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There is no universal speed winner. LLM-Scaler-vLLM PV 1.5 is an Intel-focused vLLM distribution for XPU and Arc deployments, while TensorRT-LLM builds optimized NVIDIA TensorRT engines for NVIDIA GPUs. Your hardware, model, precision, batching, parallelism and software versions determine which delivers better latency or throughput. The official material available for these projects does not include a controlled, same-hardware PV 1.5-versus-TensorRT-LLM benchmark.
What the two stacks actually are
LLM-Scaler-vLLM PV 1.5
LLM-Scaler-vLLM is Intel’s distribution of vLLM for Intel XPU platforms, including Arc-oriented workflows. Intel’s official README documents Ubuntu 24.04, an Intel offline installer, Docker deployment and model-serving commands. It uses vLLM’s OpenAI-style serving model and documents tensor, pipeline and data parallelism, including multi-node deployment.
The README also tells administrators to pull an exact image version rather than the floating latest tag. It labels intel/llm-scaler-vllm:1.0 as a stable PV release image. That label should not be assumed to be the PV 1.5 image tag: confirm the exact tag associated with the PV 1.5 release you intend to deploy.
NVIDIA TensorRT-LLM
TensorRT-LLM is an NVIDIA software stack rather than a general-purpose accelerator abstraction. NVIDIA describes a Python API for defining large language models, building TensorRT engines and running those engines through Python and C++ runtimes. The stack is tied to NVIDIA GPUs and its supported models, GPUs, drivers and software combinations are listed in NVIDIA’s release documentation and support matrix.
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TensorRT-LLM can serve an OpenAI-compatible API through trtllm-serve, including /v1/models, /v1/completions and /v1/chat/completions. Its optimization flow commonly includes an engine-build stage before production serving.
At-a-glance differences
| Area | LLM-Scaler-vLLM PV 1.5 | TensorRT-LLM |
|---|---|---|
| Primary accelerator | Intel XPU and Arc workflows | NVIDIA GPUs |
| Execution approach | vLLM serving distribution | TensorRT engine build plus Python or C++ runtime |
| Operating-system guidance | Ubuntu 24.04 and Linux/Docker device-permission setup in Intel’s README | Linux and Windows support is listed for the TensorRT backend; deployment remains NVIDIA-GPU specific |
| Serving interface | vLLM’s OpenAI-style server | trtllm-serve with OpenAI-compatible endpoints |
| Parallelism documented by the project | Tensor, pipeline and data parallelism, including multi-node paths | In-flight batching and the parallel execution options supported by the selected model, GPU and release |
| Version discipline | Pin the Intel image tag and model revision | Pin the TensorRT-LLM release, CUDA/driver combination, model revision and engine configuration |
Is PV 1.5 faster than TensorRT-LLM?
The available official evidence cannot answer that as a universal yes-or-no question. Intel publishes an example for Qwen-7B with tensor parallelism set to 1: data-parallel setting 1 with batch size 10 is the 1x baseline, setting 2 with batch size 20 reaches 1.9x, and setting 4 with batch size 40 reaches 3.58x throughput. Intel characterizes data parallelism on Intel XPU as offering near-linear scaling.
Those figures describe scaling inside the Intel example. They are not a comparison with TensorRT-LLM, and they do not establish a fixed tokens-per-second result for every Arc or XPU model.
NVIDIA’s performance overview reports “Output Throughput per GPU (tokens/sec/GPU)” under a high-rate, maximum-load scenario. NVIDIA explicitly describes those tables as reference measurements rather than the peak performance TensorRT-LLM can deliver, and says that not every configuration was tested on every GPU. A number from those tables therefore cannot be placed directly beside Intel’s Qwen scaling multipliers to declare a winner.
Why published numbers are not directly comparable
- Hardware: An Intel XPU or Arc system and an NVIDIA GPU have different memory systems, kernel implementations and driver stacks.
- Model implementation: Even when the model name is identical, kernels, graph fusions and supported features can differ.
- Precision: FP16, dynamic FP8, dynamic INT4 and MXFP4 have different memory and compute behavior, and support varies by model and GPU generation.
- Workload: Prompt length, generated-token count, context size, concurrency and request mix change both latency and throughput.
- Batching and parallelism: Batch size, data parallelism, tensor parallelism, pipeline parallelism and in-flight batching affect utilization differently.
- Software versions: Image tags, TensorRT-LLM releases, CUDA versions, drivers and model revisions can change kernels and supported paths.
How to run a fair comparison
If you need a procurement or production decision, run both systems with a controlled workload rather than copying a headline number. Keep the following variables identical wherever the hardware and backend permit:
- Model checkpoint and revision.
