meta-llama_Llama-4-Scout-17B-16E-Instruct-old GGUF size and VRAM requirements
meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF is a very large instruction-tuned chat model focused on general purpose & instruction. With approximately 108.64 billion parameters, it balances local hardware requirements with reasoning and generation fidelity. It is built on the llama4 architecture with a native context window of up to 10,485,760 tokens. The model is primarily recommended for general knowledge, document summarization, email drafting, language translation, and multi-turn conversational chat. Released under the other license, it can be executed fully offline without sending data to external APIs.
Under the hood it uses 48 transformer layers, a hidden size of 5,120, 40 attention heads. It uses grouped-query attention (40 query heads sharing 8 key/value heads), which already trims KV-cache memory compared with full multi-head attention.
To run bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF locally at a 4,096-token context, its quantized versions need between 26.06 GB (IQ1_M, lowest quality) and 202.31 GB (BF16, 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 meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF is about 0.75 GB, rising to roughly 1920.0 GB at its full 10,485,760-token context. Shorter prompts free up memory for a higher-quality quantization.
Available GGUF quantizations for bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF include IQ1_M, IQ2_XXS, IQ2_XS, IQ2_S, IQ2_M, Q2_K, Q2_K_L, IQ3_XXS, IQ3_XS, Q3_K_S, IQ3_M, Q3_K_M, Q3_K_L, Q3_K_XL, IQ4_XS, IQ4_NL, Q4_0, Q4_K_M, Q4_K_L, Q4_1, Q5_K_L, Q6_K_L, Q8_0, BF16. The model supports a native context length of up to 10,485,760 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 | 1.94 | Very low | 24.51 GB | 0.75 GB | 26.06 GB | — | Insufficient |
| IQ2_XXS | 2.22 | Very low | 28.09 GB | 0.75 GB | 29.64 GB | — | Insufficient |
| IQ2_XS | 2.43 | Very low | 30.68 GB | 0.75 GB | 32.23 GB | — | Insufficient |
| IQ2_S | 2.53 | Very low | 31.98 GB | 0.75 GB | 33.53 GB | — | Insufficient |
| IQ2_M | 2.73 | Low | 34.56 GB | 0.75 GB | 36.11 GB | — | Insufficient |
| Q2_K | 3.17 | Low | 40.03 GB | 0.75 GB | 41.58 GB | — | Insufficient |
| Q2_K_L | 3.24 | Low | 40.97 GB | 0.75 GB | 42.52 GB | — | Insufficient |
| IQ3_XXS | 3.31 | Fair | 41.87 GB | 0.75 GB | 43.42 GB | — | Insufficient |
| IQ3_XS | 3.49 | Fair | 44.19 GB | 0.75 GB | 45.74 GB | — | Insufficient |
| Q3_K_S | 3.66 | Fair | 46.34 GB | 0.75 GB | 47.89 GB | — | Insufficient |
| IQ3_M | 3.71 | Fair | 46.87 GB | 0.75 GB | 48.42 GB | — | Insufficient |
| Q3_K_M | 4.0 | Fair | 50.59 GB | 0.75 GB | 52.14 GB | — | Insufficient |
| Q3_K_L | 4.26 | Good | 53.83 GB | 0.75 GB | 55.38 GB | — | Insufficient |
| Q3_K_XL | 4.32 | Good | 54.67 GB | 0.75 GB | 56.22 GB | — | Insufficient |
| IQ4_XS | 4.41 | Good | 55.78 GB | 0.75 GB | 57.33 GB | — | Insufficient |
| IQ4_NL | 4.64 | Good | 58.67 GB | 0.75 GB | 60.22 GB | — | Insufficient |
| Q4_0 | 4.64 | Good | 58.72 GB | 0.75 GB | 60.27 GB | — | Insufficient |
| Q4_K_M | 4.97 | Good | 62.91 GB | 0.75 GB | 64.46 GB | — | Insufficient |
| Q4_K_L | 5.03 | Very good | 63.62 GB | 0.75 GB | 65.17 GB | — | Insufficient |
| Q4_1 | 5.09 | Very good | 64.35 GB | 0.75 GB | 65.9 GB | — | Insufficient |
| Q5_K_L | 5.84 | Very good | 73.87 GB | 0.75 GB | 75.42 GB | — | Insufficient |
| Q6_K_L | 6.57 | Excellent | 83.13 GB | 0.75 GB | 84.68 GB | — | Insufficient |
| Q8_0 | 8.35 | Excellent | 105.61 GB | 0.75 GB | 107.16 GB | — | Insufficient |
| BF16 | 15.87 | Excellent | 200.76 GB | 0.75 GB | 202.31 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 bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF?
bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF is an instruction-tuned chat model with 108.64 billion parameters, based on the llama4 architecture. It is released under the other license and distributed as GGUF files for local inference.
How much VRAM do you need to run bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF?
You need about 29.64 GB of VRAM to run bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF entirely on the GPU using the IQ2_XXS quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF on an 8 GB GPU?
No. bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF does not fit on an 8 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF on a 16 GB GPU?
Partially. bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF only fits on a 16 GB GPU by offloading part of it to system RAM (with Q3_K_S), which runs but is slower.
Can I run bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF on a 24 GB GPU?
Partially. bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF only fits on a 24 GB GPU by offloading part of it to system RAM (with Q4_1), which runs but is slower.
What context length does bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF support?
bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF supports a native context length of up to 10,485,760 tokens. A longer context grows the KV cache, so it increases the memory needed to run the model.
What is the best quantization for bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF?
For bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF, a strong default is Q4_K_M, which needs about 64.46 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.