🚀 TensorRT-LLM foundation images with CUDA & PyTorch for AI model compilation & inference
777
High-performance Docker ecosystem for TensorRT-LLM development and deployment, built on NVIDIA CUDA 12.4 with Ubuntu 22.04. Optimized for GPU-accelerated AI model inference and compilation workflows.
baseRuntime Environment for Production Inference
Core Stack:
Pre-installed Libraries:
Best for: Production inference, model serving, runtime deployment
devComplete Compilation Environment
Build Environment:
/workspace with TensorRT-LLM v0.21 sourceAdvanced Features:
Best for: Model compilation, custom engine building, development workflows
latest → base # Points to most commonly used image
Base Image:
andspace/trt-llm:1.0-base-12.4-0.21 # Full version
andspace/trt-llm:1.0-base # Short version
andspace/trt-llm:base # Type tag
andspace/trt-llm:latest # Latest (→ base)
Development Image:
andspace/trt-llm:1.0-dev-12.4-0.21 # Full version
andspace/trt-llm:1.0-dev # Short version
andspace/trt-llm:dev # Type tag
{release}-{type}-{cuda}-{tensorrt}
│ │ │ └─ TensorRT-LLM version (major.minor)
│ │ └────────── CUDA version (major.minor)
│ └────────────────── Image type (base/dev)
└────────────────────────── Release version (1.0)
# Runtime inference (recommended)
docker pull andspace/trt-llm:latest
# Development & compilation
docker pull andspace/trt-llm:dev
# Version pinning
docker pull andspace/trt-llm:1.0-base-12.4-0.21
| Component | Requirement |
|---|---|
| GPU | NVIDIA GPU with CUDA Compute Capability 8.6+ |
| Docker | Docker with NVIDIA Container Toolkit |
| Memory | 8GB+ GPU memory recommended |
| CUDA | Compatible with CUDA 12.4 |
Content type
Image
Digest
sha256:057cce48c…
Size
16 GB
Last updated
about 1 year ago
docker pull andspace/trt-llm