- Tokenizer and sampling settings.
- Input and output token lengths.
- Precision or quantization target.
- Concurrency, request arrival pattern and maximum sequence length.
- Warm-up duration and measurement window.
- Number and type of accelerators.
Record at least these outcomes for each run:
- Time to first token (TTFT).
- Inter-token latency (ITL), sometimes called time per output token.
- End-to-end request latency, including tail percentiles such as p95 or p99.
- Output tokens per second and completed requests per second.
- Memory consumption and headroom under the target concurrency.
- Container startup time and, for TensorRT-LLM, engine-build time.
- Failure rate, queueing behavior and recovery time when concurrency exceeds the target.
For TensorRT-LLM, NVIDIA provides a reproducible throughput workflow through trtllm-bench throughput. Use the same benchmark definition and report the complete configuration with the results. For Intel, use the documented vLLM serving path and report the image tag, XPU model, parallelism settings and batch sizes.
A practical test matrix
Run more than one operating point. A low-concurrency test with short prompts exposes interactive responsiveness, while a high-concurrency test with long prompts and longer generations shows saturation behavior. Include the exact Qwen-7B-style scaling scenario only as an internal Intel baseline if it matches your service; do not present its 1x, 1.9x and 3.58x multipliers as cross-vendor scores. Repeat each case after warm-up, discard startup requests, and report median as well as p95 or p99 latency. Keep tokenizer, stop conditions and sampling parameters fixed so that different output lengths do not distort tokens-per-second results.
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Hardware and operating-system compatibility
Intel XPU and Arc
Choose the Intel distribution when the deployment is built around Intel XPU or Arc hardware. The documented setup is Linux-oriented: Intel’s README covers Ubuntu 24.04, an offline installer, Docker and the device permissions needed to expose the accelerator to the container. Confirm that the exact accelerator model, driver and PV release are listed in the support table before committing to a model or precision.
NVIDIA GPUs
TensorRT-LLM requires NVIDIA GPUs. NVIDIA’s backend information lists Linux and Windows availability for the TensorRT path, but operating-system support does not remove the GPU requirement. The GPU generation, CUDA version, driver and TensorRT-LLM release must form a supported combination.
Mixed clusters
Neither stack should be selected as a drop-in abstraction for a heterogeneous Intel-and-NVIDIA cluster. In practice, maintain separate serving images, validation matrices and benchmark baselines for each accelerator family. An OpenAI-compatible endpoint can make client integration similar, but it does not make kernels, checkpoints or performance interchangeable.
Model checkpoints, formats and precision
A checkpoint that loads in vLLM is not automatically a TensorRT engine input. TensorRT-LLM’s engine-building process requires that the model architecture and checkpoint path be supported by the specific release. Consult NVIDIA’s release support matrix for the model before attempting a build.
Intel’s compatibility table lists model-specific support for FP16, dynamic FP8, dynamic INT4 and MXFP4. These entries are not universal guarantees: a precision can be supported for one model and absent for another, or depend on the XPU generation. Validate the intended precision in the table and then test the exact checkpoint.
Quantization affects more than memory footprint. It can change kernel availability, output quality, throughput and latency. Benchmark the precision you will actually operate, rather than comparing an FP16 run on one backend with a quantized run on the other.
Checkpoint portability checklist
- Identify the model architecture and exact revision, not just a repository nickname.
- Verify that Intel’s table lists the architecture and requested precision for the target XPU or Arc generation.
- Verify that the TensorRT-LLM release matrix lists the same architecture, GPU family and precision.
- Confirm whether TensorRT-LLM expects a direct checkpoint path or a model-specific conversion and engine-build step.
- Compare generated outputs for a fixed prompt set before measuring speed, because a faster path is not useful if conversion changes behavior or quality.
Parallelism and scaling choices
Intel data parallelism
Intel documents data-parallel deployment on XPU and gives the Qwen-7B example described above. Increasing data-parallel replicas and batch size improved aggregate throughput from the 1x baseline to 1.9x at two replicas and 3.58x at four replicas in that example. The results show the scaling behavior of that setup, not a promise that every model will scale identically.
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Tensor and pipeline parallelism
Use tensor parallelism when a model must be split across devices for memory or compute reasons. Pipeline parallelism can distribute model stages, but introduces stage coordination and potential bubbles. Intel documents both approaches, as well as multi-node options; verify the supported topology and communication setup for the exact release.
