What are the hardware requirements for Falcon 40B Instruct GGML?

Falcon 40B Instruct GGML requires approximately 13.7 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Falcon 40B Instruct GGML and how large is it?

Falcon 40B Instruct GGML is developed by TheBloke, a model with 40b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Falcon 40B Instruct GGML?

Falcon 40B Instruct GGML is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Falcon 40B Instruct GGML by TheBloke — VRAM 13.7GB

Name: Falcon 40B Instruct GGML
Author: TheBloke

Falcon 40B Instruct GGML is an open-source language model by TheBloke. Features: 40b LLM, VRAM: 13.7GB, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.07.

Arxiv:1911.02150 Arxiv:2005.14165 Arxiv:2104.09864 Arxiv:2205.14135 Arxiv:2304.01196 Arxiv:2306.01116 Dataset:tiiuae/falcon-refinedw... En Falcon Ggml Instruct Quantized Region:us

Model Card on HF 🤗: https://huggingface.co/TheBloke/falcon-40b-instruct-GGML

Falcon 40B Instruct GGML Parameters and Internals

Model Type

Causal decoder-only, Instruct model

Use Cases

Areas:

Research, Commercial applications

Applications:

Text generation, Chatbot systems

Primary Use Cases:

Ready-to-use chat model, Instruction generation

Limitations:

Mostly trained on English data, May carry stereotypes and biases from web data

Considerations:

Develop guardrails for production use.

Additional Notes

This is a specialized instruct model. For fine-tuning, consider starting from the base Falcon-40B model.

Supported Languages

English (High), French (Moderate)

Training Details

Data Sources:

Baize, RefinedWeb

Data Volume:

150M tokens

Methodology:

Finetuned on chat dataset with 5% RefinedWeb data

Context Length:

2048

Hardware Used:

64 A100 40GB GPUs in P4d instances

Model Architecture:

Causal decoder-only with optimized architecture

Input Output

Input Format:

Natural language prompts

Accepted Modalities:

text

Output Format:

Textual responses

Performance Tips:

Ensure sufficient VRAM and optimize inference settings accordingly.

Release Notes

Notes:

Includes various quantized model files for different inference needs.

LLM Name	Falcon 40B Instruct GGML
Repository 🤗	https://huggingface.co/TheBloke/falcon-40b-instruct-GGML
Base Model(s)	Medfalcon 40B Lora nmitchko/medfalcon-40b-lora
Model Size	40b
Required VRAM	13.7 GB
Updated	2025-09-23
Maintainer	TheBloke
Model Type	falcon
Instruction-Based	Yes
Model Files	13.7 GB 18.0 GB 23.5 GB 26.2 GB 23.5 GB 28.8 GB 31.4 GB 28.8 GB 34.3 GB 44.5 GB
Supported Languages	en
GGML Quantization	Yes
Quantization Type	ggml
Model Architecture	AutoModel
License	apache-2.0

Rank the Falcon 40B Instruct GGML capabilities

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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