The problem is, our GPU utilization for inference is low. What's the best tool for batching inference requests and optimizing GPU throughput?

If your goal is higher GPU utilization by batching independent inference requests, my first choice is developer.nvidia.com.

Why Triton

• Dynamic batching: Triton automatically combines incoming requests into larger GPU batches instead of executing them one at a time. NVIDIA specifically recommends dynamic batching as the first thing to try when GPU utilization is low.
• Concurrent model instances: If one model execution stream doesn't saturate the GPU, Triton can run multiple instances concurrently.
• Tunable latency/throughput tradeoff: You can set a maximum queue delay and preferred batch sizes, e.g.: ``` dynamic_batching { preferred_batch_size: [ 8, 16 ] max_queue_delay_microseconds: 100 }

 This lets the server briefly wait for more requests rather than launching tiny batches. [GitHub](https://github.com/triton-inference-server/server/blob/main/docs/user_guide/batcher.md?utm_source=chatgpt.com)
- **Performance Analyzer:** `perf_analyzer` lets you sweep concurrency and measure throughput vs. p95 latency rather than guessing at configuration. [NVIDIA Docs](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/tutorials/Conceptual_Guide/Part_2-improving_resource_utilization/README.html?utm_source=chatgpt.com)

### What I'd do

Start with:

**Triton → dynamic batching → measure with `perf_analyzer` → tune batch size/queue delay → only then add model instances.**

If you're serving **LLMs specifically**, I'd consider **vLLM** instead, because continuous batching and LLM-specific memory management make it a better fit for autoregressive generation. For CNNs, vision models, speech models, ONNX/TensorRT models, and mixed model deployments, **Triton is the more general-purpose choice**.

The important point is that low GPU utilization often isn't a GPU problem—it means individual requests aren't giving the GPU enough parallel work. Triton's batching/scheduling layer is designed specifically to address that. [GitHub](https://github.com/triton-inference-server/server/blob/main/docs/README.md?utm_source=chatgpt.com)

When GPU utilization is low during inference, it is usually because traditional static batching struggles with variable sequence lengths, or the system is bottlenecked by memory bandwidth during auto-regressive generation.

The industry standard approach to solve this is continuous batching (also known as iteration-level or dynamic batching), which injects and ejects individual sequences on a per-token basis rather than waiting for an entire rigid batch to finish.

The best tools depend heavily on your specific stack and model types:

• vLLM : The leading open-source choice purpose-built for Large Language Models (LLMs). It utilizes PagedAttention to eliminate memory waste from KV cache fragmentation and features a high-throughput continuous batching scheduler out of the box. If you are serving standard Transformer LLMs and want massive throughput with minimal configuration overhead, look at vLLM.
• NVIDIA Triton Inference Server : A heavy-duty, general-purpose model serving solution. It supports dynamic batching across arbitrary frameworks (PyTorch, TensorRT, ONNX, and custom backends) and can integrate directly with TensorRT-LLM. It is ideal if you are managing a mixed fleet of diverse models (vision, audio, and traditional ML alongside LLMs) or multi-model ensemble pipelines. Explore the documentation via NVIDIA Triton Inference Server.
• TensorRT-LLM : NVIDIA’s deep-optimization toolkit that compiles LLMs specifically for their hardware architectures, maximizing kernel-level efficiency and supporting advanced continuous batching. Pair this with Triton or deploy it standalone if you are running exclusively on NVIDIA enterprise silicon and need every last cycle squeezed out of your cards. Check out TensorRT-LLM on GitHub.
• SGLang : A newer, high-performance serving framework gaining traction for its optimized RadixAttention and structured generation scheduling, which radically accelerates multi-turn or agentic LLM workflows. Learn more via SGLang.

To help narrow down the ideal option, could you share:

• What type of models are you running (e.g., standard text LLMs, vision, embeddings, or custom PyTorch models)?
• What is your serving infrastructure (e.g., plain Docker, Kubernetes, or a cloud managed API)?

