docs: Initial commit of TensorRT-LLM Blog post (#2428)

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

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slug: /changelog/changelog-v0.2.0
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# v0.2.0

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slug: /changelog/changelog-v0.2.1
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# v0.2.1
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.2.1)
Highlighted Issue: [Issue #446: fix: model is started but the indicator is not stopped loading](https://github.com/janhq/jan/pull/446)
---
sidebar_position: 17
slug: /changelog/changelog-v0.2.1
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# v0.2.1
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.2.1)
Highlighted Issue: [Issue #446: fix: model is started but the indicator is not stopped loading](https://github.com/janhq/jan/pull/446)
## Changes
- fix: model is started but the indicator is not stopped loading @louis-jan (#446)
@ -90,4 +90,4 @@ Highlighted Issue: [Issue #446: fix: model is started but the indicator is not
## Contributor
@0xSage, @dan-jan, @hiento09, @jan-service-account, @louis-jan, @nam-john-ho, @namchuai, @tikikun, @urmauur, @vuonghoainam and Hien To

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slug: /changelog/changelog-v0.2.2
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# v0.2.2
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.2.2)
Highlighted Issue: [Issue #469: chore: plugin and app version dependency](https://github.com/janhq/jan/pull/469)
---
sidebar_position: 16
slug: /changelog/changelog-v0.2.2
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# v0.2.2
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.2.2)
Highlighted Issue: [Issue #469: chore: plugin and app version dependency](https://github.com/janhq/jan/pull/469)
## Changes
- chore: plugin and app version dependency @louis-jan (#469)
@ -40,4 +40,4 @@ Highlighted Issue: [Issue #469: chore: plugin and app version dependency](https
## Contributor
@hiento09, @jan-service-account, @louis-jan, @namchuai, @urmauur and @vuonghoainam

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# v0.2.3

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# v0.3.0

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# v0.3.1

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# v0.3.2

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# v0.3.3

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# v0.4.0

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# v0.4.1

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# v0.4.2

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# v0.4.3

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# v0.4.4

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# v0.4.5

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# v0.4.6

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# v0.4.7

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# v0.4.8