Which GPU Hardware Offers the Best Performance for AI Inference Workloads?

The H100 SXM and H200 SXM lead across all major AI inference workload types, and that's true whether you're running LLMs, image generation, video synthesis, or speech models.

The right choice between them depends on your specific modality, model size, and whether VRAM capacity or memory bandwidth is your primary bottleneck.

If you're managing multiple AI workload types and want a single infrastructure decision that covers all of them, GMI Cloud's GPU instances give you H100 and H200 SXM with pre-configured software stacks and flexible on-demand or reserved pricing.

AI Inference Is Not One Workload

Most GPU selection guides focus exclusively on LLMs. That misses a big part of what modern AI inference actually looks like. The majority of AI-powered applications today combine multiple modalities: a product might use an LLM for text reasoning, a diffusion model for image generation, and a TTS model for audio output.

Each of those modalities has different GPU demands. LLM text inference is memory-bandwidth-bound during the decode phase and increasingly VRAM-constrained as context lengths grow. Image generation with diffusion models is GPU compute-intensive during the denoising steps, with moderate VRAM requirements.

Video generation compounds those demands with temporal consistency across frames and large intermediate tensor sizes. Audio and speech synthesis tends to be lighter on VRAM but sensitive to low-latency serving.

Understanding the profile of each workload type is how you pick the right hardware, not by looking at GPU specs in isolation.

AI Inference Workload Taxonomy

Here's how the four major inference modalities break down by GPU demand:

LLM Text Inference: Memory-bandwidth-bound during decode. VRAM requirements scale with model size (roughly 2 bytes per parameter in FP16). KV-cache pressure grows with context length and batch size. FP8 compute matters for prefill throughput.

Multi-GPU NVLink is essential for models that don't fit on a single card.

Image Generation (Diffusion Models): Compute-intensive during denoising steps (typically 20-50 steps per image). VRAM requirements are moderate (8-24 GB for most production models). High FP16/FP8 TFLOPS improve throughput directly. No sequential KV-cache pressure, unlike LLMs.

Video Generation: Similar to image generation but with much larger tensor sizes due to temporal frames. Models like Wan2.1 and Sora require 24-80 GB VRAM depending on resolution and duration. Long generation times mean higher compute requirements per output.

Memory bandwidth matters for reading and writing large frame buffers.

Audio/Speech (TTS and ASR): Typically the lightest of the four on VRAM (4-16 GB for most production models). Latency-sensitive for real-time TTS. Throughput is less extreme than LLMs or video. FP16 or INT8 precision is standard. Can often run efficiently on mid-tier hardware.

Full GPU Spec Comparison

Rank	GPU	VRAM	Memory BW	FP16 TFLOPS	FP8 TFLOPS	NVLink	TDP	MIG
#1	H100 SXM	80 GB HBM3	3.35 TB/s	989	1,979	900 GB/s bidir. agg./GPU (HGX/DGX)	700W	Up to 7
#2	H200 SXM	141 GB HBM3e	4.8 TB/s	989	1,979	900 GB/s bidir. agg./GPU (HGX/DGX)	700W	Up to 7
#3	A100 80GB	80 GB HBM2e	2.0 TB/s	312	N/A	600 GB/s	400W	Up to 7
#4	L4	24 GB GDDR6	300 GB/s	121	242	None (PCIe)	72W	No
#5	B200 (est.)	192 GB HBM3e (est.)	8.0 TB/s (est.)	N/A	~4,500 (est.)	1,800 GB/s (est.)	~1,000W (est.)	TBD

Sources: NVIDIA H100 Tensor Core GPU Datasheet (2023); NVIDIA H200 Tensor Core GPU Product Brief (2024); NVIDIA A100 Tensor Core GPU Datasheet; NVIDIA L4 Tensor Core GPU Datasheet. B200 specs are estimates based on GTC 2024 disclosures.

Workload-Specific GPU Recommendations

Workload	Best GPU	Runner-Up	Avoid	Key Reason
LLM, large models (70B+, FP16)	H200 SXM	Multi-GPU H100	L4, A100 single	VRAM + bandwidth for decode
LLM, medium models (7B-70B, FP8)	H100 SXM	H200 SXM	L4	FP8 support, cost-efficient
Image generation (SD XL, Flux)	H100 SXM	A100 80GB	L4 (OK for small)	High FP16 TFLOPS for denoising
Video generation (Wan2.1, Sora)	H200 SXM	H100 SXM (multi)	A100 (borderline)	VRAM + bandwidth for large tensors
Audio/TTS (small-medium models)	H100 SXM	A100 80GB	—	Balanced compute and VRAM
High-volume low-latency audio	H100 SXM	L4 (small models)	—	Throughput at reasonable cost

Key Selection Criteria Across All Modalities

There are four hardware characteristics that determine fit across any inference workload. Use these as your checklist before selecting GPU hardware.

1. VRAM fit: Can your model's weights, intermediate activations, and cache structures (for LLMs: KV-cache; for diffusion: latent tensors) all fit simultaneously in VRAM? If not, you'll either need a larger-VRAM GPU or multi-GPU tensor parallelism. VRAM is the non-negotiable first filter.

2. Memory bandwidth: For workloads that are bandwidth-bound (LLM decode, high-resolution video frame reads), bandwidth directly determines throughput. The H200's 4.8 TB/s versus H100's 3.35 TB/s and A100's 2.0 TB/s translate into measurable tokens-per-second and frames-per-second differences.

