Key takeaways
- The right GPU for LLM inference depends on the model, workload, latency requirements, and cost, not raw compute power. VRAM determines whether a model fits, memory bandwidth drives response speed at low batch sizes, and compute capacity matters most for high-throughput, large-batch workloads.
- Inference performance depends on the software stack as much as the hardware. In benchmarks serving Llama 3 8B, an H100 running TensorRT-LLM generated 2,850 tokens per second at batch size 32, compared with 2,200 tokens per second on the same GPU running vLLM.
- Cost per token and cost per request are more useful than hourly GPU price for comparing inference options. For example, an H100 costing $3.50 per hour and generating 2,850 tokens per second serves roughly one million tokens for about $0.34 under those benchmark conditions.
- Utilization determines whether cloud or owned GPUs are more economical. Cloud suits unpredictable demand, bare metal becomes cost-effective with consistent use, and a hybrid approach can pair dedicated capacity for baseline demand with cloud capacity for spikes.
- Estimate VRAM requirements as (parameters × bytes per weight) × 1.3 to account for KV cache overhead, and plan for headroom, since models tend to grow. A GPU that seems oversized for an 8B model may be right-sized once a 70B model is deployed.
Choosing a GPU for LLM inference isn't as simple as picking the most powerful card. The right choice depends on the model, memory requirements, workload, latency needs, and cost.
Inference also has different hardware requirements from training. Once a model is in production, ongoing performance and GPU utilization directly affect the cost of serving each request.
This guide compares the key GPU specifications for LLM inference, explores current hardware options and software considerations, and explains how to choose the right GPU for your workload.
Why GPU Choice Matters for AI Inference
Once a model is in production, inference becomes an ongoing infrastructure cost. The GPU you choose affects how efficiently you can serve requests, making hardware selection an important part of planning an AI deployment.
Inference Has Different Hardware Needs
Training and inference place different demands on GPUs. Training often benefits from high compute performance and fast communication between GPUs. Inference performance depends on several factors, including GPU memory, memory bandwidth, compute capacity, model size, and workload characteristics.
For LLMs, memory bandwidth can be particularly important during token generation because the GPU repeatedly accesses model weights and KV cache data. However, the relative importance of each specification depends on the model and serving workload.
Consider Cost as Well as Performance
Raw GPU performance doesn't tell you whether a particular GPU is the most cost-effective choice. A less expensive GPU may be the better option if it provides sufficient throughput and memory for your workload.
Useful metrics include cost per token, cost per request, and overall GPU utilization. These help you compare the cost of serving workloads rather than simply comparing hardware specifications.
Avoid Over- or Under-Provisioning
Choosing too much GPU capacity can leave resources underutilized, while choosing too little can make it difficult to meet latency or throughput requirements. The right balance depends on factors such as request volume, model size, batch size, and performance targets.
Key GPU Metrics for LLM Inference
When comparing GPUs for LLM inference, don't focus only on raw compute performance. Three specifications are particularly important: VRAM, memory bandwidth, and compute capacity.
VRAM: Can the Model Fit?
VRAM is the GPU's available memory. Your model needs enough of it to store its weights and the additional data required while generating responses.
Larger models require more VRAM, while techniques such as quantization can reduce the amount of memory they need. This makes VRAM one of the first specifications to check when choosing a GPU.
Memory Bandwidth: How Quickly Can the GPU Access Data?
Memory bandwidth affects how quickly the GPU can move data between its memory and its processing units. For LLM inference, higher memory bandwidth can help improve response speed, particularly when serving individual requests or smaller batches.
Compute: How Much Processing Power Do You Need?
Compute performance becomes more important as you increase the number of requests being processed at the same time. Workloads with larger batches or higher throughput requirements can benefit from GPUs with greater compute capacity.
The Right Balance Depends on Your Workload
There isn't one GPU specification that determines inference performance. The importance of VRAM, memory bandwidth, and compute depends on the model you're running and how you serve it.
The best GPU is therefore the one that provides enough memory and performance for your workload without paying for capacity you don't need.
Current GPU Options for LLM Inference
There are several GPU options for LLM inference, but NVIDIA remains the most widely used choice for production AI workloads. AMD and Intel also offer data center alternatives, while consumer GPUs can be useful for development and smaller deployments.
NVIDIA GPUs
NVIDIA offers GPUs across a wide range of performance and price points.
The H100 is widely used for demanding production AI workloads, while newer Blackwell GPUs, including the B100 and B200, offer higher memory capacity and performance for larger and more demanding models.
For smaller workloads, GPUs such as the A100 and L40S can provide a practical balance of performance and cost. Consumer GPUs such as the RTX 4090 can also be useful for development, testing, and smaller models where data center hardware isn't necessary.
The best NVIDIA GPU depends on your model size, expected workload, performance requirements, and budget.