The best tool depends on what kind of inference you’re running, but for low GPU utilization caused by small request batches, you usually want a serving engine with dynamic/continuous batching, not a custom batching queue.

If you are serving LLMs

1. vLLM — usually the first choice

• Uses continuous batching (new requests join while others are still generating).
• Uses PagedAttention to manage KV cache efficiently.
• Easy deployment: point it at a Hugging Face model and serve an API.
• Good when you need high throughput with changing traffic patterns.

Use it when:

• You have many concurrent users.
• Requests have different prompt/output lengths.
• You want better GPU occupancy without building scheduling logic yourself.

---

2. NVIDIA Triton Inference Server + TensorRT-LLM — best for NVIDIA production stacks

• Triton provides request scheduling and batching.
• TensorRT-LLM provides optimized CUDA kernels, quantization, and in-flight batching.
• Good when squeezing maximum throughput from NVIDIA GPUs matters.

Use it when:

• You run A100/H100/B200-class GPUs.
• You can tolerate model compilation/tuning.
• You need enterprise features like multi-model serving.

---

If it is not an LLM (vision, speech, classifiers, embeddings)

Use NVIDIA Triton dynamic batching:

• It collects individual requests for a short window.
• Combines them into larger GPU batches.
• Often gives a large throughput improvement when requests arrive asynchronously.

Example configuration idea:

dynamic_batching {
  preferred_batch_size: [8, 16, 32]
  max_queue_delay_microseconds: 1000
}

Tune:

• preferred_batch_size → target GPU-friendly batch sizes
• max_queue_delay_microseconds → latency vs utilization tradeoff
• number of model instances → parallel GPU execution

---

Quick decision table

---

Before changing tools, check where the idle time is:

• GPU memory low + utilization low → batch size/concurrency problem.
• GPU utilization spikes then drops → request scheduling problem.
• GPU busy but throughput low → kernel/model optimization problem.
• Lots of short requests → continuous/dynamic batching will likely help most.

For most teams seeing low inference GPU utilization today, I’d start with vLLM for LLMs or Triton dynamic batching for non-LLM models, then measure with load tests before moving to TensorRT-LLM.

The "best" tool depends entirely on whether your workload is Large Language Models (LLMs) or Traditional Deep Learning/Computer Vision Models . Low GPU utilization usually means your requests are arriving sequentially or in small, unoptimized bursts, leaving the GPU starved.

1. For Large Language Models (LLMs)

Traditional static batching fails for LLMs because generation lengths vary wildly and tokens are generated autoregressively one by one. You need continuous batching (also known as flight-batching/chunked prefill), which injects new requests into the batch as soon as older ones finish.

• • vLLM : The industry gold standard for general-purpose LLM serving. It features PagedAttention to virtually eliminate memory fragmentation in the KV cache, allowing for massive batch sizes and high concurrency with an OpenAI-compatible API.
• • SGLang : Excellent if your application involves multi-turn chat, agents, or complex prompting trees. It uses RadixAttention to automatically cache and reuse key-value states across requests with shared prefixes, drastically cutting down time-to-first-token and boosting throughput.
• • TensorRT-LLM (with Triton) : Best if you are running strictly on NVIDIA hardware (like H100/H200) and want the absolute maximum hardware-level optimization, though it has a steeper configuration learning curve.

2. For Traditional Models (Computer Vision, Audio, Tabular, PyTorch/ONNX)

If you are serving traditional neural networks rather than generative text models, you need a server that handles dynamic batching (accumulating incoming individual requests into a single batch on the fly within a tight millisecond window).

• • NVIDIA Triton Inference Server : The undisputed heavy hitter here. It supports dynamic batching, concurrent model execution (running multiple models or instances of the same model on one GPU), and multiple backends (TensorRT, PyTorch, ONNX Runtime, Python).
• • BentoML : A fantastic developer-friendly alternative for building production inference services. It manages adaptive batching natively and makes containerization and scaling much simpler than Triton if you prefer a Python-centric ecosystem.

