What are the hardware requirements for Falcon 40B Instruct GPTQ Inference Endpoints?

Falcon 40B Instruct GPTQ Inference Endpoints requires approximately 22.5 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Falcon 40B Instruct GPTQ Inference Endpoints and how large is it?

Falcon 40B Instruct GPTQ Inference Endpoints is developed by philschmid, 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 GPTQ Inference Endpoints?

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

Falcon 40B Instruct GPTQ Inference Endpoints by philschmid — VRAM 22.5GB

Name: Falcon 40B Instruct GPTQ Inference Endpoints
Author: philschmid

Falcon 40B Instruct GPTQ Inference Endpoints is an open-source language model by philschmid. Features: 40b LLM, VRAM: 22.5GB, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.07.

Arxiv:1911.02150 Arxiv:2005.14165 Arxiv:2104.09864 Arxiv:2205.14135 4bit Custom code Dataset:tiiuae/falcon-refinedw... En Endpoints compatible Gptq Instruct Quantized Refinedweb Region:us

Model Card on HF 🤗: https://huggingface.co/philschmid/falcon-40b-instruct-GPTQ-inference-endpoints

Falcon 40B Instruct GPTQ Inference Endpoints Parameters and Internals

Model Type

Causal decoder-only

Use Cases

Areas:

Chatbots, Instruction-based tasks

Primary Use Cases:

Chat datasets

Limitations:

Limited generalization to non-English languages

Considerations:

Guardrails recommended for production use

Additional Notes

Experimental GPTQ model; support is limited.

Supported Languages

English (primarily trained), French (also supported)

Training Details

Data Sources:

Baize, RefinedWeb

Data Volume:

150M tokens

Methodology:

finetuned

Context Length:

2048

Hardware Used:

64 A100 40GB GPUs

Model Architecture:

Adapted from GPT-3, causal decoder-only with rotary positional embeddings and FlashAttention

Input Output

Accepted Modalities:

text

Performance Tips:

Expected to be slow with around 0.7 tokens/s

LLM Name	Falcon 40B Instruct GPTQ Inference Endpoints
Repository 🤗	https://huggingface.co/philschmid/falcon-40b-instruct-GPTQ-inference-endpoints
Model Size	40b
Required VRAM	22.5 GB
Updated	2026-06-17
Maintainer	philschmid
Model Type	RefinedWeb
Instruction-Based	Yes
Model Files	22.5 GB
Supported Languages	en
GPTQ Quantization	Yes
Quantization Type	gptq\|4bit
Model Architecture	RWForCausalLM
License	apache-2.0
Model Max Length	2048
Transformers Version	4.29.2
Is Biased	0
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	65024
Torch Data Type	bfloat16

Rank the Falcon 40B Instruct GPTQ Inference Endpoints capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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

Best Alternatives to Falcon 40B Instruct GPTQ Inference Endpoints

Best Alternatives	Context / RAM	Downloads	Likes
Falcon 40B Instruct GPTQ	0K / 22.5 GB	152	197
Falcon 40B Instruct GPTQ	0K / 22.5 GB	11	1
Falcon 40B Instruct 8bit	0K / 41.8 GB	12	6
...alcon 40B Instruct W4 G128 AWQ	0K / 22.3 GB	15	2
H2ogpt Oig Oasst1 Falcon 40B	0K / 82.5 GB	9	6

Note: green Score (e.g. "73.2") means that the model is better than philschmid/falcon-40b-instruct-GPTQ-inference-endpoints.