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Field:	Response:
Participation considerations from adversely impacted groups (protected classes) in model design and testing:	None
Measures taken to mitigate against unwanted bias:	None

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Nemotron-4-340B-Base

$\"Model$ $\"Model$ $\"Language\"$

Model Overview:

Nemotron-4-340B-Base is a large language model (LLM) that can be used as part of a synthetic data generation pipeline to create training data that helps researchers and developers build their own LLMs. This model has 340 billion parameters, and supports a context length of 4,096 tokens. It is pre-trained for a total of 9 trillion tokens, consisting of a diverse assortment of English-based texts, 50+ natural languages and 40+ coding languages. After an initial pre-training phase of 8 trillion tokens, continuous pre-training of 1 trillion tokens was performed on top of the pre-trained model in order to improve model quality. During continuous pre-training, we altered the data composition by using a different distribution than the one seen at the beginning of training.

Under the NVIDIA Open Model License, NVIDIA confirms:\nModels are commercially usable.\nYou are free to create and distribute Derivative Models.\nNVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models

License:

NVIDIA Open Model License

Intended use

Nemotron-4-340B-Base is a completion model intended for use in over 50+ natural and 40+ coding languages. It is compatible with NVIDIA NeMo Framework. For best performance on a given task, users are encouraged to customize the model using the NeMo Framework suite of customization tools including Parameter-Efficient Fine-Tuning (P-tuning, Adapters, LoRA, and more), and Model Alignment (SFT, SteerLM, RLHF, and more) using NeMo-Aligner.

Model Developer: NVIDIA

Model Dates: Nemotron-4-340B-Base was trained between December 2023 and May 2024.

Required Hardware

BF16 Inference:

8x H200 (1x H200 node)
16x H100 (2x H100 nodes)
16x A100 80GB (2x A100 80GB nodes)

Model Architecture:

Nemotron-4-340B-Base is trained with a global batch-size of 2304, a sequence length of 4096 tokens, uses Grouped-Query Attention (GQA), and Rotary Position Embeddings (RoPE).

Architecture Type: Transformer Decoder (auto-regressive language model)

Network Architecture:\nNemotron-4

Usage

First unpack the .nemo file with:

mkdir Nemotron-4-340B-Base && cd Nemotron-4-340B-Base && tar xf /path/to/nemotron-4-340b-base-v1.nemo\n

Deployment and inference with Nemotron-4-340B-Base can be done in three steps using NeMo Framework:

Create a Python script to interact with the deployed model.\nCreate a Bash script to start the inference server.\nSchedule a Slurm job to distribute the model across 2 nodes and associate them with the inference server.

Define the Python script call_server.py

", "", "\\x11", "User"],\n }\n sentences = text_generation(data, port=1424)['sentences']\n return sentences[0] if not batch else sentences\n\nPROMPT_TEMPLATE = "{prompt}"\n\nquestion = "Write a poem on NVIDIA in the style of Shakespeare"\nprompt = PROMPT_TEMPLATE.format(prompt=question)\nprint(prompt)\n\nresponse = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)\nresponse = response[len(prompt):]\nprint(response)\n\">

import requests\nimport json\n\nheaders = {\"Content-Type\": \"application/json\"}\n\ndef text_generation(data, ip='localhost', port=None):\n    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)\n    return resp.json()\n\n\ndef get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):\n    data = {\n        \"sentences\": [prompt] if not batch else prompt,\n        \"tokens_to_generate\": int(token_to_gen),\n        \"temperature\": temp,\n        \"add_BOS\": add_BOS,\n        \"top_k\": top_k,\n        \"top_p\": top_p,\n        \"greedy\": greedy,\n        \"all_probs\": False,\n        \"repetition_penalty\": repetition,\n        \"min_tokens_to_generate\": int(min_tokens),\n        \"end_strings\": [\"<|endoftext|>\", \"<extra_id_1>\", \"\\x11\", \"<extra_id_1>User\"],\n    }\n    sentences = text_generation(data, port=1424)['sentences']\n    return sentences[0] if not batch else sentences\n\nPROMPT_TEMPLATE = \"{prompt}\"\n\nquestion = \"Write a poem on NVIDIA in the style of Shakespeare\"\nprompt = PROMPT_TEMPLATE.format(prompt=question)\nprint(prompt)\n\nresponse = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)\nresponse = response[len(prompt):]\nprint(response)\n