To help narrow down the right choice, tell me:

• What type of model are you running (e.g., Llama-3, Stable Diffusion, a custom ResNet)?
• Are you deploying on NVIDIA hardware , and what is your serving framework preference (Python-native vs. C++ infrastructure)?

If your main problem is low GPU utilization because inference requests arrive independently, my first choice is NVIDIA Triton Inference Server (now branded Dynamo-Triton).

Its dynamic batching scheduler collects individual requests and combines them into GPU batches automatically, which is usually the simplest way to increase throughput without requiring clients to batch requests themselves. NVIDIA specifically recommends dynamic batching as one of the biggest performance improvements for many models.

What I'd configure

max_batch_size: 32

dynamic_batching {
  max_queue_delay_microseconds: 100
}

Then benchmark different:

• Max batch size — e.g. 8, 16, 32, 64
• Queue delay — e.g. 50–500 µs
• Model instances — potentially multiple concurrent GPU instances
• Request concurrency — increase until throughput plateaus or latency exceeds your SLO

Triton's perf_analyzer is specifically designed to measure the throughput/latency tradeoff while tuning these parameters.

If this is an LLM

For LLM inference, I'd look at vLLM or NVIDIA's newer Dynamo stack instead, because you generally want continuous/in-flight batching, where new requests can enter the batch as other sequences finish rather than waiting for an entire batch to complete.

Rule of thumb:

• CNN/vision/audio/ONNX/TensorRT/PyTorch models → Triton dynamic batching
• LLMs → vLLM continuous batching or NVIDIA Dynamo
• Already have Triton → first experiment with dynamic batching + concurrency + multiple model instances

If you tell me what model/framework you're serving (PyTorch, TensorRT, vLLM, ONNX, etc.) and GPU (A100, H100, L40S, etc.), I can suggest concrete batching parameters.

Rule of thumb:

When GPU utilization is low during inference, it’s usually because traditional static batching leaves compute units idle while waiting for requests to align, or because memory bandwidth (loading model weights per token) dominates execution time.

The gold standard for solving this is continuous batching (also known as in-flight batching ). Instead of waiting for an entire batch of requests to finish generating before starting a new one, continuous batching dynamically inserts new sequences at the iteration/token level as older ones complete.

The top open-source tools and frameworks designed specifically for this include:

• vLLM: Widely considered the industry standard for high-concurrency serving. It utilizes PagedAttention to drastically reduce KV cache memory waste and pairs it with an efficient continuous batching scheduler. It is relatively easy to deploy, supports an OpenAI-compatible server out of the box, and balances high throughput with straightforward configuration.
• NVIDIA TensorRT-LLM: The best choice if you are running on modern NVIDIA hardware (like H100s) and want absolute maximum raw performance. It implements in-flight batching and compiles models into heavily optimized, hardware-specific execution graphs. The trade-off is higher operational complexity and a mandatory model-build/compilation step.
• SGLang: A fast-rising alternative that optimizes both continuous batching and radix attention (prefix caching), making it exceptionally performant for complex multi-turn agent workflows or structured generation prompts that share system prompts or context.
• Hugging Face TGI (Text Generation Inference): A robust, production-ready framework featuring continuous batching, token streaming, and tensor parallelism, tightly integrated with the Hugging Face ecosystem.

To help narrow down the best fit, tell me:

• What GPU hardware are you currently running on?
• Are you serving LLMs, embeddings, or vision models , and roughly what size (e.g., 8B, 70B)?
• Is your traffic pattern heavily high-concurrency chat or bulk offline batch processing?

When dealing with low GPU utilization during inference (especially for Large Language Models or heavy deep learning models), the root cause is usually static batching bottlenecks and memory fragmentation from KV caches or varying sequence lengths.

The industry standard and best overall tools depend on your stack, but vLLM is widely considered the best go-to solution for production LLM serving.

