Choosing the best GPU for AI inference depends on your workload’s SLOs, model size, context length, budget and the maturity of your serving stack. For instance, the recent MLPerf Inference v5.1 added new LLM tests, including DeepSeek-R1 and Llama 3.1-8B.
It also highlights large gen-over-gen throughput and latency improvements across platforms. In other words, the rising demands for high throughput and processing make Cloud GPUs the best fit for AI Inferencing.
However, vendor specifications for current accelerators show major differences in memory and bandwidth that directly affect tokens per second and tail latency. For example,
- NVIDIA H200 provides 141 GB of HBM3e and 4.8 TB/s of peak bandwidth.
- AMD MI300X provides 192 GB of HBM3 with a peak bandwidth of 5.3 TB/s.
- NVIDIA L40S offers 48 GB GDDR6 at ~864 GB/s peak bandwidth.
Therefore, this guide will help you choose the right GPU for your AI inferencing workload. Let’s get started!
What is AI Inferencing?
AI inferencing is the process of running a trained model to generate outputs from new inputs. Instead of learning patterns, the model applies its learned parameters to classify, detect, translate, summarize or predict.
In production, inferencing prioritizes low latency, high throughput and cost efficiency. Performance depends on compute type, memory bandwidth, batching, quantization and model architecture.
For generative models, it means producing tokens step by step while managing KV cache and parallel requests. Teams deploy inference on GPUs, CPUs or specialized accelerators behind APIs with autoscaling, observability and safeguards.
The goal is real-time decisions that serve users and business workflows.
Why are GPUs Important for AI inference?
GPU for AI inference is a parallel processor optimized for matrix and tensor math. They accelerate neural network layers, sustain high memory bandwidth and exploit tensor cores and quantization for efficiency.
Used in servers and edge devices, they deliver low-latency, high-throughput predictions via batching, caching and libraries like cuDNN and TensorRT. As an enterprise, you ultimately care about user-visible latency and sustained throughput under cost and power limits.
Throughput and latency (critical for modern LLMs)
Decoder-phase FLOPs, memory bandwidth and keeping the KV cache resident dominate end-user latency for autoregressive models. As mentioned earlier, MLPerf Inference v5.1 results show material tokens/sec differences across GPU classes and server designs, especially under interactive scenarios with TTFT/TPOT constraints.
Memory capacity (decides sharding vs single-GPU)
Today’s GPUs differ widely, for example, H200 offers ~141 GB HBM3e, MI300X ~192 GB HBM3 and L40S 48 GB GDDR6. However, if weights and KV cache fit in memory, you avoid tensor or pipeline parallel overhead.
Energy efficiency and TCO
For sustained services, finishing work faster on a GPU can be more energy-efficient than prolonged CPU runs. Yet dense racks still require careful power and cooling planning. Recent publication by the Atlantic Council highlights GPUs’ practical energy advantages and the need for data-center-grade thermal design as density rises.
What Factors to Consider When Choosing GPU for AI Inference?
Choosing correctly requires aligning hardware capabilities with workload behavior and operational constraints. Here are the factors involved:
SLOs and traffic shape
- Define p50/p99 latency, burstiness and concurrency, then select for batch ability.
- NVIDIA Triton Inference Server supports dynamic batching to raise throughput while honoring latency budgets.
Precision and quantization
- Most stacks target FP8 or INT8 today, with FP4 arriving alongside Blackwell’s second-generation Transformer Engine.
- NVIDIA’s Model Optimizer documents measurable speedups from PTQ and sparsity on LLMs and SD-class workloads.
Memory and bandwidth
- You need capacity for weights and KV cache and bandwidth for long-context decoding.
- H200 lists ~4.8 TB/s, MI300X ~5.3 TB/s and L40S ~864 GB/s.
Power, cooling and density planning
- Check GPU TDPs, rack power limits and the cooling envelope before shortlisting hardware.
- DGX-class deployments and high-density racks emphasize careful planning.
Multi-tenant utilization with MIG
- Partition larger GPUs to improve utilization and QoS for mixed workloads where supported.
- For example, NVIDIA A100 documentation details up to seven hardware-isolated MIG instances.
How to benchmark before buying?
- Combine MLPerf Inference results with your “golden path” tests to measure p50/p99 and throughput under your scheduler.
- Community and vendor guides reference MLPerf, Merlin for recsys and HF/vLLM inference benchmarks as complementary signals.