Given this Python script, create a Bash script which spins up the inference server within the NeMo container (docker pull nvcr.io/nvidia/nemo:24.01.framework) and calls the Python script call_server.py. The Bash script nemo_inference.sh is as follows:

/dev/null 2>/dev/null; echo $?)\n while [ $STATUS -ne 0 ]\n do\n echo "waiting for server ($HOST:$PORT) to be up"\n sleep 10\n STATUS=$(curl -X PUT http://$HOST:$PORT >/dev/null 2>/dev/null; echo $?)\n done\n echo "server ($HOST:$PORT) is up running"\n}\n\n\n\n/usr/bin/python3 /opt/NeMo/examples/nlp/language_modeling/megatron_gpt_eval.py \\\n gpt_model_file=$NEMO_FILE \\\n pipeline_model_parallel_split_rank=0 \\\n server=True tensor_model_parallel_size=8 \\\n trainer.precision=bf16 pipeline_model_parallel_size=2 \\\n trainer.devices=8 \\\n trainer.num_nodes=2 \\\n web_server=False \\\n port=${WEB_PORT} &\n SERVER_PID=$!\n\n readonly local_rank="${LOCAL_RANK:=${SLURM_LOCALID:=${OMPI_COMM_WORLD_LOCAL_RANK:-}}}"\n if [ $SLURM_NODEID -eq 0 ] && [ $local_rank -eq 0 ]; then\n depends_on "0.0.0.0" ${WEB_PORT}\n\n echo "start get json"\n sleep 5\n\n echo "SLURM_NODEID: $SLURM_NODEID"\n echo "local_rank: $local_rank"\n /usr/bin/python3 /scripts/call_server.py\n echo "clean up dameons: $$"\n kill -9 $SERVER_PID\n pkill python\n fi\n wait\n\">

NEMO_FILE=$1\nWEB_PORT=1424\n\ndepends_on () {\n    HOST=$1\n    PORT=$2\n    STATUS=$(curl -X PUT http://$HOST:$PORT >/dev/null 2>/dev/null; echo $?)\n    while [ $STATUS -ne 0 ]\n    do\n         echo \"waiting for server ($HOST:$PORT) to be up\"\n         sleep 10\n         STATUS=$(curl -X PUT http://$HOST:$PORT >/dev/null 2>/dev/null; echo $?)\n    done\n    echo \"server ($HOST:$PORT) is up running\"\n}\n\n\n\n/usr/bin/python3 /opt/NeMo/examples/nlp/language_modeling/megatron_gpt_eval.py \\\n        gpt_model_file=$NEMO_FILE \\\n        pipeline_model_parallel_split_rank=0 \\\n        server=True tensor_model_parallel_size=8 \\\n        trainer.precision=bf16 pipeline_model_parallel_size=2 \\\n        trainer.devices=8 \\\n        trainer.num_nodes=2 \\\n        web_server=False \\\n        port=${WEB_PORT} &\n    SERVER_PID=$!\n\n    readonly local_rank=\"${LOCAL_RANK:=${SLURM_LOCALID:=${OMPI_COMM_WORLD_LOCAL_RANK:-}}}\"\n    if [ $SLURM_NODEID -eq 0 ] && [ $local_rank -eq 0 ]; then\n        depends_on \"0.0.0.0\" ${WEB_PORT}\n\n        echo \"start get json\"\n        sleep 5\n\n        echo \"SLURM_NODEID: $SLURM_NODEID\"\n        echo \"local_rank: $local_rank\"\n        /usr/bin/python3 /scripts/call_server.py\n        echo \"clean up dameons: $$\"\n        kill -9 $SERVER_PID\n        pkill python\n    fi\n    wait\n

Launch nemo_inference.sh with a Slurm script defined like below, which starts a 2-node job for model inference.