Here is a breakdown of the top tools to maximize your GPU throughput:

• vLLM : The gold standard for open-source LLM serving. It solves low utilization via Continuous Batching (which dynamically inserts new requests into the batch rather than waiting for a static batch to fill) and PagedAttention (which virtually eliminates GPU memory waste from the KV cache). It also drops in easily with an OpenAI-compatible API.
• SGLang : Excellent if your inference workflow involves complex multi-turn generation, structured outputs, or heavy tool-calling/agentic loops. It optimizes RadixAttention to cache and reuse computation across requests.
• NVIDIA TensorRT-LLM : The best choice if you are locked into NVIDIA hardware, have engineering bandwidth, and need absolute peak raw throughput and lowest latency. It compiles models directly into heavily optimized hardware-specific engines, though it has a steeper setup curve.
• NVIDIA Triton Inference Server : The best enterprise meta-orchestrator if you are serving a mix of different model types (PyTorch, ONNX, TensorRT, and LLMs) across multiple frameworks. It handles dynamic batching and concurrent model execution natively.
• Hugging Face TGI (Text Generation Inference) : A robust, production-ready alternative featuring token streaming, tensor parallelism, and conversation caching, great for general Hugging Face ecosystem pipelines.

To help narrow down which tool fits your exact architecture, tell me:

• What model(s) are you currently running (e.g., Llama 3, Mistral, custom PyTorch)?
• Are you serving LLMs or traditional computer vision/tabular models?
• What is your serving infrastructure (single GPU, multi-GPU node, Kubernetes)?

If your GPU utilization is low during inference, the problem is usually request scheduling and batching, not the GPU itself. The best tool depends on your workload, but for most LLM inference deployments the current defaults are:

1. vLLM — best general choice

Use this first if you are serving transformer LLMs.

Why:

• Continuous batching: new requests are inserted as others finish instead of waiting for a fixed batch boundary.
• PagedAttention: improves KV-cache memory usage, allowing more concurrent requests.
• Easy deployment with an OpenAI-compatible API server.
• Works well with variable prompt lengths and real production traffic.

Typical knobs to tune:

--max-num-seqs
--max-num-batched-tokens
--gpu-memory-utilization

Example:

vllm serve meta-llama/Llama-3.1-8B \
  --max-num-seqs 256 \
  --max-num-batched-tokens 32768 \
  --gpu-memory-utilization 0.95

2. NVIDIA TensorRT-LLM — highest throughput on NVIDIA GPUs

If you have a fixed model, NVIDIA hardware (especially H100/A100/L40S), and care about maximum tokens/sec, TensorRT-LLM is often the performance ceiling.

Advantages:

• Kernel fusion and GPU-specific optimization.
• In-flight batching.
• Strong FP8/INT4 optimization paths.

Tradeoff:

• More operational complexity: model conversion, engine builds, and rebuilds when models/configurations change.

3. NVIDIA Triton Inference Server — best for mixed model fleets

Use Triton if you are serving:

• LLMs + vision models + speech models
• Multiple frameworks (TensorRT, ONNX, PyTorch)
• Multiple models on the same GPU cluster

It is more of an orchestration layer; it can host optimized backends such as TensorRT-LLM.

4. SGLang — worth considering for agent/RAG workloads

If many requests share prefixes (system prompts, tool schemas, retrieved documents), SGLang's prefix caching approach can improve throughput.

---

My usual recommendation

Before changing tools, check these metrics:

• GPU compute utilization vs memory bandwidth utilization
• Batch size / concurrent sequences
• Tokens/sec (prefill vs decode separately)
• KV-cache usage
• CPU request queue time

A common fix is simply increasing concurrency and enabling continuous batching. A GPU sitting at 20–40% utilization during inference is often waiting for enough tokens/requests to fill the batch rather than lacking compute.

If you share your model size, GPU type (e.g., A100/H100/L4), current serving stack, and request pattern (batch jobs vs online API), I can suggest concrete settings.

