118 lines
5.1 KiB
Markdown
118 lines
5.1 KiB
Markdown
---
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title: Jan now supports TensorRT-LLM
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description: Jan has added for Nvidia's TensorRT-LLM, a hardware-optimized LLM inference engine that runs very fast on Nvidia GPUs
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tags: [Nvidia, TensorRT-LLM]
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keywords: [Nvidia, TensorRT-LLM]
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---
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Jan now supports [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) as an alternative inference engine. TensorRT-LLM is a hardware-optimized LLM inference engine that compiles models to [run extremely fast on Nvidia GPUs](https://blogs.nvidia.com/blog/tensorrt-llm-windows-stable-diffusion-rtx/).
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- [TensorRT-LLM Extension](/guides/providers/tensorrt-llm) is available in [0.4.9 release](https://github.com/janhq/jan/releases/tag/v0.4.9)
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- Currently available only for Windows
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We've made a few TensorRT-LLM models TensorRT-LLM models available in the Jan Hub for download:
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- TinyLlama-1.1b
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- Mistral 7b
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- TinyJensen-1.1b, which is trained on Jensen Huang's 👀
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## What is TensorRT-LLM?
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Please read our [TensorRT-LLM Guide](/guides/providers/tensorrt-llm).
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TensorRT-LLM is mainly used in datacenter-grade GPUs to achieve [10,000 tokens/s](https://nvidia.github.io/TensorRT-LLM/blogs/H100vsA100.html) type speeds.
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## Performance Benchmarks
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We were curious to see how this would perform on consumer-grade GPUs, as most of Jan's users use consumer-grade GPUs.
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- We’ve done a comparison of how TensorRT-LLM does vs. llama.cpp, our default inference engine.
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| NVIDIA GPU | Architecture | VRAM Used (GB) | CUDA Cores | Tensor Cores | Memory Bus Width (bit) | Memory Bandwidth (GB/s) |
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| ---------- | ------------ | -------------- | ---------- | ------------ | ---------------------- | ----------------------- |
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| RTX 4090 | Ada | 24 | 16,384 | 512 | 384 | ~1000 |
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| RTX 3090 | Ampere | 24 | 10,496 | 328 | 384 | 935.8 |
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| RTX 4060 | Ada | 8 | 3,072 | 96 | 128 | 272 |
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> We test using batch_size 1 and input length 2048, output length 512 as it’s the common use case people all use. We run 5 times and get the Average.
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> We use Windows task manager and Linux NVIDIA-SMI/ Htop to get CPU/ Memory/ NVIDIA GPU metrics per process.
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> We turn off all user application and only open Jan app with Nitro tensorrt-llm or NVIDIA benchmark script in python
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### RTX 4090 on Windows PC
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- CPU: Intel 13th series
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- GPU: NVIDIA GPU 4090 (Ampere - sm 86)
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- RAM: 120GB
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- OS: Windows
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#### TinyLlama-1.1b q4
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| Metrics | GGUF (using the GPU) | TensorRT-LLM |
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| -------------------- | -------------------- | ------------ |
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| Throughput (token/s) | 104 | ✅ 131 |
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| VRAM Used (GB) | 2.1 | 😱 21.5 |
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| RAM Used (GB) | 0.3 | 😱 15 |
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| Disk Size (GB) | 4.07 | 4.07 |
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#### Mistral-7b int4
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| Metrics | GGUF (using the GPU) | TensorRT-LLM |
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| -------------------- | -------------------- | ------------ |
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| Throughput (token/s) | 80 | ✅ 97.9 |
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| VRAM Used (GB) | 2.1 | 😱 23.5 |
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| RAM Used (GB) | 0.3 | 😱 15 |
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| Disk Size (GB) | 4.07 | 4.07 |
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### RTX 3090 on Windows PC
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- CPU: Intel 13th series
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- GPU: NVIDIA GPU 3090 (Ampere - sm 86)
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- RAM: 64GB
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- OS: Windows
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#### TinyLlama-1.1b q4
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| Metrics | GGUF (using the GPU) | TensorRT-LLM |
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| -------------------- | -------------------- | ------------ |
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| Throughput (token/s) | 131.28 | ✅ 194 |
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| VRAM Used (GB) | 2.1 | 😱 21.5 |
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| RAM Used (GB) | 0.3 | 😱 15 |
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| Disk Size (GB) | 4.07 | 4.07 |
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#### Mistral-7b int4
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| Metrics | GGUF (using the GPU) | TensorRT-LLM |
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| -------------------- | -------------------- | ------------ |
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| Throughput (token/s) | 88 | ✅ 137 |
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| VRAM Used (GB) | 6.0 | 😱 23.8 |
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| RAM Used (GB) | 0.3 | 😱 25 |
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| Disk Size (GB) | 4.07 | 4.07 |
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### RTX 4060 on Windows Laptop
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- Manufacturer: Acer Nitro 16 Phenix
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- CPU: Ryzen 7000
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- RAM: 16GB
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- GPU: NVIDIA Laptop GPU 4060 (Ada)
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#### TinyLlama-1.1b q4
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| Metrics | GGUF (using the GPU) | TensorRT-LLM |
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| -------------------- | -------------------- | ------------ |
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| Throughput (token/s) | 65 | ❌ 41 |
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| VRAM Used (GB) | 2.1 | 😱 7.6 |
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| RAM Used (GB) | 0.3 | 😱 7.2 |
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| Disk Size (GB) | 4.07 | 4.07 GB |
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#### Mistral-7b int4
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| Metrics | GGUF (using the GPU) | TensorRT-LLM |
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| -------------------- | -------------------- | ------------ |
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| Throughput (token/s) | 22 | ❌ 19 |
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| VRAM Used (GB) | 2.1 | 😱 7.7 |
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| RAM Used (GB) | 0.3 | 😱 13.5 |
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| Disk Size (GB) | 4.07 | 4.07 |
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