FP6-LLM

Efficiently serving large language models

CommonProductProgrammingLarge language modelsGPU inference

FP6-LLM is a new supporting solution for large language models. Through six-bit quantization (FP6), it effectively reduces the model size while maintaining model quality across various applications. We present TC-FPx, the first complete GPU kernel design that uniformly supports various quantization bit widths for floating-point weights. By integrating the TC-FPx kernel into existing inference systems, we provide a new end-to-end support for quantized LLM inference (called FP6-LLM), achieving a better balance between inference cost and model quality. Experiments demonstrate that FP6-LLM enables inference of LLaMA-70b using a single GPU, achieving normalized inference throughput 1.69x to 2.65x higher than the FP16 baseline.

AI News

AI Daily

AI Timeline

Al Hardware

Latest Cases

Image Collection

Video Collection

Audio Collection

Content Collection

Latest Tutorials

AI Product Ranking

AI Traffic Growth Ranking

AI Traffic Decline Ranking

AI Weekly Ranking

United States

China

India

Brazil

Image Generation

Personal Assistant

Character Generation

Video Generation

AI Project Ranking

AI Project Growth Ranking

AI Developer Ranking

AI Organization Ranking

Deepseek

TTS

LLM

ChatGPT

Overview

FP6-LLM

FP6-LLM Visit Over Time

FP6-LLM Visit Trend

FP6-LLM Visit Geography

FP6-LLM Traffic Sources

FP6-LLM Alternatives

FP6-LLM — Efficiently serving large language models

Models Table — A comprehensive list and information about large language models

Large World Models — Large World Models: Understanding Video and Language

BiTA — Bidirectional Adjustment for Large Language Models

MInference — Accelerate the inference process of long context large language models

BitNet — Inference framework for 1-bit large language models

Phi Open Models — Phi Open Models are powerful, cost-effective, low-latency small language models.

LLM Maybe LongLM — Extends the context window of large language models

Star-Attention — EfficientInference Technology for Long Sequence Large Language Models

Prompt Engineering Guide — A comprehensive guide to prompt engineering for large language models

Benchmarking API Performance of Large Language Models — In-depth analysis of key metrics like TTFT and TPS

Open LLM Leaderboard — A publicly accessible leaderboard of large language models.

Brainglue — Brainglue is an interesting experimental platform for large language models

OpenAI Embedding Models — New generation embedding models with improved performance and lower prices.

parsera — A lightweight Python library for web scraping using large language models.

xLAM — Research on intelligent agents based on large language models

AutoDAN-Turbo — An automated framework for breaking the limitations of large language models

DCLM — Comprehensive framework for building and training large language models

Zhipu AI Large Model Open Platform — Integrate large models with just a few lines of code.

CuMo — An advanced architecture for extending multimodal large language models (LLMs).

VSP-LLM — A framework that combines Visual Speech Processing with Large Language Models

RoleLLM — Role-playing framework for large language models

EAGLE — Exploration of the design space for multimodal large language models

Entry Point AI — A platform for training customized large language models

Buffer of Thoughts — Improves the accuracy and efficiency of large language models in reasoning

SmolLM — Efficient Small Language Models

prism-alignment — Explore the preferences and value alignment of large language models.

Supervised app — A no-code platform for building supervised large language models.

MM1.5 — Optimization and analysis of multimodal large language models

KnowEdit — A knowledge editing benchmark for evaluating the knowledge editing capabilities of large language models.