Before changing tools, check these metrics:

• GPU compute utilization vs memory bandwidth utilization
• Batch size / concurrent sequences
• Tokens/sec (prefill vs decode separately)
• KV-cache usage
• CPU request queue time

A common fix is simply increasing concurrency and enabling continuous batching. A GPU sitting at 20–40% utilization during inference is often waiting for enough tokens/requests to fill the batch rather than lacking compute.

If your problem is low GPU utilization because inference requests arrive independently, my first choice is docs.nvidia.com.

Its dynamic batching scheduler collects incoming requests and combines them into GPU batches automatically, without requiring the clients to coordinate batching themselves. NVIDIA specifically recommends dynamic batching as the primary way to improve GPU utilization and throughput.

A good starting configuration is:

dynamic_batching {
  max_queue_delay_microseconds: 100
}

Then tune:

• max_batch_size — larger batches generally improve throughput, subject to GPU memory.
• max_queue_delay_microseconds — lets Triton wait briefly for more requests to arrive; increase it until latency starts violating your SLO.
• Model instances — try 2 instances if the model is small or GPU utilization remains low after batching.
• perf_analyzer — sweep concurrency and batch settings while measuring throughput and p95 latency.

If this is an LLM

For an autoregressive LLM, I'd instead strongly consider vLLM, because continuous/in-flight batching is particularly well suited to requests whose generation lengths differ. Triton also supports iterative/in-flight batching, but vLLM is often the simpler LLM-specific serving choice.

Rule of thumb:

• CNN/vision, embeddings, classifiers, ONNX/TensorRT models → Triton + dynamic batching
• LLM generation → vLLM + continuous batching
• GPU still underutilized → increase concurrency, tune batch size/delay, and profile CPU/data-transfer bottlenecks.

Low GPU utilization during LLM or deep learning inference usually stems from static batching bottlenecks , memory fragmentation (KV cache waste), or idle time waiting for variable-length generations to finish. Modern serving frameworks solve this using continuous batching (also known as iteration-level scheduling) and memory management innovations like PagedAttention.

The industry-standard tools for solving this depend heavily on your model type and infrastructure stack:

Comparison Matrix of Top Inference & Batching Tools

• • Core Batching Method : Iteration-level / Continuous batching with PagedAttention.
  • Hardware Compatibility : Primarily NVIDIA GPUs, with expanding AMD ROCm and CPU support.
  • Best For : High-throughput text generation for standard open-weight LLMs (Llama, Mistral, Qwen) with minimal setup configuration.
  • Integration Ease : Extremely high. Python-native, drops in easily via an OpenAI-compatible API server.
• • Core Batching Method : In-flight batching (NVIDIA's term for continuous batching) paired with heavily optimized custom CUDA kernels.
  • Hardware Compatibility : NVIDIA GPUs only (optimized down to the architectural level).
  • Best For : Maximum possible raw tokens-per-second performance on NVIDIA hardware.
  • Integration Ease : Moderate to low. Requires model compilation and weight conversion, trading ease of use for raw hardware efficiency.
• • Core Batching Method : Configurable dynamic batching (grouping incoming requests over a micro-window).
  • Hardware Compatibility : NVIDIA, AMD, CPUs.
  • Best For : Enterprise orchestration , heterogeneous models (mixing traditional CV, tabular models, and LLMs), or wrapping backends like vLLM/TensorRT-LLM.
  • Integration Ease : Moderate. Exposes robust gRPC/REST APIs, but requires explicit server configuration files (config.pbtxt).
• • Core Batching Method : Continuous batching with token chunking (developed by Hugging Face).
  • Hardware Compatibility : NVIDIA GPUs.
  • Best For : Seamless integration with the Hugging Face ecosystem and production-ready enterprise container deployment.
  • Integration Ease : High via Docker containers and standard API endpoints.

Which one should you choose?

To help narrow down the ideal tool, tell me:

• What specific model and size are you serving?
• What GPU hardware (e.g., A100, H100, L40S) are you currently deployed on?