We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Users predominantly praise TGI for its community-driven development and ability to facilitate access to numerous machine learning models, fostering a barrier-free environment for experimentation. Key complaints are scarce, showcasing a generally positive reception. The sentiment around pricing isn't explicitly mentioned, but the emphasis on open-source contributions suggests a cost-effective approach. Overall, TGI enjoys a robust reputation as a pivotal component in the machine learning ecosystem, celebrated for its innovation and community engagement.
Mentions (30d)
0
Reviews
0
Platforms
3
Sentiment
5%
6 positive
Users predominantly praise TGI for its community-driven development and ability to facilitate access to numerous machine learning models, fostering a barrier-free environment for experimentation. Key complaints are scarce, showcasing a generally positive reception. The sentiment around pricing isn't explicitly mentioned, but the emphasis on open-source contributions suggests a cost-effective approach. Overall, TGI enjoys a robust reputation as a pivotal component in the machine learning ecosystem, celebrated for its innovation and community engagement.
Features
Use Cases
Industry
information technology & services
Employees
730
Funding Stage
Series D
Total Funding
$395.7M
20
npm packages
40
HuggingFace models
Welcome to @OpenAI on @huggingface! https://t.co/HFjGP6RtjU
Welcome to @OpenAI on @huggingface! https://t.co/HFjGP6RtjU
View originall9gpu - open-source GPU observability with workload-level attribution [P]
GPU monitoring tools like DCGM give you hardware-level metrics but no workload context. When a node is saturated, you can't tell which experiment, team, or job is responsible without digging through logs. We built l9gpu to close that gap. It's a node-level agent that exports GPU metrics via OTLP with workload attribution embedded: - Kubernetes: correlates GPU metrics with pod, namespace, and deployment - Slurm: correlates with job ID, user, and partition - LLM inference: native metrics for vLLM, SGLang, and TGI - Hardware: NVIDIA, AMD MI300X, Intel Gaudi - 17 pre-built Prometheus alert rules + Grafana dashboards Derived from Meta's gcm project, extended with K8s attribution, multi-vendor GPU support, and OTLP export. MIT licensed. https://github.com/last9/gpu-telemetry Happy to discuss design decisions around the attribution mapping. What is the ML infra community using for GPU cost visibility in shared research clusters? submitted by /u/bakibab [link] [comments]
View original@thorwebdev @pollenrobotics @ailozovskaya @andimarafioti 💎🤗🤖
@thorwebdev @pollenrobotics @ailozovskaya @andimarafioti 💎🤗🤖
View originalllama-server -hf ggml-org/gemma-4-26b-a4b-it-GGUF:Q4_K_M openclaw onboard --non-interactive \ --auth-choice custom-api-key \ --custom-base-url "http://127.0.0.1:8080/v1" \ --custom-model-id "gg
llama-server -hf ggml-org/gemma-4-26b-a4b-it-GGUF:Q4_K_M openclaw onboard --non-interactive \ --auth-choice custom-api-key \ --custom-base-url "http://127.0.0.1:8080/v1" \ --custom-model-id "ggml-org-gemma-4-26b-a4b-gguf" \ --custom-api-key "llama.cpp" \ --secret-input-mode plaintext \ --custom-compatibility openai \ --accept-risk
View original@LottoLabs https://t.co/h2frA6iR2I
@LottoLabs https://t.co/h2frA6iR2I
View originalLet's go! https://t.co/HakmkNzDT2
Let's go! https://t.co/HakmkNzDT2
View originalModel weights are here: https://t.co/rQlfP51Db7!
Model weights are here: https://t.co/rQlfP51Db7!
View originaldo the right thing anon!
do the right thing anon!
View originalhttps://t.co/QLPgege4CI
https://t.co/QLPgege4CI
View originalSeeing the worldwide demand we are kicking off global applications for Hugging Face Builders! If you're passionate about open AI and love bringing people together, this is your invitation to lead ✉️
Seeing the worldwide demand we are kicking off global applications for Hugging Face Builders! If you're passionate about open AI and love bringing people together, this is your invitation to lead ✉️ Learn more about the program and apply to become a Builder ➡️ https://t.co/MR0fmruSDi
View originalWe are sponsoring Gemini hackathon with Cerebral Valley, see you this weekend!
We are sponsoring Gemini hackathon with Cerebral Valley, see you this weekend!
View originalLearn more and apply from the link below🤗 https://t.co/QLPgege4CI
Learn more and apply from the link below🤗 https://t.co/QLPgege4CI
View originalHugging Face Builders is a global community program that puts local leaders at the center of the open-source AI movement 🤗 If you're passionate about open AI and love bringing people together, this
Hugging Face Builders is a global community program that puts local leaders at the center of the open-source AI movement 🤗 If you're passionate about open AI and love bringing people together, this is your invitation to lead ✉️ Apply for to build the Paris chapter today ➡️ https://t.co/ONVBZdxRdc
View originalRead our blog to learn more 🤗 https://t.co/asj0iZulGe
Read our blog to learn more 🤗 https://t.co/asj0iZulGe
View original🪣 We just shipped Storage Buckets: S3-like mutable storage, cheaper & faster Git falls short for everything on high-throughput side of AI (checkpoints, processed data, agent traces, logs etc) Buc
🪣 We just shipped Storage Buckets: S3-like mutable storage, cheaper & faster Git falls short for everything on high-throughput side of AI (checkpoints, processed data, agent traces, logs etc) Buckets fixes that: fast writes, overwrites, directory sync 💨 All powered by Xet dedup so successive checkpoints skip the bytes that already exist ➡️
View originalRepository Audit Available
Deep analysis of huggingface/text-generation-inference — architecture, costs, security, dependencies & more
TGI uses a tiered pricing model. Visit their website for current pricing details.
Key features include: Simple launcher to serve most popular LLMs, Production ready (distributed tracing with Open Telemetry, Prometheus metrics), Tensor Parallelism for faster inference on multiple GPUs, Token streaming using Server-Sent Events (SSE), Continuous batching of incoming requests for increased total throughput, Logits warper (temperature scaling, top-p, top-k, repetition penalty), Stop sequences, Log probabilities.
TGI is commonly used for: Generating creative writing prompts for authors, Building conversational agents for customer support, Creating personalized content recommendations for users, Automating report generation in business intelligence tools, Enhancing educational tools with interactive learning content, Developing chatbots for social media engagement.
TGI integrates with: Hugging Face Transformers, TensorFlow, PyTorch, Kubernetes for container orchestration, Docker for containerization, OpenTelemetry for distributed tracing, Prometheus for monitoring metrics, FastAPI for building APIs, Streamlit for creating interactive web apps, Flask for lightweight web applications.
Julien Chaumond
CTO at Hugging Face
1 mention
Based on user reviews and social mentions, the most common pain points are: cost visibility, breaking.
Based on 114 social mentions analyzed, 5% of sentiment is positive, 95% neutral, and 0% negative.