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TensorRT-LLM emphasizes in-flight batching, which combines work from active requests as tokens become available. This can improve utilization for a busy service, but the benefit depends on request length, concurrency and scheduling limits. Measure both interactive latency and maximum-load throughput because a configuration tuned for one can degrade the other.
Serving APIs and operational workflow
Using the Intel vLLM server
- Install the documented Intel software prerequisites on Ubuntu 24.04, including the offline installer when required.
- Configure Docker and the XPU device permissions described by Intel so the container can access the accelerator.
- Pull the exact LLM-Scaler-vLLM image tag. Do not use
latest; verify which tag corresponds to PV 1.5 because the README’s stable example isintel/llm-scaler-vllm:1.0. - Start the vLLM OpenAI-style server with the model path, precision and parallelism settings validated against Intel’s support table.
- Send a small test request, check model loading and token streaming, then run a warmed-up benchmark at the intended concurrency.
Using TensorRT-LLM
- Install a TensorRT-LLM release together with its supported CUDA and NVIDIA driver combination.
- Check the release support matrix for the model architecture, checkpoint path, GPU generation and precision.
- Build the TensorRT engine using the release’s model-specific workflow. Record build options because they affect runtime behavior and reproducibility.
- Run the Python or C++ runtime, or launch the production server with
trtllm-serve. - Verify
/v1/models,/v1/completionsand/v1/chat/completionsbefore loading production traffic. - Use
trtllm-bench throughputwith a documented workload, then repeat with latency-oriented concurrency and request sizes.
Version pinning and reproducibility
Pin every input that can change a result. For LLM-Scaler-vLLM, record the full image tag, Intel driver and installer version, XPU model, model revision, precision, parallelism, batch size and Docker configuration. For TensorRT-LLM, record the package release, CUDA version, NVIDIA driver, GPU model, checkpoint revision, engine-build options and server settings.
Floating tags and moving support matrices make old results difficult to reproduce. Store the benchmark command, configuration file, logs and measured hardware utilization alongside the throughput and latency numbers.
Deployment cost and planning considerations
No single software price determines this choice. The practical cost is the combination of accelerator procurement, host and driver administration, validation time and ongoing upgrade work. Intel deployments require the documented Ubuntu, installer, Docker and device-permission setup. TensorRT-LLM adds an engine-generation stage and requires the team to retest engines when the model, GPU, CUDA, driver or TensorRT-LLM release changes.
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Budget capacity for parallel validation if the cluster contains several accelerator generations. A model and precision that pass on one XPU or NVIDIA GPU may have a different support status or kernel path on another. Keep separate images and benchmark baselines instead of assuming that a single container can represent every node. The engineering time spent on conversion, engine builds, troubleshooting and revalidation is part of the deployment decision even when serving APIs look similar.
Migration and rollout checklist
- Freeze a model revision, tokenizer, prompt set and target precision.
- Inventory each accelerator, driver, operating system and container runtime.
- Build a compatibility table for architecture, precision, parallelism and maximum context.
- Bring up a single replica and verify health, model loading, streaming and API responses.
- Run warm-up and saturation tests, recording TTFT, ITL, tail latency, throughput, memory and failures.
- Scale to the planned tensor, pipeline or data-parallel topology and repeat the tests.
- Canary production traffic, watching queue depth and tail latency before increasing concurrency.
- Archive image or package versions, engine artifacts, commands and logs so an upgrade can be compared with the same baseline.
Which backend fits your deployment?
Choose LLM-Scaler-vLLM when
- Your servers use Intel XPU or Arc accelerators.
- You want vLLM’s OpenAI-style model-serving workflow.
- The model and precision appear in Intel’s documented support table.
- Intel’s tensor, pipeline, data-parallel or multi-node deployment paths match your cluster.
- You prefer to avoid a separate TensorRT engine-build stage and the model is already validated in the Intel image.
Choose TensorRT-LLM when
- Your infrastructure is NVIDIA-only.
- You can validate the exact model, GPU, CUDA, driver and TensorRT-LLM release combination.
- Engine-level optimization and in-flight batching justify the build and compatibility work.
- Your team wants NVIDIA’s benchmark workflow and an OpenAI-compatible production server.
- You can budget time for engine generation, upgrades and revalidation when releases change.
Do not choose from a generic speed claim
A backend that wins on maximum-load output throughput may lose on TTFT, tail latency, memory headroom or startup time. Conversely, a simpler serving workflow can be the better operational choice even when another stack posts a higher peak number. Select the backend that satisfies the target service-level objective on the hardware you will actually run.