AMD GPUs
AMD's data center GPUs, including the MI300X, are another option for LLM inference. Its large memory capacity makes it well-suited to workloads involving larger models.
AMD's ROCm software platform has also expanded support for AI workloads, although teams should test their specific models and inference software before choosing AMD hardware for production.
Intel and Other Options
Intel also offers data center accelerators such as Gaudi 3, which are designed for AI workloads and can be worth evaluating for organizations looking for alternatives to NVIDIA and AMD.
However, hardware is only part of the decision. Software support, model compatibility, availability, and the ability to manage the infrastructure effectively should all be considered alongside raw GPU performance.
The Software System: Why CUDA and TensorRT Matter
GPU hardware is only part of the equation when it comes to inference performance. The software stack used to run and optimize a model can make a significant difference, even when the underlying hardware is the same.
NVIDIA has an advantage here because of its mature software ecosystem. CUDA provides the foundation, while tools such as TensorRT and TensorRT-LLM help optimize and serve AI models efficiently. Triton Inference Server adds capabilities for deploying and managing inference workloads.
This software support is an important consideration for production environments. A strong ecosystem can make it easier to optimize models, integrate different tools, troubleshoot issues, and maintain workloads over time.
AMD has also made significant progress with its ROCm software platform, which supports frameworks such as vLLM. However, software support and optimization can vary depending on the model and workload. Teams considering AMD should therefore test their specific models and inference stack before making a hardware decision.
Ultimately, GPU specifications tell only part of the story. The software available for a particular platform can have just as much influence on how well a GPU performs in a real-world inference environment.
Emerging Alternatives: TPUs and LPUs
GPUs aren't the only option for AI inference. TPUs and LPUs offer alternatives for specific workloads, although their suitability depends on the models, software, and infrastructure you already use.
Google TPUs
Google's TPUs are designed specifically for machine learning workloads and can be well suited to high-throughput inference on Google Cloud. They can be particularly attractive for teams already using Google's infrastructure and frameworks.
The main consideration is portability. TPUs are closely tied to Google's ecosystem, so they may be less suitable for organizations that need to run workloads across different cloud or hardware environments.
Groq LPUs
Groq takes a different approach to AI inference, with hardware and software designed around fast, predictable model execution. Its platform can be particularly relevant for applications where low response times are a priority.
However, its supported models and deployment options are more limited than those available with general-purpose GPUs, so it is better suited to specific use cases than as a universal GPU replacement.
GPUs Remain the Most Flexible Option
TPUs and LPUs are worth considering for workloads that match their strengths, but GPUs remain the more flexible choice for organizations running a broad range of models and deployment environments. The right option ultimately depends on your workload, software requirements, and infrastructure strategy.
Real-World GPU Performance for LLM Inference
GPU performance can vary significantly depending on the model, serving software, batch size, and other workload conditions. The following benchmark results compare several GPUs using Llama 3 8B and Llama 3 70B.
Tokens per second measures how quickly a model generates output. TTFT (time to first token) measures how long it takes to start receiving a response. Lower TTFT means a faster initial response.
Llama 3 8B Performance
The table below compares several GPUs serving Llama 3 8B in FP16.
The results show that performance depends not only on the GPU but also on the inference software. The H100 configurations perform best in this benchmark, while TensorRT-LLM delivers higher throughput than vLLM on the NVIDIA systems tested.
Llama 3 70B Performance
The 70B model requires more GPU memory, so some configurations use multiple GPUs, while the H100 configuration also includes an INT4 version.
What the Results Show
The benchmarks illustrate an important point: there is no single GPU that is always the best choice for LLM inference.
Performance changes depending on the model, precision, batch size, GPU, and inference software. A GPU that performs well for a small model at low batch sizes may not be the best option for a larger model or a high-throughput workload.
The results also show why hardware specifications alone aren't enough when choosing an inference GPU. Testing the combination of hardware and software with your actual model and workload provides a more useful basis for making a decision.
Comparing Cloud and Bare-Metal GPU Costs
The cheapest GPU isn't always the one with the lowest purchase or hourly price. To understand the real cost of inference, you also need to consider how much the GPU is used and the costs of running and maintaining it.
Cost Per Token
One useful way to compare inference costs is cost per token. This combines the price of the GPU with the amount of output it can generate.
For example, if a GPU costs $3.50 per hour and generates 2,850 tokens per second, its cost per million tokens would be approximately $0.34 under those specific benchmark conditions.
The actual cost will vary depending on the GPU price, model, inference software, and workload.
Cloud vs. Bare-Metal Costs
The following example compares the estimated costs of running an H100 through the cloud versus owning a bare-metal server over three years.
*Hardware cost is amortized over three years.
These figures are an example rather than a universal cost comparison. Actual costs will vary based on cloud pricing, hardware configuration, utilization, electricity costs, staffing, and other operating expenses.