NVIDIA's benchmarks show H200 delivering up to 1.9x inference speedup over H100 on Llama 2 70B (NVIDIA H200 Tensor Core GPU Product Brief, 2024, TensorRT-LLM, FP8, batch 64, 128/2048 tokens).

3. MIG support: Multi-Instance GPU lets you partition one physical GPU into up to 7 isolated instances. This is valuable for multi-tenant environments or workloads where you're serving multiple small models from a single card. The H100 and H200 both support MIG with up to 7 instances.

The L4 does not support MIG.

4. NVLink for multi-GPU: When a model doesn't fit on a single GPU, NVLink bandwidth determines how fast the GPUs can share data during tensor-parallel inference. H100 and H200 both deliver 900 GB/s bidirectional aggregate per GPU on HGX/DGX platforms. The A100 runs 600 GB/s.

The L4 has no NVLink at all, making it a poor choice for any multi-GPU configuration.

Cost-Performance Framework

Price-to-performance analysis looks different for each workload type. Here's how to approach it consistently.

For LLM text inference, the right unit is $/output-token at your target batch size and context length. Measure actual token throughput, divide hourly cost by tokens-per-second. For image generation, use $/image at your target resolution and step count. For video, use $/second-of-video.

For audio, use $/minute-of-speech.

Don't optimize for lowest hourly rate. A GPU that delivers 2x throughput at 1.2x the hourly cost gives you a better $/unit-output. The L4 has a low sticker price but low throughput; for most production workloads, its effective $/output is not competitive with H100 at scale.

Here's a rough framework for GPU selection by workload volume and budget:

Volume Level	LLM	Image Gen	Video Gen	Audio/TTS
High volume, quality-first	H200 SXM	H100 SXM	H200 SXM	H100 SXM
Medium volume, balanced	H100 SXM	H100 SXM	H100 SXM	A100 80GB
Low volume, cost-sensitive	A100 80GB	A100 80GB	H100 SXM	L4

Infrastructure for Multi-Workload AI Inference

If you're running multiple modalities from a single infrastructure, you want a platform that handles all of them without forcing different vendors for different workload types.

GMI Cloud's Inference Engine includes 100+ pre-deployed models across text, image, video, and audio modalities accessible via API with no GPU provisioning required.

Current models include video generation with wan2.6-t2v ($0.15/video, featured), image generation with seedream-5.0-lite ($0.035/image, featured), and text-to-speech with minimax-tts-speech-2.6-hd ($0.10/request, featured). Pricing runs from $0.000001 to $0.50 per request depending on model.

Browse the full list in the model library.

For teams that need dedicated GPU capacity for custom models, H100 SXM runs at approximately $2.00/GPU-hour and H200 SXM at approximately $2.60/GPU-hour. Check gmicloud.ai/pricing for current rates on both on-demand and reserved configurations.

Data source: GMI Cloud Inference Engine page, snapshot 2026-03-03; check gmicloud.ai for current availability and pricing.

Frequently Asked Questions

Q: Do I need different GPUs for different AI workload types? A: Not necessarily. The H100 and H200 SXM are strong across all major modalities.

In practice, most teams use one GPU family for everything and accept slightly suboptimal efficiency for some workloads rather than managing separate hardware pools.

Q: Is image generation more compute-bound or memory-bandwidth-bound? A: Diffusion model denoising is more compute-intensive than LLM decode. Each denoising step performs dense convolutions or attention operations at high arithmetic intensity, making it more compute-bound.

High FP16 TFLOPS matter more than memory bandwidth for diffusion models.

Q: How much VRAM does Stable Diffusion XL need? A: Around 8-10 GB in FP16 for the base model at 1024x1024 resolution. With LoRA adapters and VAE loaded simultaneously, budget 12-16 GB. The L4 (24 GB) can handle it, but H100/A100 give you headroom for batching.

Q: What about running LLMs and image generation on the same GPU node? A: It's possible with MIG (on H100/H200) or through time-sharing. MIG partitions the GPU into isolated instances with dedicated VRAM slices, so you can run a 13B LLM and a diffusion model on separate MIG instances of one H100.

Throughput is lower than a dedicated GPU, but utilization is much better for variable traffic.

Q: Does the A100 support FP8 for image or video generation? A: No. The A100 lacks native FP8 hardware support. It uses INT8 (624 TOPS) for quantized inference. For FP8-optimized pipelines (which are increasingly the standard for production deployments), you need H100 or H200.

Q: How do I evaluate a GPU for video generation specifically? A: Focus on VRAM capacity first (most production video models need 24-80 GB), then memory bandwidth (large frame tensors need fast reads and writes), then FP16 TFLOPS for the compute-intensive generation steps.

The H200 SXM covers all three with the most headroom for high-resolution, long-duration video.

Q: What's the roadmap beyond H200? A: The B200 specifications are estimates based on GTC 2024 disclosures. Projected at 192 GB HBM3e (est.) and 8.0 TB/s memory bandwidth (est.), it would substantially improve video and LLM inference.

NVLink 5.0 at 1,800 GB/s (est.) would also double multi-GPU communication speed. Production availability and confirmed third-party benchmarks are pending. Plan around H100/H200 for 2025 deployments.