Hy3 GGUF size and VRAM requirements
vcruz305/Hy3-GGUF is a very large language model with 298.79 billion parameters, built on the hy_v3 architecture. It is released under the apache-2.0 license and has been downloaded 877,722 times.
To run vcruz305/Hy3-GGUF locally at a 4,096-token context, its quantized versions need between 86.89 GB (IQ1_M, lowest quality) and 297.27 GB (Q8_0, highest quality) of memory, weights plus KV cache and a system margin included.
Available GGUF quantizations for vcruz305/Hy3-GGUF include IQ1_M, IQ2_M, Q2_K, IQ3_XXS, Q3_K_M, IQ4_XS, Q4_K_M, Q5_K_M, Q8_0. 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_M | 2.46 | Very low | 85.45 GB | 0.63 GB | 86.89 GB | — | Insufficient |
| IQ2_M | 2.68 | Low | 93.14 GB | 0.63 GB | 94.57 GB | — | Insufficient |
| Q2_K | 2.91 | Low | 101.28 GB | 0.63 GB | 102.71 GB | — | Insufficient |
| IQ3_XXS | 3.14 | Low | 109.28 GB | 0.63 GB | 110.71 GB | — | Insufficient |
| Q3_K_M | 3.82 | Fair | 132.94 GB | 0.63 GB | 134.37 GB | — | Insufficient |
| IQ4_XS | 4.26 | Good | 148.22 GB | 0.63 GB | 149.65 GB | — | Insufficient |
| Q4_K_M | 4.85 | Good | 168.57 GB | 0.63 GB | 170.0 GB | — | Insufficient |
| Q5_K_M | 5.68 | Very good | 197.61 GB | 0.63 GB | 199.04 GB | — | Insufficient |
| Q8_0 | 8.51 | Excellent | 295.84 GB | 0.63 GB | 297.27 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 vcruz305/Hy3-GGUF?
vcruz305/Hy3-GGUF is a language model with 298.79 billion parameters, based on the hy_v3 architecture. It is released under the apache-2.0 license and distributed as GGUF files for local inference.
Can I run vcruz305/Hy3-GGUF on an 8 GB GPU?
No. vcruz305/Hy3-GGUF does not fit on an 8 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run vcruz305/Hy3-GGUF on a 16 GB GPU?
No. vcruz305/Hy3-GGUF does not fit on a 16 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run vcruz305/Hy3-GGUF on a 24 GB GPU?
No. vcruz305/Hy3-GGUF does not fit on a 24 GB GPU, even with the smallest quantization and system RAM offloading.
What context length does vcruz305/Hy3-GGUF support?
vcruz305/Hy3-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 vcruz305/Hy3-GGUF?
For vcruz305/Hy3-GGUF, a strong default is Q4_K_M, which needs about 170.0 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.