As enterprises move from model experimentation to production-scale AI, the choice of accelerator becomes a critical factor for performance and cost. This article provides a stage-based framework for selecting the right AI hardware for each phase of the inference lifecycle. In many cases, it involves additional challenges such as balancing performance, cost, and deployment constraints.
This article offers a high-level overview of several common stages in the inference workflow, from basic service setup to large-scale and edge deployments. Some stages include tasks that might overlap with training-related testing or configuration, but the focus remains on understanding how accelerator requirements change as inference workloads move from local testing to production environments.
Inference workflow: 5 common stages
The inference workflow can vary depending on model size, target environment, and performance requirements. To better understand how AI accelerator needs shift throughout the process, this article outlines five typical stages that commonly appear in real-world deployments:
- Initial setup: Loading the model, starting the service, and verifying basic functionality
- Performance tuning: Optimizing latency, throughput, and resource usage through profiling and adjustments
- Production deployment: Running inference in live environments with a focus on stability, scaling, and monitoring
- Large model serving: Handling high-parameter models that require multiple accelerators or specialized memory management
- Edge deployment: Deploying models on low-power or constrained environments such as local devices or embedded systems
Each stage introduces new challenges and considerations that affect the type and capability of AI accelerators used. The following sections walk through these stages in more detail.
1. Initial setup
The first step in running an inference service is simply getting the model to load and respond. This might sound trivial, but in practice, it involves important steps like container startup, model loading, and basic service checks. Although this stage doesn't involve real traffic yet, issues like slow startup, memory overuse, or poor platform compatibility can delay deployment or introduce hidden risks later on.
While the hardware requirements at this point are relatively light, details like available memory, load time, and container integration already start to matter. Choosing a responsive and stable AI accelerator lays a solid foundation for what comes next.
Recommended AI accelerators
Recommended AI accelerators: L40S, A10, L4
These accelerators are well-suited for early-stage service setup due to their combination of moderate VRAM (16–48 GB), fast load performance, and compatibility with containerized environments. L40S, in particular, offers excellent cold-start behavior and fast model initialization times, making it a good choice for dry-run testing and multi-model startup scenarios.
Key considerations
Key considerations include:
- Memory capacity: Ensure the accelerator has sufficient VRAM to load the model without overflow.
- Load speed: Faster model initialization reduces wait time.
- Container compatibility: Seamless integration with Docker, Kubernetes, and similar tools.
- Stability: Reliable performance during initial testing phases.
- Cost-effectiveness: Reasonable pricing for development and testing environments.
Practical example
Scenario: Your team wants to deploy a Llama-2-7B chatbot and needs to verify it can load and respond to basic requests.
What you're doing: Testing if the model loads without errors, measuring startup time, and sending simple test queries. With the recommended accelerators for this stage, the model loads in ~30 seconds with room to test multiple configurations. With insufficient VRAM, you might encounter out-of-memory errors or wait 3–5 minutes for initialization.
2. Performance tuning
Once the model is running, the next focus usually shifts to performance. Can the system respond faster? Can it handle more requests per second? This stage often includes profiling, adjusting batch sizes, enabling mixed precision (FP8/FP16), and managing memory use more efficiently.
These optimizations heavily depend on what the hardware can support. Some AI accelerators provide native support for low-precision operations, which helps boost speed without compromising accuracy. Others offer better memory architectures that handle higher concurrency or dynamic batching more gracefully.
Recommended AI accelerators
Recommended AI accelerators: L40S, A10, L4
These accelerators support mixed-precision inference (FP16 and FP8) natively, which is key for reducing latency and improving throughput without sacrificing accuracy. L40S delivers high memory bandwidth (~700 GB/s), which helps sustain performance under dynamic batching. A10 and L4 strike a good balance between power efficiency and the compute required for tuning experiments.
Key considerations
Key considerations include:
- Mixed precision support: FP16/FP8 operations can significantly boost performance.
- Memory bandwidth: Higher bandwidth supports larger batches and faster data transfer.
- Dynamic batching capability: Efficiently handle variable workloads.
- Power efficiency: Balance between performance and power consumption.
- Profiling tools support: Compatibility with performance analysis tools.
- Thermal management: Stable performance under sustained workloads.
Practical example
Scenario: Your chatbot is working but too slow—responses take 250 ms and can only handle 4 requests per second. You need to optimize before launch.
What you're doing: Experimenting with mixed precision (FP16/FP8) to reduce latency, enabling dynamic batching to increase throughput, and profiling memory use to find bottlenecks. By the end, latency drops to 120 ms and throughput jumps to 45 requests per second. High memory bandwidth accelerators enable these optimizations without performance degradation.
3. Production deployment
Once the model moves into a live environment, the focus shifts from tuning to stability. Now the system needs to handle concurrent traffic, scale on demand, survive failures, and integrate cleanly with APIs or frontend systems. At this point, hardware-related issues like performance jitter or resource scheduling inefficiencies can lead to poor user experience or downtime.
In this stage, AI accelerators should support long-running workloads, multi-tenant execution, and smooth integration with orchestration platforms like Kubernetes or OpenShift. Compatibility with GPU operators also makes lifecycle management much easier.
Recommended AI accelerators
Recommended AI accelerators: L40S (primary), A10, L4, H100 (for lighter tasks)
L40S offers performance, memory (48 GB), and software ecosystem support, making it suitable for high-availability services in production. It also works well with GPU Operators and orchestration tools like OpenShift. H100 can be introduced in hybrid scenarios where lighter LLM workloads require extremely low latency or consistent multiuser performance.
