the same size, but all 512 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 unsloth/Qwen3-Coder-Next-GGUF?
You need about 22.89 GB of VRAM to run unsloth/Qwen3-Coder-Next-GGUF entirely on the GPU using the IQ2_XXS quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run unsloth/Qwen3-Coder-Next-GGUF on an 8 GB GPU?
Partially. unsloth/Qwen3-Coder-Next-GGUF only fits on an 8 GB GPU by offloading part of it to system RAM (with IQ2_XXS), which runs but is slower.
Can I run unsloth/Qwen3-Coder-Next-GGUF on a 16 GB GPU?
Partially. unsloth/Qwen3-Coder-Next-GGUF only fits on a 16 GB GPU by offloading part of it to system RAM (with Q4_1), which runs but is slower.
Can I run unsloth/Qwen3-Coder-Next-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run unsloth/Qwen3-Coder-Next-GGUF fully on the GPU using IQ2_XXS (about 22.89 GB).
What context length does unsloth/Qwen3-Coder-Next-GGUF support?
unsloth/Qwen3-Coder-Next-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 unsloth/Qwen3-Coder-Next-GGUF?
For unsloth/Qwen3-Coder-Next-GGUF, a strong default is Q4_K_M, which needs about 47.09 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.