\nOUTFILE="${RESULTS}/slurm-%j-%n.out"\nERRFILE="${RESULTS}/error-%j-%n.out"\nMODEL=/Nemotron-4-340B-Base\n\nMOUNTS="--container-mounts=:/scripts,MODEL:/model"\nread -r -d '' cmd <

#!/bin/bash\n#SBATCH -A SLURM-ACCOUNT\n#SBATCH -p SLURM-PARITION\n#SBATCH -N 2\n#SBATCH -J generation      \n#SBATCH --ntasks-per-node=8   \n#SBATCH --gpus-per-node=8\nset -x\n\nRESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>\nOUTFILE=\"${RESULTS}/slurm-%j-%n.out\"\nERRFILE=\"${RESULTS}/error-%j-%n.out\"\nMODEL=<PATH_TO>/Nemotron-4-340B-Base\n\nMOUNTS=\"--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model\"\nread -r -d '' cmd <<EOF\nbash /scripts/nemo_inference.sh /model\nEOF\n\nsrun -o $OUTFILE -e $ERRFILE --container-image=\"$CONTAINER\" $MOUNTS bash -c \"${cmd}\"\n

Dataset & Training

The training corpus for Nemotron-4-340B-Base consists of English and multilingual text, as well as code. Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including legal, math, science, finance, and more. In our continued training set, we introduce a small portion of question-answering, and alignment style data to improve model performance.

Data Freshness: The pretraining data has a cutoff of June 2023.

Evaluation Results

Overview

5-shot performance. Language Understanding evaluated using Massive Multitask Language Understanding:

\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Average
81.1

Zero-shot performance. Evaluated using select datasets from the LM Evaluation Harness with additions:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

HellaSwag	Winogrande	BBH	ARC-Challenge
90.53	89.50	85.44	94.28

Chain of Thought (CoT). Multilingual capabilities evaluated using Multilingual Grade School Math:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

ES Exact Match (%)	JA Exact Match (%)	TH Exact Match (%)
68.8	69.6	68.4

Code generation performance. Evaluated using HumanEval:

\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

p@1, 0-Shot
57.3

Limitations

The model was trained on data that contains toxic language, unsafe content, and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards here.. Please report security vulnerabilities or NVIDIA AI Concerns here.