What are the GPU requirements for the latest LLM Models?
When choosing the best GPU for AI inferencing, you must evaluate model context, precision and kernel coverage.
Context length inflation
- Llama 3.1 supports long contexts, including 128K, which expands the KV cache and stresses bandwidth and memory.
- A practical sizing formula is: 2 × layers × heads × head_dim × bytes_per_elem × tokens × batch.
Model variety
- Portfolios increasingly include Llama 3.x and DeepSeek-R1 families with differing compute and memory demands.
- MLPerf v5.1 added new LLMs and reasoning benchmarks, which shift best GPU picks depending on latency scenario and precision.
What is the Memory and Bandwidth Requirement for LLMs?
In our experience as a cloud GPU provider, you should size for single-GPU fit where possible, then scale out only when necessary.
Practical single-GPU thresholds
- 48 GB cards fit many 7B–13B models or larger models when quantized. The 80–96 GB cards expand headroom.
- Single-GPUs with 141–192 GB reduce the need for tensor parallel at long contexts.
- H200’s 141 GB and MI300X’s 192 GB materially increase single-GPU feasibility for 70B-class models at meaningful context windows.
When do you need a multi-GPU or NVLink?
- If weights and KV cache overflow a single device at your context and latency target, add tensor or pipeline parallel.
- You can also use NVLink-connected multi-GPU to keep p50 within budget.
Bandwidth for decoding
- Long sequences stress memory bandwidth more than peak FLOPs.
- HBM3/e bandwidth correlates with higher tokens/sec at fixed batch across benchmark rounds and vendor analyses.
Which NVIDIA GPUs are Best for AI Inference?
Start by choosing hardware that matches your memory, bandwidth and latency targets, then squeeze the most from software before you scale out.
NVIDIA L40S (48 GB GDDR6)
A cost-efficient workhorse for mid-size or quantized LLMs, the L40S performs strongly in FP8/INT8 with TensorRT-LLM and offers roughly 864 GB/s of bandwidth. Model Optimizer and structured sparsity can provide additional uplift where supported, making it a compelling default for budget-conscious inference at scale.
NVIDIA RTX 6000 Ada (48 GB GDDR6)
Ideal for workstation deployments, this card delivers the same capacity as L40S with slightly higher throughput (about 960 GB/s) suited to on-prem professional workflows that need strong inference performance without moving to datacenter SKUs.
NVIDIA RTX 4090 (24 GB GDDR6X)
Great for developer workstations and small-scale inference, the 4090 handles quantized or smaller LLMs and rapid prototyping with ~1 TB/s-class bandwidth and 24 GB of VRAM. Be aware of the trade-offs: no ECC, no server-grade form factors and no MIG support.
NVIDIA A100 (40/80 GB HBM2e)
A mature datacenter option with broad ecosystem support and strong INT8 performance, the A100 provides ~1.6–2.0 TB/s of bandwidth and supports MIG partitioning into up to seven instances. SXM configurations commonly run around 400 W TDP, making it a reliable choice for steady, well-supported inference stacks.
NVIDIA H100 / H200 (HBM)
Designed for mainstream LLM inference at scale, these accelerators add high-speed interconnect options via NVLink. The H200 increases on-package memory to 141 GB of HBM3e and pushes bandwidth to approximately 4.8 TB/s, improving long-context and high-throughput workloads.
NVIDIA L40 (non-S)
Best considered when cost sensitivity is paramount, the L40 offers the same 48 GB capacity and about 864 GB/s of GDDR6 bandwidth. That said, most teams will prefer the L40S for its higher compute density at similar budgets.
NVIDIA T4 and Tesla P4 (low-power)
Efficient, low-TDP accelerators for classical NLP/vision and smaller LLMs, these cards are well-suited to horizontal scale-out. Typical figures: T4 with 16 GB, ~320 GB/s, ~70 W and P4 with 8 GB, ~192 GB/s, ~50 W.
NVIDIA A30 (24 GB HBM2)
A solid mid-tier option for batched inference, the A30 combines 24 GB of HBM2 with ~933 GB/s of bandwidth at roughly 165 W, offering a balanced profile between performance and power.
Blackwell Series (B200/GB200)
The upcoming Blackwell generation introduces FP4 via a second-generation Transformer Engine, with early reports pointing to substantial tokens-per-second gains. Plan for high availability and ensure your software stack is ready to enable these features as they land.