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Common failure modes and fixes
The container cannot see the accelerator
On Intel, recheck the Ubuntu, Docker and device-permission steps in the README and verify that the host driver matches the image. On NVIDIA, confirm the container runtime, driver and CUDA compatibility before troubleshooting the model.
The model loads on one backend but not the other
Check architecture and checkpoint support for the exact release. A vLLM-compatible checkpoint may require a supported conversion or engine-building path for TensorRT-LLM; do not assume that the same directory can be served unchanged.
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Confirm that the selected FP8, INT4 or MXFP4 mode is listed for that model and accelerator generation. If the required kernel is unavailable, test a supported precision and compare memory, latency and output quality before changing production settings.
Throughput improves while user latency worsens
Higher batch size, data parallelism or in-flight batching can increase aggregate tokens per second while adding queueing delay. Track TTFT, ITL and tail latency at the same time as throughput, and select the operating point that meets your latency objective.
Results change after an upgrade
Compare the full pinned configuration. Image tags, TensorRT-LLM releases, CUDA or driver versions, model revisions and engine-build settings can all change support and performance. Rebuild and rerun the benchmark rather than comparing numbers from different software stacks.
Bottom line
LLM-Scaler-vLLM PV 1.5 is the practical starting point for Intel XPU and Arc serving, especially when vLLM compatibility and Intel’s documented parallelism paths matter. TensorRT-LLM is the NVIDIA-specific choice when your team can build and maintain TensorRT engines and wants NVIDIA’s optimized runtime, in-flight batching and benchmark tooling. Official figures currently available do not establish a controlled PV 1.5-versus-TensorRT-LLM winner. Measure both stacks on the same model, precision and workload, and publish the complete configuration with latency, throughput, memory and build-time results.
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Is LLM-Scaler-vLLM PV 1.5 faster than TensorRT-LLM?
There is no universal answer. Intel’s published Qwen-7B figures show 1.9x throughput with two data-parallel replicas and 3.58x with four in that specific setup. NVIDIA’s figures are reference output-throughput measurements under different hardware and workloads. Run a controlled test on your own model and accelerators.
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Will PV 1.5 run my Intel Arc or XPU model?
It is designed for Intel XPU and Arc workflows, but support is model-, precision- and generation-specific. Check Intel’s compatibility table, then verify the exact driver, image tag and model revision on a single device before scaling out.
Can TensorRT-LLM use the same checkpoint as vLLM?
Not automatically. TensorRT-LLM must support the architecture and checkpoint path in the selected release, and the workflow may require model-specific conversion and engine building. A directory that loads in vLLM is not guaranteed to be a valid TensorRT engine input.
Which backend gives better throughput or latency for my GPU?
The hardware decides which backend is applicable: LLM-Scaler-vLLM targets Intel accelerators and TensorRT-LLM targets NVIDIA GPUs. Within those families, compare TTFT, inter-token latency, tail latency, output tokens per second, request throughput, memory and startup or engine-build time under identical workload conditions.
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Do both servers expose an OpenAI-compatible API?
Yes. Intel uses vLLM’s OpenAI-style server. TensorRT-LLM’s trtllm-serve exposes /v1/models, /v1/completions and /v1/chat/completions. Similar endpoints simplify client changes but do not make model support or performance interchangeable.
Which precisions are available?
Intel documents model-specific FP16, dynamic FP8, dynamic INT4 and MXFP4 entries. TensorRT-LLM precision support depends on its release, model and NVIDIA GPU. Validate the exact cell in each support matrix and benchmark the precision you will actually deploy.
How should I pin versions for reproducible results?
Record the full Intel image tag, installer and driver versions, XPU model, model revision, precision, parallelism, batch size and Docker settings. For TensorRT-LLM, also pin the package release, CUDA and driver versions, GPU model, engine-build options and server settings. Avoid floating tags such as latest.
Is TensorRT-LLM worth the extra engine-build work?
It can be when an NVIDIA-only service benefits from optimized TensorRT engines, in-flight batching and NVIDIA’s benchmark tooling. Include engine-build time, conversion effort, upgrade revalidation and operational complexity in the decision rather than comparing only peak throughput.
The Bottom Line
Use LLM-Scaler-vLLM PV 1.5 for Intel XPU or Arc deployments that value vLLM serving and Intel’s documented parallelism options. Use TensorRT-LLM for NVIDIA deployments where engine building, in-flight batching and NVIDIA-specific optimization fit the workflow.
Neither published data set proves a universal winner. Pin versions, verify model and precision support, and benchmark TTFT, inter-token latency, tail latency, throughput, memory and startup or engine-build time on the hardware and workload you will operate.
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