Laguna-S-2.1 GGUF size and VRAM requirements
unsloth/Laguna-S-2.1-GGUF is a very large language model with 117.56 billion parameters, built on the laguna architecture. It is released under the openmdw-1.1 license and has been downloaded 129,601 times.
To run unsloth/Laguna-S-2.1-GGUF locally at a 4,096-token context, its quantized versions need between 32.44 GB (IQ1_S, lowest quality) and 220.04 GB (BF16, highest quality) of memory, weights plus KV cache and a system margin included.
Available GGUF quantizations for unsloth/Laguna-S-2.1-GGUF include IQ1_S, IQ1_M, IQ2_XXS, IQ2_M, Q2_K_XL, IQ3_XXS, IQ3_S, Q3_K_M, Q3_K_XL, IQ4_XS, IQ4_NL, Q4_K_S, GGUF, Q4_K_M, Q4_K_XL, 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 |
|---|---|---|---|---|---|---|---|
| IQ1_S | 2.3 | Very low | 31.45 GB | 0.19 GB | 32.44 GB | — | Insufficient |
| IQ1_M | 2.43 | Very low | 33.19 GB | 0.19 GB | 34.18 GB | — | Insufficient |
| IQ2_XXS | 2.53 | Very low | 34.64 GB | 0.19 GB | 35.62 GB | — | Insufficient |
| IQ2_M | 2.54 | Very low | 34.71 GB | 0.19 GB | 35.7 GB | — | Insufficient |
| Q2_K_XL | 2.7 | Low | 36.96 GB | 0.19 GB | 37.95 GB | — | Insufficient |
| IQ3_XXS | 3.01 | Low | 41.24 GB | 0.19 GB | 42.23 GB | — | Insufficient |
| IQ3_S | 3.3 | Low | 45.1 GB | 0.19 GB | 46.09 GB | — | Insufficient |
| Q3_K_M | 3.68 | Fair | 50.31 GB | 0.19 GB | 51.3 GB | — | Insufficient |
| Q3_K_XL | 3.68 | Fair | 50.38 GB | 0.19 GB | 51.37 GB | — | Insufficient |
| IQ4_XS | 3.92 | Fair | 53.61 GB | 0.19 GB | 54.6 GB | — | Insufficient |
| IQ4_NL | 4.0 | Fair | 54.71 GB | 0.19 GB | 55.7 GB | — | Insufficient |
| Q4_K_S | 4.67 | Good | 63.88 GB | 0.19 GB | 64.87 GB | — | Insufficient |
| GGUF | 4.84 | Good | 66.2 GB | 0.19 GB | 67.19 GB | — | Insufficient |
| Q4_K_M | 4.98 | Good | 68.1 GB | 0.19 GB | 69.09 GB | — | Insufficient |
| Q4_K_XL | 4.99 | Good | 68.35 GB | 0.19 GB | 69.34 GB | — | Insufficient |
| Q5_K_S | 5.62 | Very good | 76.98 GB | 0.19 GB | 77.97 GB | — | Insufficient |
| Q5_K_M | 5.98 | Very good | 81.83 GB | 0.19 GB | 82.81 GB | — | Insufficient |
| Q5_K_XL | 5.99 | Very good | 82.02 GB | 0.19 GB | 83.01 GB | — | Insufficient |
| Q6_K | 6.66 | Excellent | 91.19 GB | 0.19 GB | 92.18 GB | — | Insufficient |
| Q6_K_XL | 7.29 | Excellent | 99.73 GB | 0.19 GB | 100.71 GB | — | Insufficient |
| Q8_0 | 8.51 | Excellent | 116.44 GB | 0.19 GB | 117.42 GB | — | Insufficient |
| Q8_K_XL | 8.72 | Excellent | 119.31 GB | 0.19 GB | 120.3 GB | — | Insufficient |
| BF16 | 16.01 | Excellent | 219.05 GB | 0.19 GB | 220.04 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/Laguna-S-2.1-GGUF?
unsloth/Laguna-S-2.1-GGUF is a language model with 117.56 billion parameters, based on the laguna architecture. It is released under the openmdw-1.1 license and distributed as GGUF files for local inference.
How much VRAM do you need to run unsloth/Laguna-S-2.1-GGUF?
You need about 46.09 GB of VRAM to run unsloth/Laguna-S-2.1-GGUF entirely on the GPU using the IQ3_S quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run unsloth/Laguna-S-2.1-GGUF on an 8 GB GPU?
No. unsloth/Laguna-S-2.1-GGUF does not fit on an 8 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run unsloth/Laguna-S-2.1-GGUF on a 16 GB GPU?
Partially. unsloth/Laguna-S-2.1-GGUF only fits on a 16 GB GPU by offloading part of it to system RAM (with IQ3_S), which runs but is slower.
Can I run unsloth/Laguna-S-2.1-GGUF on a 24 GB GPU?
Partially. unsloth/Laguna-S-2.1-GGUF only fits on a 24 GB GPU by offloading part of it to system RAM (with Q4_K_XL), which runs but is slower.
What context length does unsloth/Laguna-S-2.1-GGUF support?
unsloth/Laguna-S-2.1-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/Laguna-S-2.1-GGUF?
For unsloth/Laguna-S-2.1-GGUF, a strong default is Q4_K_M, which needs about 69.09 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.