What Hardware Is Best Suited for AI Inference Workloads?

GMI Cloud Blog | AI Infrastructure Guide | gmicloud.ai

The best hardware for AI inference depends entirely on what you're running. An LLM serving pipeline has different bottlenecks than a batch image processing job, which has different bottlenecks than a real-time TTS service.

Choosing hardware without profiling your workload first leads to overspending on idle capacity or underprovisioning for demand.

This guide starts from five common inference workload types, maps each to the hardware specs that matter, and lands on specific GPU recommendations.

Infrastructure providers like GMI Cloud offer on-demand H100 and H200 instances alongside a model library for API-based inference.

We focus on NVIDIA data center GPUs; AMD MI300X, Google TPUs, and AWS Trainium are outside scope.

Five Inference Workload Types and What They Demand

Not all inference is the same. Each workload type stresses different hardware specs. Profiling yours correctly is the first step to choosing the right GPU.

1. LLM Serving (Chatbots, Code Assistants, RAG)

LLMs generate tokens one at a time, reading the full parameter set from memory per token. The bottleneck is memory bandwidth. A 70B model at FP8 reads 70 GB per forward pass. Longer outputs (200+ tokens) amplify this: every token adds another full memory read.

What matters most: Memory bandwidth (TB/s), VRAM capacity (to fit model + KV-cache for concurrent users), FP8 support.

2. Batch Image and Video Generation

Diffusion models run 20-50 denoising passes per output, each involving heavy matrix math. Unlike LLMs, the bottleneck is raw compute (FLOPS) rather than bandwidth. Batch jobs can tolerate higher latency per request since they're not user-facing.

What matters most: FP8 TFLOPS, VRAM (for high-resolution outputs), GPU count for parallel batch processing.

3. Real-Time TTS and Audio

TTS models convert text to audio waveforms with strict latency constraints: users expect sub-second response. The models are lighter than LLMs or diffusion models, but real-time requirements mean you can't trade latency for throughput.

What matters most: Low per-request latency, moderate compute, cost-efficiency (TTS runs at high volume for voice applications).

4. Multi-Model Serving

Many production systems serve multiple models simultaneously: a text model, an image model, and a TTS model on the same infrastructure. Without isolation, models compete for VRAM and compute, creating unpredictable latency.

What matters most: MIG support (GPU partitioning), large VRAM (to fit multiple models), workload isolation.

5. Edge and Lightweight Inference

Small models (7B or less) running in power-constrained environments: retail kiosks, on-premise servers, cost-sensitive deployments. Latency matters less than power consumption and hardware cost.

What matters most: Low TDP, sufficient VRAM for small models, PCIe form factor.

Each workload type maps to a specific set of hardware requirements. Here's the matching.

Workload-to-Hardware Matching

Workload (Key Bottleneck / Recommended GPU / Why)

• LLM serving (7B-70B) - Key Bottleneck: Bandwidth - Recommended GPU: H100 SXM - Why: 3.35 TB/s, 80 GB, FP8, MIG
• LLM serving (70B+, long context) - Key Bottleneck: Bandwidth + VRAM - Recommended GPU: H200 SXM - Why: 4.8 TB/s, 141 GB
• Batch image/video generation - Key Bottleneck: Compute - Recommended GPU: H100 or H200 - Why: 1,979 FP8 TFLOPS each
• Real-time TTS - Key Bottleneck: Latency - Recommended GPU: A100 or L4 - Why: Cost-efficient for lighter models
• Multi-model serving - Key Bottleneck: Isolation + VRAM - Recommended GPU: H100/H200 with MIG - Why: Up to 7 partitions per GPU
• Edge / lightweight - Key Bottleneck: Power - Recommended GPU: L4 - Why: 72W TDP, 24 GB, PCIe

Reference: GPU Specs

VRAM

• H100 SXM: 80 GB HBM3
• H200 SXM: 141 GB HBM3e
• A100 80GB: 80 GB HBM2e
• L4: 24 GB GDDR6

Bandwidth

• H100 SXM: 3.35 TB/s
• H200 SXM: 4.8 TB/s
• A100 80GB: 2.0 TB/s
• L4: 300 GB/s

FP8

• H100 SXM: 1,979 TFLOPS
• H200 SXM: 1,979 TFLOPS
• A100 80GB: N/A
• L4: 242 TOPS

TDP

• H100 SXM: 700W
• H200 SXM: 700W
• A100 80GB: 400W
• L4: 72W

MIG

• H100 SXM: Up to 7
• H200 SXM: Up to 7
• A100 80GB: Up to 7
• L4: No

Sources: NVIDIA H100 Datasheet (2023), H200 Product Brief (2024), A100 Datasheet, L4 Datasheet.

