Qwen35B-Agent-R2-i1 GGUF size and VRAM requirements
mradermacher/Qwen35B-Agent-R2-i1-GGUF is a very large language model with 34.66 billion parameters, built on the qwen35moe architecture. It is released under the agpl-3.0 license and has been downloaded 11,836 times.
To run mradermacher/Qwen35B-Agent-R2-i1-GGUF locally at a 4,096-token context, its quantized versions need between 1.06 GB (GGUF, lowest quality) and 27.43 GB (Q6_K, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is IQ1_S, needing about 7.85 GB. That means mradermacher/Qwen35B-Agent-R2-i1-GGUF fits entirely in the VRAM of a 6 GB GPU or larger, running fully on the GPU.
Available GGUF quantizations for mradermacher/Qwen35B-Agent-R2-i1-GGUF include GGUF, IQ1_S, IQ1_M, IQ2_XXS, IQ2_XS, IQ2_S, IQ2_M, Q2_K_S, Q2_K, IQ3_XXS, IQ3_XS, Q3_K_S, IQ3_S, IQ3_M, Q3_K_M, Q3_K_L, IQ4_XS, Q4_0, Q4_K_S, Q4_K_M, Q4_1, Q5_K_S, Q5_K_M, Q6_K. 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 |
|---|---|---|---|---|---|---|---|
| GGUF | 0.04 | Very low | 0.18 GB | 0.08 GB | 1.06 GB | 2234.4 t/s | Fits in VRAM |
| IQ1_S | 1.73 | Very low | 6.97 GB | 0.08 GB | 7.85 GB | 57.4 t/s | Fits in VRAM |
| IQ1_M | 1.9 | Very low | 7.67 GB | 0.08 GB | 8.55 GB | 6.5 t/s | Offload |
| IQ2_XXS | 2.19 | Very low | 8.85 GB | 0.08 GB | 9.72 GB | 5.7 t/s | Offload |
| IQ2_XS | 2.43 | Very low | 9.79 GB | 0.08 GB | 10.66 GB | 5.1 t/s | Offload |
| IQ2_S | 2.46 | Very low | 9.92 GB | 0.08 GB | 10.8 GB | 5.0 t/s | Offload |
| IQ2_M | 2.69 | Low | 10.86 GB | 0.08 GB | 11.74 GB | 4.6 t/s | Offload |
| Q2_K_S | 2.8 | Low | 11.32 GB | 0.08 GB | 12.2 GB | 4.4 t/s | Offload |
| Q2_K | 2.99 | Low | 12.05 GB | 0.08 GB | 12.93 GB | 4.1 t/s | Offload |
| IQ3_XXS | 3.14 | Low | 12.69 GB | 0.08 GB | 13.57 GB | 3.9 t/s | Offload |
| IQ3_XS | 3.34 | Fair | 13.49 GB | 0.08 GB | 14.37 GB | 3.7 t/s | Offload |
| Q3_K_S | 3.5 | Fair | 14.14 GB | 0.08 GB | 15.02 GB | 3.5 t/s | Offload |
| IQ3_S | 3.52 | Fair | 14.2 GB | 0.08 GB | 15.08 GB | 3.5 t/s | Offload |
| IQ3_M | 3.56 | Fair | 14.38 GB | 0.08 GB | 15.26 GB | 3.5 t/s | Offload |
| Q3_K_M | 3.87 | Fair | 15.61 GB | 0.08 GB | 16.49 GB | 3.2 t/s | Offload |
| Q3_K_L | 4.18 | Fair | 16.87 GB | 0.08 GB | 17.75 GB | 3.0 t/s | Offload |
| IQ4_XS | 4.32 | Good | 17.44 GB | 0.08 GB | 18.32 GB | 2.9 t/s | Offload |
| Q4_0 | 4.57 | Good | 18.44 GB | 0.08 GB | 19.32 GB | 2.7 t/s | Offload |
| Q4_K_S | 4.59 | Good | 18.52 GB | 0.08 GB | 19.4 GB | 2.7 t/s | Offload |
| Q4_K_M | 4.89 | Good | 19.71 GB | 0.08 GB | 20.59 GB | 2.5 t/s | Offload |
| Q4_1 | 5.04 | Very good | 20.35 GB | 0.08 GB | 21.23 GB | 2.5 t/s | Offload |
| Q5_K_S | 5.54 | Very good | 22.33 GB | 0.08 GB | 23.21 GB | 2.2 t/s | Offload |
| Q5_K_M | 5.71 | Very good | 23.03 GB | 0.08 GB | 23.91 GB | 2.2 t/s | Offload |
| Q6_K | 6.58 | Excellent | 26.56 GB | 0.08 GB | 27.43 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 mradermacher/Qwen35B-Agent-R2-i1-GGUF?
mradermacher/Qwen35B-Agent-R2-i1-GGUF is a language model with 34.66 billion parameters, based on the qwen35moe architecture. It is released under the agpl-3.0 license and distributed as GGUF files for local inference.
How much VRAM do you need to run mradermacher/Qwen35B-Agent-R2-i1-GGUF?
You need about 1.06 GB of VRAM to run mradermacher/Qwen35B-Agent-R2-i1-GGUF entirely on the GPU using the GGUF quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run mradermacher/Qwen35B-Agent-R2-i1-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run mradermacher/Qwen35B-Agent-R2-i1-GGUF fully on the GPU using IQ1_S (about 7.85 GB).
Can I run mradermacher/Qwen35B-Agent-R2-i1-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run mradermacher/Qwen35B-Agent-R2-i1-GGUF fully on the GPU using IQ3_M (about 15.26 GB).
Can I run mradermacher/Qwen35B-Agent-R2-i1-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run mradermacher/Qwen35B-Agent-R2-i1-GGUF fully on the GPU using Q5_K_M (about 23.91 GB).
What context length does mradermacher/Qwen35B-Agent-R2-i1-GGUF support?
mradermacher/Qwen35B-Agent-R2-i1-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 mradermacher/Qwen35B-Agent-R2-i1-GGUF?
For mradermacher/Qwen35B-Agent-R2-i1-GGUF, a strong default is Q4_K_M, which needs about 20.59 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.