Which AMD GPU is best for AI Inference?
When choosing between AMD GPUs for AI inferencing, you should consider capacity and software maturity together.
AMD MI300X (192 GB HBM3)
Its high capacity simplifies single-GPU deployment in longer contexts. However, ROCm support is maturing with vLLM. AMD MI300X provides192 GB HBM3 and ~5.3 TB/s bandwidth in vendor data sheets.
Stack considerations
- Confirm operator coverage and kernels in ROCm for your models, then validate quantization paths.
- AMD’s best-practice notes and vLLM guide outline recommended configurations and parameters for MI300X LLM inference.
Which GPUs to Choose for Different Use Cases?
You should always map workload patterns to parts, then apply serving optimizations before scaling hardware.
Real-time chatbots (tight p50)
- You should pick H200/H100 or MI300X for single-GPU fits and add NVLink or tensor parallel only if necessary.
- Long-context workloads benefit from 141–192 GB cards thanks to reduced sharding.
Batchable assistants and tools
- L40S or RTX 6000 Ada excel under dynamic or continuous batching with relaxed latency budgets.
- If used properly, PTQ and sparsity can provide ~1.5× or better throughput improvements in supported paths.
High-throughput serverless endpoints
- You should favor higher-bandwidth HBM parts and enable speculative decoding in TRT-LLM or vLLM to raise tokens/sec.
- Reported gains range up to ~3× depending on model and traffic.
Low-power or edge inference
- We suggest you use T4 or P4 where power budgets are strict and models are smaller or quantized, then scale out horizontally.
Should You Run On-Prem or in the Cloud for Inference?
When deploying AI inferencing workload on GPUs, you should evaluate cost, availability and operational maturity before committing.
Cost and availability
Compare hourly, reserved and spot pricing. Recent data and vendor pricing confirms wide variance for H-class and MI-class GPUs.
Operational maturity
Cloud provides faster access and managed Kubernetes, while on-prem offers control at high utilization. SLAs and networking features like multi-zone VPC and DDoS protection affect reliability. For example, AceCloud provides a 99.99%* SLA with multi-zone networking.
Consider colocation for density and uptime
High-density racks, specialized power and cooling and 24×7 operations can simplify scaling without owning the facility. Industry stats highlights liquid cooling for 40–100 kW racks.
Key Takeaways
There you have it. When selecting the best GPU for AI inferencing, you ultimately win by sizing for memory and bandwidth.
- You should align precision, batching and concurrency with your latency targets first.
- Next, validate throughput with dynamic or continuous batching plus speculative decoding, because software choices often outpace hardware upgrades in impact.
- When capacity or bandwidth becomes the bottleneck, move to HBM parts or NVLink clusters while preserving single-GPU residency where possible.
- Finally, choose deployment that matches utilization and risk, whether on premises, in colocation or in a reliable cloud with SLAs.
Need help choosing the right NVIDIA GPU for AI Inferencing? Connect with our friendly experts by booking a free consultation!
Frequently Asked Questions:
H100 is a strong, widely supported default with NVLink, while H200 often wins for long contexts thanks to higher capacity and bandwidth. MI300X is competitive if your ROCm stack covers your models. A100 remains cost-effective but usually trails H100/H200 but for tighter budgets consider L40/L40S/RTX 6000 Ada or cloud spot (T4/P4 only for smaller/classic workloads).
If you need FP4-class density soon, plan around B200/GB200. Otherwise, deploy now and optimize as most serving gains (quantization, batching, speculative decoding) carry forward.
Expect roughly 141–192 GB for long contexts on a single GPU. If (model + context + KV) cache exceeds that at your latency target, quantize and/or shard. When you must scale beyond one device, NVLink helps reduce cross-GPU penalties.
4090-class cards are great for development and quantized/smaller models, but for production SLAs use data-center GPUs for ECC, reliability, server form factors and features like MIG.
Inference is dominated by memory bandwidth, KV-cache behavior and serving features, while training stresses peak math throughput and interconnect scaling across many GPUs.
Yes, for small models or low QPS, but beyond trivial loads GPUs dominate on both latency and throughput.
Tune dynamic batching to raise throughput while watching tail latency. Combine with quantization and speculative decoding (e.g., TRT-LLM) for multi-X gains. Actual tokens/sec depends on GPU class, precision, context length and serving setup, so benchmark on your stack.
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