Per NVIDIA's H200 Product Brief (2024), the H200 delivers up to 1.9x inference speedup on Llama 2 70B vs. H100 (TensorRT-LLM, FP8, batch 64, 128/2048 tokens). Independent cloud tests confirm 1.4-1.6x in typical production workloads.

Hardware specs are one part of the decision. For production deployments, infrastructure-level factors matter just as much.

Beyond Specs: Infrastructure Considerations

Selecting the right GPU model is necessary but not sufficient. Production inference also depends on factors that don't appear on a datasheet.

Supply Stability

GPU availability fluctuates. During high-demand periods, H100/H200 allocation can take weeks from major hyperscalers. Providers with direct supply chain relationships and pre-provisioned inventory can deliver faster. Evaluate lead times before committing.

Data Sovereignty and Localization

Some workloads require data to stay within specific geographic boundaries. If your inference pipeline processes regulated data (healthcare, finance, government), confirm that your provider offers regional deployment options that satisfy local compliance requirements.

Pre-Configured Software Stack

Setting up CUDA, cuDNN, NCCL, TensorRT-LLM, and vLLM from scratch takes days. Providers that ship pre-configured stacks eliminate this overhead. The difference between "GPU available" and "GPU ready to serve inference" can be significant for time-sensitive projects.

Node Topology

For workloads that span multiple GPUs, inter-GPU communication speed matters. NVLink 4.0 (900 GB/s bidirectional aggregate per GPU on HGX/DGX platforms) enables fast tensor parallelism. InfiniBand (3.2 Tbps inter-node) matters for multi-node deployments. Not all cloud providers offer equivalent interconnect quality.

Elastic Scaling

Inference traffic is rarely constant. Production systems need to scale up during peak hours and scale down to control costs. On-demand GPU instances with per-hour billing provide this flexibility without long-term commitments.

With hardware and infrastructure decided, optimization techniques determine how much performance you actually capture.

Optimization Stack

Quantization. FP8 on H100/H200 halves VRAM usage vs. FP16 with minimal quality loss. This is the single highest-impact optimization for most workloads. It directly increases the model sizes you can fit and the concurrency you can support.

Continuous batching. Inserts new requests into processing slots as they open, instead of waiting for a full batch. Typically delivers 2-3x throughput improvement over static batching. Supported by vLLM and TensorRT-LLM.

Speculative decoding. Uses a smaller draft model to predict multiple tokens, then verifies in one pass on the main model. Delivers 1.5-2x throughput gains for LLM serving without quality loss.

Framework selection. TensorRT-LLM for peak production throughput with NVIDIA-specific optimizations. vLLM for flexible memory management via PagedAttention and broader model support.

Applying This by Role

For Technical Leads

Profile your workload first. Identify whether you're bandwidth-bound (LLM), compute-bound (diffusion), or latency-bound (TTS). Then match to the GPU table above. Run benchmarks on your actual model with FP8 enabled before committing to fleet size.

For Procurement

Calculate total cost of ownership, not just $/GPU-hour. Factor in utilization rate, supply lead time, and pre-configured stack value. Also evaluate API-based inference for workloads under ~10,000 requests/day, where per-request pricing may beat dedicated GPU rental.

For Startup Teams

Start with API-based inference to validate your product and estimate per-request costs. When traffic justifies dedicated hardware, migrate to GPU instances. This avoids upfront hardware commitments before product-market fit.

Getting Started

First, profile your inference workload using the five categories above. Then match to the recommended GPU. Finally, evaluate infrastructure factors (supply, localization, software stack) alongside raw specs.

Cloud platforms like GMI Cloud offer both paths: GPU instances (H100 ~$2.10/GPU-hour, H200 ~$2.50/GPU-hour; check gmicloud.ai/pricing for current rates) for dedicated deployments, and a model library for API-based inference.

Start from your workload, not from the hardware catalog.

FAQ

Should I optimize for bandwidth or FLOPS?

It depends on your workload. LLM serving is bandwidth-bound (choose H200 for maximum bandwidth). Diffusion model inference is compute-bound (H100 and H200 are equivalent on FLOPS). Profile first, then match.

When should I use API-based inference instead of dedicated GPUs?

Below ~10,000 requests/day, per-request API pricing often costs less than dedicated GPU hours. Above that, dedicated instances with optimized serving typically win on unit economics.

How do I handle GPU supply constraints?

Diversify across providers. Prioritize providers with pre-provisioned inventory and direct supply chain relationships. For critical workloads, consider reserved instances to lock in availability.

Does NVLink matter for inference?

For single-GPU inference (models that fit in one GPU's VRAM), no. For tensor-parallel inference across multiple GPUs, NVLink speed directly affects token generation latency. The 900 GB/s NVLink 4.0 on H100/H200 HGX/DGX platforms is significantly faster than PCIe-based multi-GPU setups.

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