Hy3 GGUF Size & VRAM Requirements
Hy3-GGUF is a very large language model focused on general purpose & instruction. With approximately 298.79 billion parameters, it balances local hardware requirements with reasoning and generation fidelity. It is built on the hy_v3 architecture with a native context window of up to 262,144 tokens. The model is primarily recommended for general knowledge, document summarization, email drafting, language translation, and multi-turn conversational chat. Released under the apache-2.0 license, it can be executed fully offline without sending data to external APIs.
Hy3-GGUF is a Mixture-of-Experts model with 192 experts, of which 8 are active on each token. Routing only 8 of 192 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 80 transformer layers, a hidden size of 4,096, 64 attention heads. It uses grouped-query attention (64 query heads sharing 8 key/value heads), which already trims KV-cache memory compared with full multi-head attention.
To run AngelSlim/Hy3-GGUF locally at a 4,096-token context, its quantized versions need between 87.5 GB (IQ1_M, lowest quality) and 174.03 GB (Q4_K_M, 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 Hy3-GGUF is about 1.25 GB, rising to roughly 80.0 GB at its full 262,144-token context. Shorter prompts free up memory for a higher-quality quantization.
Available GGUF quantizations for AngelSlim/Hy3-GGUF include IQ1_M, Q2_K_XL, Q3_K_M, Q4_K_M. The model supports a native context length of up to 262,144 tokens; a longer context grows the KV cache and the memory needed.
Recommended Use Cases & Local Setup
Ideal for: General knowledge, document summarization, email drafting, language translation, and multi-turn conversational chat.
Simple one-line CLI installation running silently in the background
Polished desktop client with one-click model downloads and GPU offloading
Open-source privacy-focused desktop assistant
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 | 1.25 GB | 87.5 GB | — | Insufficient |
| Q2_K_XL | 2.72 | Low | 94.6 GB | 1.25 GB | 96.65 GB | — | Insufficient |
| Q3_K_M | 3.65 | Fair | 127.08 GB | 1.25 GB | 129.13 GB | — | Insufficient |
| Q4_K_M | 4.94 | Good | 171.98 GB | 1.25 GB | 174.03 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 AngelSlim/Hy3-GGUF?
AngelSlim/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.
Is AngelSlim/Hy3-GGUF a Mixture-of-Experts (MoE) model?
Yes. AngelSlim/Hy3-GGUF is a Mixture-of-Experts model with 192 experts, of which 8 are activated per token. That makes it faster than a dense model of the same size, but all 192 experts must be loaded into memory, so the VRAM/RAM it needs is driven by the total parameter count, not the active one.
Can I run AngelSlim/Hy3-GGUF on an 8 GB GPU?
No. AngelSlim/Hy3-GGUF does not fit on an 8 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run AngelSlim/Hy3-GGUF on a 16 GB GPU?
No. AngelSlim/Hy3-GGUF does not fit on a 16 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run AngelSlim/Hy3-GGUF on a 24 GB GPU?
No. AngelSlim/Hy3-GGUF does not fit on a 24 GB GPU, even with the smallest quantization and system RAM offloading.
What context length does AngelSlim/Hy3-GGUF support?
AngelSlim/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 AngelSlim/Hy3-GGUF?
For AngelSlim/Hy3-GGUF, a strong default is Q4_K_M, which needs about 174.03 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.