Key considerations include:
Key considerations
Key considerations include:
- Stability and reliability: Consistent performance under long-running conditions.
- Scalability: Support for both horizontal and vertical scaling.
- Multi-tenancy support: Efficiently handle requests from multiple users or services.
- Orchestration integration: Works with Kubernetes, Red Hat OpenShift, and other platforms.
- Monitoring and observability: Support for performance metrics collection and troubleshooting.
- Failover capabilities: Graceful degradation and recovery mechanisms.
- API integration: Clean interfaces for front-end and back-end systems.
Practical example
Scenario: Your customer service chatbot is now live, handling 10,000 users per day with real business impact. Downtime means lost revenue.
What you're ensuring: The system runs 24/7 with 99.9% uptime, automatically scales during traffic spikes, and recovers gracefully from failures. You're monitoring latency (p50=120 ms, p99=250 ms), error rates (0.01%), and GPU utilization (65% average). With sufficient memory headroom, the system handles burst traffic without crashes and has been stable for 30+ days.
4. Large model serving
As large language models (LLMs) and foundation models become more common, deployment scenarios increasingly involve models with tens of billions of parameters. These models rarely fit on a single card and require techniques like tensor parallelism or model sharding to run efficiently.
This makes hardware selection much more demanding. Accelerators need not only high VRAM capacity but also fast memory bandwidth and robust interconnects. Features like HBM3 memory and NVLink/NVSwitch become essential when running long-context inference across multiple accelerators.
Recommended AI accelerators
Recommended AI accelerators: H100 SXM/PCIe, H200, GH200
Large models require accelerators with high-bandwidth memory and advanced interconnects to minimize communication overhead. H100 and H200 provide HBM3 support, with H100 SXM offering superior multi-GPU scalability. These are optimal for long-sequence LLM tasks and multi-GPU parallel execution environments.
Key considerations
Key considerations include:
- Ultra-high VRAM capacity: Support for models with tens of billions of parameters
- High memory bandwidth: HBM3 memory provides faster data access
- Advanced interconnects: NVLink/NVSwitch for efficient multi-GPU communication
- Tensor parallelism support: Ability to split models across multiple accelerators
- Long context processing: Efficiently handle long-sequence inputs
- Model sharding: Support for distributed model execution
- Communication overhead: Minimize latency in multi-GPU setups
Practical example
Scenario: You need to deploy Llama-2-70B, a massive model requiring ~140GB of memory that won't fit on any single GPU.
What you're managing: Splitting the model across 4 GPUs using tensor parallelism, with each GPU handling ~35 GB of model weights. The challenge is minimizing communication overhead between GPUs during inference. With high-bandwidth interconnects between GPUs, each request completes in 450 ms. Using standard connections would result in 2,000 ms latency—4x slower and unusable for production.
5. Edge deployment
Not all inference workloads run in a data center. In many cases (factories, autonomous devices, on-premises systems), models need to run at the edge. These environments often come with strict limitations on power, cost, and space. Traditional accelerators don't always work well here.
In these scenarios, the goal is to maintain reasonable inference performance while keeping energy consumption and physical footprint low. Edge-compatible accelerators also need to support lightweight models, often using quantization techniques like LoRA or INT4.
Recommended AI accelerators
Recommended AI accelerators: L4, A10, T4
These accelerators are designed for low-power, space-constrained environments, typically consuming 70-120 watts. L4 supports INT8 and FP16 inference efficiently and fits into compact form factors, making it a solid choice for edge servers or AI boxes. A10 offers a good trade-off between cost and performance, while T4 remains widely used for embedded deployments and lightweight workloads.
Key considerations
Key considerations include:
- Low power consumption: Typically under 100 watts
- Compact form factor: Suitable for small devices and embedded systems
- Quantization support: INT8, INT4, and other techniques to reduce model size
- Cost-effectiveness: Edge deployments often require many devices
- Lightweight model optimization: Support for efficient inference techniques like LoRA
- Thermal constraints: Passive or minimal cooling requirements
- Ruggedness: Reliability in harsh environmental conditions
Practical example
Scenario: Deploying voice assistants to 500 retail stores, each with limited power (75 W budget), space (small kiosk), and intermittent network connectivity.
What you're optimizing: Balancing performance against strict power and cost constraints. Using low-power accelerators with INT8-quantized models, each device achieves 180 ms latency with 98% accuracy while consuming only $75/year in electricity per location. High-power data center GPUs would cost 10x more in electricity and wouldn't fit in the physical space, making edge deployment impractical.
Summary
AI accelerator selection for inference is not a one-size-fits-all decision. From initial setup to edge deployment, each stage has unique needs that require different hardware capabilities:
- Initial setup requires stability and fast startup.
- Performance tuning benefits from mixed precision and high memory bandwidth.
- Production deployment demands reliability and orchestration integration.
- Large model serving needs maximum VRAM and advanced interconnects.
- Edge deployment prioritizes power consumption and compact design.
By understanding these stages and their corresponding accelerator recommendations, organizations can make more informed decisions about when and where to invest in specific hardware, ensuring their inference infrastructure is both cost-effective and well-suited to their real-world workload demands.
Red Hat AI give syou access to Red Hat AI Inference Server to optimize model inference across the hybrid cloud for faster, cost-effective deployments. Powered by vLLM, the inference server maximizes GPU utilization and enables faster response times. Learn more about the Red Hat AI Inference Server.
