The "Best" GPU Changes Depending on What You're Generating

There's no single "best" GPU for AI workloads. The optimal choice depends entirely on where your bottleneck lives: memory bandwidth for text inference, compute throughput for image generation, or VRAM capacity for video. Understanding these distinctions cuts infrastructure costs and prevents overprovisioning.

This article covers: how to match workload type to GPU bottlenecks, selection rules for text, image, video, and multimodal workloads, cost comparisons with GMI Cloud's inference engine alternatives, and a decision framework for your team.

Text Generation: It's All About Memory Bandwidth

Text inference splits into two phases: the prefill phase (which is compute-bound) and the decode phase (which is memory-bandwidth-bound). The decode phase dominates latency in production, making memory bandwidth the critical constraint.

The H200 SXM delivers 4.8 TB/s bandwidth compared to the H100's 3.35 TB/s. For models above 70B parameters with long context windows, this difference compounds quickly. However, small models like Llama 3.1 7B don't saturate even the L4's 300 GB/s.

A suggested selection approach: models under 7B parameters work efficiently on L4 GPUs (roughly $0.30 per hour). Models between 7B and 70B typically fit on H100s (80GB HBM3). Models exceeding 70B parameters, especially with 32K+ context lengths, benefit from H200's 141GB HBM3e.

One way to estimate fit is comparing model weights plus key-value cache requirements. A rough formula suggests that if model weights plus KV-cache exceeds 80GB, an H200 becomes necessary. Below that threshold, an H100 usually suffices for single-request throughput.

Image Generation: Compute Over Memory Bandwidth

Diffusion models require many sequential denoising steps, each step running matrix operations across the image tensor. This makes compute throughput (TFLOPS) the bottleneck, not bandwidth. Both H100 and H200 share identical FP8 performance at 1,979 TFLOPS, so they deliver identical speed for single-image generation.

The H200's advantage appears when batching images or generating larger resolutions. Batching four simultaneous 512×512 images consumes roughly 60GB VRAM. A single 1024×1024 image approaches 80GB. H100's 80GB limit becomes tight, while H200's 141GB provides safety margin.

For teams comfortable with serverless, skipping GPU infrastructure altogether is worth exploring. Serverless platforms include seedream-5.0-lite at $0.035 per request, handling most batch sizes without manual provisioning. The math often favors pay-per-use over reserved capacity for unpredictable image workloads.

Video Generation: VRAM Capacity Is the Hard Constraint

Video generation loads multiple frames into VRAM simultaneously, multiplying memory requirements. A 5-second 720p video at 24 fps requires roughly 120 frames in the diffusion pipeline, consuming approximately 50GB on the H100.

The same 5-second clip at 1080p pushes into 80-100GB territory, requiring the H200. Longer durations or higher resolutions demand multi-GPU setups. A 10-second 1080p video typically needs at least two H200 GPUs coordinated via NVLink.

Alternatively, serverless remains competitive. Available models include Kling V2.6 at $0.07 per request for basic video, Wan 2.6 at $0.15 per request for higher quality, and Sora-2 at $0.10 per request. The break-even point depends on your generation frequency and whether keeping GPUs idle between requests costs more than the per-request fee.

Audio and Multimodal: Mixed Bottleneck Patterns

Text-to-speech is memory-efficient; most TTS models fit comfortably on L4s (24GB GDDR6). The constraint becomes throughput: how many concurrent requests can one GPU handle. An L4 processes roughly 10-20 concurrent requests depending on target latency.

Multimodal pipelines chain multiple single-modality models: image encoding, text embedding, fusion, then generation. Each stage has different optimal hardware. One approach is sizing each stage independently rather than forcing all stages onto identical GPU types.

For organizations preferring managed inference, available TTS services include elevenlabs-tts-v3 at $0.10 per request and inworld-tts-1.5-mini at $0.005 per request, which removes the need to manage audio-specific GPU infrastructure. This simplifies multimodal orchestration.

Workload-GPU Decision Table

The table below maps common workloads to their primary bottleneck, recommended GPU, reasoning, and cost implications.

Workload	Bottleneck	Best GPU	Why	$/hour	Serverless Alternative
Llama 3.1 7B chat	Memory BW	L4	Saturates 300 GB/s fully	~$0.30	N/A
Llama 3.1 70B chat	Memory BW	H100	Fits 80GB, saturates 3.35 TB/s	~$2.10	N/A
Llama 3.1 405B chat	Memory BW	H200	Needs 141GB, benefits from 4.8 TB/s	~$2.50	N/A
Stable Diffusion 3.5 (single)	TFLOPS	H100 or H200	Identical speed, 1,979 TFLOPS	~$2.10–2.50	seedream-5.0-lite @ $0.035/req
Stable Diffusion 3.5 (batch 4+)	VRAM	H200	Batch consumes 60GB+	~$2.50	seedream-5.0-lite @ $0.035/req
Kling video (5s 720p)	VRAM	H100	50GB required	~$2.10	Kling V2.6 @ $0.07/req
Kling video (10s 1080p)	VRAM	2x H200	160GB+ distributed	~$5.00	Kling V2.6 @ $0.07/req
ElevenLabs TTS (10 concurrent)	Throughput	L4	Lightweight, throughput-driven	~$0.30	elevenlabs-tts-v3 @ $0.10/req

Don't want to manage GPU capacity, monitoring, or autoscaling infrastructure in-house? The rightmost column shows inference engine alternatives for each workload, allowing teams to shift operational burden to managed platforms.

GMI Cloud Infrastructure: Pre-Configured Inference Stack

For teams choosing dedicated GPUs, GMI Cloud provides H100 SXM (80GB HBM3, 3.35 TB/s) and H200 SXM (141GB HBM3e, 4.8 TB/s) with pre-configured inference engines. The platform includes TensorRT-LLM, vLLM, and Triton inference server, CUDA 12.x, and NVLink 4.0 connectivity (900 GB/s bidirectional per GPU within nodes).

Pre-configured stacks reduce common deployment friction: CUDA compatibility, kernel version mismatches, and driver conflicts. This typically shortens time-to-first-inference, though teams should verify that the pre-installed versions match their model requirements. The time saved on infrastructure setup compounds when multiple engineers are onboarding simultaneously.

Check gmicloud.ai/pricing for current rates, as GPU pricing fluctuates with market demand.