Benchmarking NVIDIA TensorRT-LLM

Jan now supports NVIDIA TensorRT-LLM (opens in a new tab) in addition to llama.cpp (opens in a new tab), making Jan multi-engine and ultra-fast for users with Nvidia GPUs.

We've been excited for TensorRT-LLM for a while, and had a lot of fun implementing it (opens in a new tab). As part of the process, we've run some benchmarks, to see how TensorRT-LLM fares on consumer hardware (e.g. 4090s (opens in a new tab), 3090s (opens in a new tab)) we commonly see in the Jan's hardware community (opens in a new tab).

Key Findings

TensorRT-LLM was:

• 30-70% faster than llama.cpp on the same hardware
• Consumes less memory on consecutive runs and marginally more GPU VRAM utilization than llama.cpp
• 20%+ smaller compiled model sizes than llama.cpp
• Less convenient as models have to be compiled for a specific OS and GPU architecture, vs. llama.cpp's "Compile once, run everywhere" portability
• Less accessible as it does not support older-generation NVIDIA GPUs

Why TensorRT-LLM?

TensorRT-LLM (opens in a new tab) is Nvidia's open-source inference library that incorporates Nvidia's proprietary optimizations beyond the open-source cuBLAS (opens in a new tab) library.

As compared to llama.cpp (opens in a new tab), which today dominates Desktop AI as a cross-platform inference engine, TensorRT-LLM is highly optimized for Nvidia GPUs. While llama.cpp compiles models into a single, generalizable CUDA "backend" (opens in a new tab) that can run on a wide range of Nvidia GPUs, TensorRT-LLM compiles models into a GPU-specific execution graph (opens in a new tab) that is highly optimized for that specific GPU's Tensor Cores, CUDA cores, VRAM and memory bandwidth.

TensorRT-LLM is typically used in datacenter-grade GPUs, where it produces a face-melting 10,000 tokens/s (opens in a new tab) on NVIDIA H100 Tensor Core GPUs (opens in a new tab). We were curious for how TensorRT-LLM performs on consumer-grade GPUs, and gave it a spin.

Experiment Setup

We ran the experiment using standardized inference requests in a sandboxed environment:

• Model: Mistral 7b model, compiled and quantized at a comparable int4 quantization.
• Test runs: 5 batches of 10 runs each, per inference engine, on a bare metal PC with no other applications.
• Parameters: User defaults, i.e. batch_size 1, input_len 2048 and output_len 512
• Measurements:
  • CPU, memory from Jan system monitor
  • GPU VRAM utilization metrics from nvidia-smi, and taken over an interval of 14 seconds.
  • Throughput (token/sec) using Jan's built-in Tokens/sec perf stat (opens in a new tab).

Hardware Selection

We chose the following GPUs based on our users' preferences:

llama.cpp Setup

• llama.cpp commit 15499eb (opens in a new tab)
• We used Mistral-7b-q4_k_m in GGUF with ngl at 100

TensorRT-LLM Setup

• TensorRT-LLM version 0.7.1 (opens in a new tab) and build on Windows
• For TensorRT-LLM, we used Mistral-7b-int4 AWQ
• We ran TensorRT-LLM with free_gpu_memory_fraction to test it with the lowest VRAM consumption
• Note: We picked AWQ for TensorRT-LLM to be a closer comparison to GGUF's Q4.

Results

NVIDIA GeForce RTX 4090 GPU

Jan is built on this Dual-4090 workstation, which recently got upgraded to a nice case

The original case (or lack thereof) for our Dual-4090 cluster, as posted on r/localllama (opens in a new tab)

For this test, we used Jan's Dual-4090 workstation (opens in a new tab), which our engineers timeshare to build Jan.

The NVIDIA GeForce RTX 4090 (opens in a new tab) is the latest top-of-the-line desktop GPU, with an MSRP of $1,599, and uses the Ada architecture. It has a ~1000 GB/s memory bandwidth within VRAM, and a PCIe4 x16 lane (~32 GB/s) between the GPU and the CPU.

TensorRT-LLM was almost 70% faster than llama.cpp by building the model for the GeForce RTX 4090 GPU’s Ada architecture for optimal graph execution, fully utilizing the 512 Tensor Cores, 16,384 CUDA cores, and 1,000 GB/s of memory bandwidth.

The intuition for why llama.cpp is slower is because it compiles a model into a single, generalizable CUDA “backend” (opens in a new tab) that can run on many NVIDIA GPUs. Doing so requires llama.cpp to sacrifice all the optimizations that TensorRT-LLM makes with its compilation to a GPU-specific execution graph.

NVIDIA GeForce RTX 3090 GPU

Our 3090 Machine, now used by one of our engineers to build Jan

The NVIDIA's GeForce RTX 3090 (opens in a new tab) is a popular desktop GPU, and retails for approximately $1,500 (as of April 24). It uses the NVIDIA Ampere architecture. As compared to its successor GeForce RTX 4090, it has 33% fewer CUDA cores (10,496) and Tensor Cores (328) and 7% less memory bandwidth (~930 GB/s).

Interestingly, the GeForce RTX 3090 was only 16.6% slower compared with the GeForce RTX 4090. On TPS, TensorRT-LLM outperformed llama.cpp by 62.57%. Curiously, it also used negligible RAM for subsequent inference requests after the initial model warmup.