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It is pre-trained for a total of 9 trillion tokens, consisting of a diverse assortment of English-based texts, 50+ natural languages and 40+ coding languages. After an initial pre-training phase of 8 trillion tokens, continuous pre-training of 1 trillion tokens was performed on top of the pre-trained model in order to improve model quality. During continuous pre-training, we altered the data composition by using a different distribution than the one seen at the beginning of training.\n\nUnder the NVIDIA Open Model License, NVIDIA confirms: \nModels are commercially usable. \nYou are free to create and distribute Derivative Models. \nNVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models\n\n\n### License: \n\n[NVIDIA Open Model License](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf)\n\n### Intended use\n\nNemotron-4-340B-Base is a completion model intended for use in over 50+ natural and 40+ coding languages. It is compatible with [NVIDIA NeMo Framework](https://docs.nvidia.com/nemo-framework/index.html). For best performance on a given task, users are encouraged to customize the model using the NeMo Framework suite of customization tools including Parameter-Efficient Fine-Tuning (P-tuning, Adapters, LoRA, and more), and Model Alignment (SFT, SteerLM, RLHF, and more) using [NeMo-Aligner](https://github.com/NVIDIA/NeMo-Aligner).\n\n**Model Developer:** NVIDIA\n\n**Model Dates:** Nemotron-4-340B-Base was trained between December 2023 and May 2024.\n\n### Required Hardware\n\nBF16 Inference:\n- 8x H200 (1x H200 node)\n- 16x H100 (2x H100 nodes)\n- 16x A100 80GB (2x A100 80GB nodes)\n\n### Model Architecture:\n\nNemotron-4-340B-Base is trained with a global batch-size of 2304, a sequence length of 4096 tokens, uses Grouped-Query Attention (GQA), and Rotary Position Embeddings (RoPE).\n\n**Architecture Type:** Transformer Decoder (auto-regressive language model)\n\n**Network Architecture:**\nNemotron-4\n\n### Usage\n\nFirst unpack the .nemo file with:\n```\nmkdir Nemotron-4-340B-Base && cd Nemotron-4-340B-Base && tar xf /path/to/nemotron-4-340b-base-v1.nemo\n```\n\nDeployment and inference with Nemotron-4-340B-Base can be done in three steps using NeMo Framework:\n\nCreate a Python script to interact with the deployed model.\nCreate a Bash script to start the inference server.\nSchedule a Slurm job to distribute the model across 2 nodes and associate them with the inference server.\n\n1. Define the Python script ``call_server.py``\n\n```python\nimport requests\nimport json\n\nheaders = {\"Content-Type\": \"application/json\"}\n\ndef text_generation(data, ip='localhost', port=None):\n resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)\n return resp.json()\n\n\ndef get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):\n data = {\n \"sentences\": [prompt] if not batch else prompt,\n \"tokens_to_generate\": int(token_to_gen),\n \"temperature\": temp,\n \"add_BOS\": add_BOS,\n \"top_k\": top_k,\n \"top_p\": top_p,\n \"greedy\": greedy,\n \"all_probs\": False,\n \"repetition_penalty\": repetition,\n \"min_tokens_to_generate\": int(min_tokens),\n \"end_strings\": [\"<|endoftext|>\", \"\", \"\\x11\", \"User\"],\n }\n sentences = text_generation(data, port=1424)['sentences']\n return sentences[0] if not batch else sentences\n\nPROMPT_TEMPLATE = \"{prompt}\"\n\nquestion = \"Write a poem on NVIDIA in the style of Shakespeare\"\nprompt = PROMPT_TEMPLATE.format(prompt=question)\nprint(prompt)\n\nresponse = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)\nresponse = response[len(prompt):]\nprint(response)\n```\n\n\n2. Given this Python script, create a Bash script which spins up the inference server within the [NeMo container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo) (docker pull nvcr.io/nvidia/nemo:24.01.framework) and calls the Python script ``call_server.py``. The Bash script ``nemo_inference.sh`` is as follows:\n\n```bash\nNEMO_FILE=$1\nWEB_PORT=1424\n\ndepends_on () {\n HOST=$1\n PORT=$2\n STATUS=$(curl -X PUT http://$HOST:$PORT >/dev/null 2>/dev/null; echo $?)