Utilization Changes the Economics
Cloud infrastructure can be attractive when demand is unpredictable because you can scale capacity without purchasing hardware upfront. Owning GPUs can become more economical when they are used consistently enough to justify the initial investment and ongoing operating costs.
This is why utilization should be considered alongside the headline GPU price when comparing deployment options.
Consider a Hybrid Approach
For some organizations, the most practical approach is to combine the two. Dedicated GPU capacity can handle predictable baseline demand, while cloud resources can provide additional capacity during periods of higher demand.
The right approach depends on your workload, expected utilization, infrastructure requirements, and how much flexibility you need to scale.
A Decision Framework for Selecting Your Inference GPU
Rather than prescribing a single "best" GPU, here's the framework we recommend for matching hardware to workload.
Step 1: Define your model and workload profile.
What models are you serving? What's the expected request volume? What's your latency SLA? A chatbot with sub-200ms TTFT requirements and a batch document processing pipeline have fundamentally different hardware needs.
Step 2: Calculate VRAM requirements.
Use the formula (parameters × bytes per weight) × 1.3 for KV cache overhead. Decide whether quantization is acceptable for your quality requirements, since this determines whether you need multi-GPU setups.
Step 3: Match hardware to memory bandwidth needs.
For latency-sensitive workloads at low batch sizes, prioritize memory bandwidth per dollar. For throughput-oriented batch workloads, FLOPS per dollar becomes more relevant.
Step 4: Run your TCO model.
Use the framework above with your actual utilization projections. Don't use peak capacity planning for a workload that's bursty.
Step 5: Factor in software stack maturity.
If you need maximum performance from NVIDIA hardware, budget for TensorRT-LLM integration. If you're evaluating AMD, validate your specific model works well on ROCm with vLLM before committing.
Step 6: Plan for model growth.
The models you serve today will be larger tomorrow. Buy VRAM headroom. An H100 80GB that seems oversized for your current 8B model will look right-sized when you deploy a 70B model in six months.
Orchestrating Your AI Factory with Rafay
Choosing the right GPU is only the beginning. Once organizations have GPU infrastructure across cloud, on-premises, or other environments, they need a consistent way to deploy workloads, manage resources, enforce policies, and understand how that infrastructure is being used.
Manage GPU Infrastructure Across Environments
Rafay provides a unified control plane for managing GPU clusters across hybrid and multi-cloud environments. Platform teams can give data scientists and ML teams self-service access to AI infrastructure while maintaining control over resources, policies, and security.
This helps organizations avoid managing each GPU environment separately and gives teams a more consistent way to deploy and operate AI workloads.
Turn GPU Capacity Into a Managed Service
For organizations providing AI infrastructure to multiple teams or customers, visibility into resource usage and costs becomes increasingly important. Rafay supports usage metering and cost allocation, helping organizations understand how GPU capacity is being consumed across teams, business units, or tenants.
This can also support organizations looking to offer GPU or inference capacity as an internal or external service, with appropriate governance and controls.
Get More From Your GPU Investment
The right hardware provides the foundation for inference performance, but managing that hardware effectively determines how efficiently it can be used in production. Rafay helps platform teams bring infrastructure management, self-service, governance, and resource visibility together in one operational layer.
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Frequently Asked Questions
How does Rafay help manage GPUs for LLM inference?
A centralized orchestration layer helps platform teams manage GPU infrastructure and AI workloads across cloud, on-premises, and hybrid environments. Rafay provides this layer, helping teams manage GPU resources without operating each environment separately.
Can Rafay manage different types of GPUs?
Organizations can manage different GPU configurations across their infrastructure, which is useful when different workloads require different hardware. Rafay provides a consistent management layer across these environments.
How does Rafay support self-service GPU access?
Data science and ML teams can access GPU infrastructure through self-service workflows while platform teams maintain control over resource access, policies, and usage. This reduces the need for manual provisioning while keeping governance in place.
Can Rafay help organizations manage GPU costs?
Visibility into GPU usage can help organizations understand how capacity is being consumed across teams, business units, or tenants. Rafay supports usage metering and cost allocation to make that usage easier to track.
Can Rafay support inference-as-a-service?
Organizations can use GPU infrastructure to provide AI and inference capabilities as an internal or external service. Rafay supports the self-service workflows, governance, and usage visibility needed to manage these environments.
Does Rafay support hybrid and multi-cloud GPU environments?
GPU infrastructure can span multiple clouds, on-premises environments, and other infrastructure providers. Rafay provides a consistent management layer across these environments, helping platform teams standardize how AI workloads are deployed and managed.
How does Rafay help organizations get more value from their GPU investment?
Better visibility and centralized management can help organizations make more effective use of available GPU capacity. Rafay brings resource management, workload deployment, governance, and usage visibility together in a single operational layer.