NVIDIA GeForce RTX 4070 Laptop GPU

We also benchmarked an NVIDIA GeForce RTX 4070 Laptop GPU with 8gb of VRAM, which is a popular configuration among Jan users. Laptop GPUs are less powerful than their desktop counterparts, as they trade portability for reduced energy consumption and thermal constraints.

TensorRT-LLM on the laptop dGPU was 29.9% faster in tokens per second throughput than llama.cpp, but significantly slower than the desktop GPUs.

The intuition for this is fairly simple: the GeForce RTX 4070 Laptop GPU has 53.1% fewer CUDA cores and Tensor Cores (compared to the 4090), and less VRAM (8gb vs. 24gb). This reduces the surface area for GPU-specific optimizations for TensorRT-LLM.

The GeForce RTX 4070 Laptop GPU is also ~70% slower than the GeForce RTX 4090 desktop GPU, showing the hardware effect of less electricity draw, less VRAM, and thermal constraints on inference speed.

Laptop with NVIDIA GeForce RTX 4090 eGPU

Our last benchmark was to experiment with an Asus RTX 4090 eGPU (opens in a new tab), that was connected via a Thunderbolt 3 port (opens in a new tab) to the Razer Blade 14's USB4 port (opens in a new tab). Theoretically, the results should be fairly similar to the GeForce RTX 4090 desktop GPU as they have identical underlying GPUs, but with very different connection speeds.

We thought it would be an interesting to see how TensorRT-LLM handles a 68.4% reduction in communication bandwidth between the CPU and GPU:

• Thunderbolt 3 connection (1.25-5 GB/s?) for eGPUs
• PCIe 4.0 x4 (~8 GB/s) for "on device" desktops

Overall, we used mid-to-high-end NVIDIA desktop GPUs for our tests, as TensorRT-LLM’s performance enhancements are most apparent on bigger VRAMs. For users with lower-spec machines, llama.cpp is better.

The Thunderbolt 3 eGPU had a 38.5% lower tokens/s as compared to the PCIe4.0 x16 connected GPU. But the % speedup vs. llama.cpp was similar, at around 69%.

Interestingly, the VRAM used with the eGPU was variably higher. Our hypothesis is that the slower communication bandwidth results in more VRAM being allocated, as memory is released mostly slowly as well.

Conclusion

Token Speed

• TensorRT-LLM is up to 70% faster than llama.cpp on desktop GPUs (e.g. 3090 GPU, 4090 GPUs) while using less RAM & CPU (but more fully utilizing VRAM)
• TensorRT-LLM is up to 30% faster on laptop GPUs (e.g. 4070 GPUs) with smaller VRAM

Max VRAM Utilization

• TensorRT-LLM used marginally more average VRAM utilization at peak utilization vs. llama.cpp (up to 14%). Though this could have interesting implications on consuming more electricity over time.
• Note: we used comparable (but not identical) quantizations, and TensorRT-LLM’s AWQ INT4 is implemented differently from llama.cpp’s q4_k_m

Max RAM Usage

TensorRT-LLM uses a lot less Max RAM vs. llama.cpp on slower connection (PCIe 3.0 or Thunderbolt 3) due to better memory management and efficient delegation to VRAM. On faster connection, it’s at least equal to llama.cpp.

Compiled Model Size and Number of Files

• Contrary to popular belief, TensorRT-LLM prebuilt models turned out to not be that huge
• Mistral 7b int4 was actually 25% smaller in TensorRT-LLM, at 3.05gb vs. 4.06gb
• Note: These are approximate comparisons, as TensorRT-LLM’s AWQ INT4 is implemented differently from llama.cpp’s q4_k_m
• The bigger takeaway is that the Compiled model sizes are roughly in the same ballpark, while the number of files for TensorRT-LLM is 7x the GGUF number of files.

Convenience

• Llama.cpp still wins on cross-platform versatility and convenience of a “compile once, run everywhere” approach
• TensorRT-LLM still requires compilation to specific OS and architecture, though this could be solved by pre-compiling and publishing models on Nvidia's NGC Model Catalog (opens in a new tab)

Accessibility

• Llama.cpp unsurprisingly beats TensorRT-LLM in terms of accessibility
• TensorRT-LLM does not support older NVIDIA GPUs and won’t work well on smaller VRAM cards (e.g. 2-4gb VRAM)

Final Notes

Our benchmarking is not perfect. We evaluated over a dozen tools (llmperf (opens in a new tab), psutil (opens in a new tab), gpustat (opens in a new tab), native utilities, and more) and found that everyone measures TPS, common metrics differently. We eventually settled on using our own tools in Jan, which are consistent across any inference engine and hardware. As for runtime parameters, we went with default settings, likely representing the typical user experience.

We also did not overclock for this benchmark , as it is not a default setting for most users. But we've measured in our tests that TensorRT-LLM can go even faster with a few tweaks. We see this as a pretty exciting future direction.

We're also publishing the underlying raw experimental data (opens in a new tab), and would encourage the community to scrutinize and help us improve.

Special thanks to Asli Sabanci Demiroz, Annamalai Chockalingam, Jordan Dodge from Nvidia, and Georgi Gerganov from llama.cpp for feedback, review and suggestions.

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