\n while [ $STATUS -ne 0 ]\n do\n echo \"waiting for server ($HOST:$PORT) to be up\"\n sleep 10\n STATUS=$(curl -X PUT http://$HOST:$PORT >/dev/null 2>/dev/null; echo $?)\n done\n echo \"server ($HOST:$PORT) is up running\"\n}\n\n\n\n/usr/bin/python3 /opt/NeMo/examples/nlp/language_modeling/megatron_gpt_eval.py \\\n gpt_model_file=$NEMO_FILE \\\n pipeline_model_parallel_split_rank=0 \\\n server=True tensor_model_parallel_size=8 \\\n trainer.precision=bf16 pipeline_model_parallel_size=2 \\\n trainer.devices=8 \\\n trainer.num_nodes=2 \\\n web_server=False \\\n port=${WEB_PORT} &\n SERVER_PID=$!\n\n readonly local_rank=\"${LOCAL_RANK:=${SLURM_LOCALID:=${OMPI_COMM_WORLD_LOCAL_RANK:-}}}\"\n if [ $SLURM_NODEID -eq 0 ] && [ $local_rank -eq 0 ]; then\n depends_on \"0.0.0.0\" ${WEB_PORT}\n\n echo \"start get json\"\n sleep 5\n\n echo \"SLURM_NODEID: $SLURM_NODEID\"\n echo \"local_rank: $local_rank\"\n /usr/bin/python3 /scripts/call_server.py\n echo \"clean up dameons: $$\"\n kill -9 $SERVER_PID\n pkill python\n fi\n wait\n```\n\n\n3. Launch ``nemo_inference.sh`` with a Slurm script defined like below, which starts a 2-node job for model inference.\n\n```bash\n#!/bin/bash\n#SBATCH -A SLURM-ACCOUNT\n#SBATCH -p SLURM-PARITION\n#SBATCH -N 2\n#SBATCH -J generation \n#SBATCH --ntasks-per-node=8 \n#SBATCH --gpus-per-node=8\nset -x\n\nRESULTS=\nOUTFILE=\"${RESULTS}/slurm-%j-%n.out\"\nERRFILE=\"${RESULTS}/error-%j-%n.out\"\nMODEL=/Nemotron-4-340B-Base\n\nMOUNTS=\"--container-mounts=:/scripts,MODEL:/model\"\nread -r -d '' cmd <
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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1191":1192,"_1193":1194,"_1195":1196,"_1197":1198},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[301,302,303,304,305],{"_306":843,"_142":844,"_110":845,"_310":107,"_311":121,"_313":846,"_315":847},{"_306":633,"_142":634,"_110":635,"_310":20,"_311":280,"_313":636,"_315":637},{"_306":533,"_142":534,"_110":535,"_310":20,"_311":47,"_313":536,"_315":537},{"_306":317,"_142":318,"_110":319,"_310":20,"_311":320,"_313":321,"_315":322},{"_306":307,"_142":308,"_110":309,"_310":20,"_311":312,"_313":314,"_315":316},"optanonGroupId","C0005","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485],[323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339],{"_340":447,"_155":447,"_110":342,"_313":448},{"_340":442,"_155":442,"_110":342,"_313":443},{"_340":438,"_155":438,"_110":342,"_313":439},{"_340":433,"_155":433,"_110":342,"_313":434},{"_340":428,"_155":428,"_110":342,"_313":429},{"_340":416,"_155":416,"_110":342,"_313":417},{"_340":404,"_155":404,"_110":342,"_313":405},{"_340":399,"_155":399,"_110":342,"_313":400},{"_340":388,"_155":388,"_110":342,"_313":389},{"_340":383,"_155":383,"_110":342,"_313":384},{"_340":378,"_155":378,"_110":342,"_313":379},{"_340":373,"_155":373,"_110":342,"_313":374},{"_340":368,"_155":368,"_110":342,"_313":369},{"_340":363,"_155":363,"_110":342,"_313":364},{"_340":358,"_155":358,"_110":342,"_313":359},{"_340":351,"_155":351,"_110":342,"_313":352},{"_340":341,"_155":341,"_110":342,"_313":343},"hostName","amazon-adsystem.com","",[344,345],{"_142":350,"_347":341,"_348":20,"_110":349},{"_142":346,"_347":341,"_348":20,"_110":349},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[353,354],{"_142":357,"_347":351,"_348":20,"_110":356},{"_142":355,"_347":351,"_348":20,"_110":356},"IDE","This domain is owned by Doubleclick (Google). 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593],[538,539,540,541,542],{"_340":433,"_155":433,"_110":342,"_313":564},{"_340":399,"_155":399,"_110":342,"_313":559},{"_340":552,"_155":552,"_110":342,"_313":553},{"_340":548,"_155":548,"_110":342,"_313":549},{"_340":543,"_155":543,"_110":342,"_313":544},"nvcrm.my.site.com",[545],{"_142":546,"_347":543,"_348":20,"_110":547},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[550],{"_142":551,"_347":548,"_348":20,"_110":382},"edge","github.com",[554,555],{"_142":558,"_347":552,"_348":20,"_110":557},{"_142":556,"_347":552,"_348":20,"_110":557},"logged_in","This domain is owned by Github. 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The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. 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