Hermes-4-70B GGUF size and VRAM requirements
Hermes-4-70B-GGUF is a very large language model focused on general purpose & instruction. With approximately 70.55 billion parameters, it balances local hardware requirements with reasoning and generation fidelity. It is built on the llama architecture with a native context window of up to 131,072 tokens. The model is primarily recommended for general knowledge, document summarization, email drafting, language translation, and multi-turn conversational chat. Packaged in the standard GGUF container, it runs fully offline in Ollama, LM Studio, and llama.cpp.
Under the hood it uses 80 transformer layers, a hidden size of 8,192, 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 lmstudio-community/Hermes-4-70B-GGUF locally at a 4,096-token context, its quantized versions need between 36.64 GB (Q3_K_L, lowest quality) and 71.88 GB (Q8_0, 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 Hermes-4-70B-GGUF is about 1.25 GB, rising to roughly 40.0 GB at its full 131,072-token context. Shorter prompts free up memory for a higher-quality quantization.
Available GGUF quantizations for lmstudio-community/Hermes-4-70B-GGUF include Q3_K_L, Q4_K_M, Q6_K, Q8_0. The model supports a native context length of up to 131,072 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 |
|---|---|---|---|---|---|---|---|
| Q3_K_L | 4.21 | Good | 34.59 GB | 1.25 GB | 36.64 GB | — | Insufficient |
| Q4_K_M | 4.82 | Good | 39.6 GB | 1.25 GB | 41.65 GB | — | Insufficient |
| Q6_K | 6.56 | Excellent | 53.91 GB | 1.25 GB | 55.96 GB | — | Insufficient |
| Q8_0 | 8.5 | Excellent | 69.83 GB | 1.25 GB | 71.88 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 lmstudio-community/Hermes-4-70B-GGUF?
lmstudio-community/Hermes-4-70B-GGUF is a language model with 70.55 billion parameters, based on the llama architecture. It is distributed as GGUF files for local inference.
How much VRAM do you need to run lmstudio-community/Hermes-4-70B-GGUF?
You need about 41.65 GB of VRAM to run lmstudio-community/Hermes-4-70B-GGUF entirely on the GPU using the Q4_K_M quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run lmstudio-community/Hermes-4-70B-GGUF on an 8 GB GPU?
No. lmstudio-community/Hermes-4-70B-GGUF does not fit on an 8 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run lmstudio-community/Hermes-4-70B-GGUF on a 16 GB GPU?
Partially. lmstudio-community/Hermes-4-70B-GGUF only fits on a 16 GB GPU by offloading part of it to system RAM (with Q4_K_M), which runs but is slower.
Can I run lmstudio-community/Hermes-4-70B-GGUF on a 24 GB GPU?
Partially. lmstudio-community/Hermes-4-70B-GGUF only fits on a 24 GB GPU by offloading part of it to system RAM (with Q8_0), which runs but is slower.
What context length does lmstudio-community/Hermes-4-70B-GGUF support?
lmstudio-community/Hermes-4-70B-GGUF supports a native context length of up to 131,072 tokens. A longer context grows the KV cache, so it increases the memory needed to run the model.
What is the best quantization for lmstudio-community/Hermes-4-70B-GGUF?
For lmstudio-community/Hermes-4-70B-GGUF, a strong default is Q4_K_M, which needs about 41.65 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.