Picking a cloud GPU for Generative AI usually means matching VRAM and throughput to your model and runtime behavior, not chasing a brand name. This is because most modern flagship GPUs differ sharply in memory bandwidth and Tensor Core performance, which significantly changes tokens/sec and images/min.
For example, NVIDIA H100 offers bandwidth up to 3.35 TB/s and up to 3.9 TB/s on some variants, plus FP8 Tensor performance up to 3,958 TFLOPS. To help you make the best decision, this guide gives you a practical decision framework you can apply across providers and instance catalogs.
What Determines Whether a GPU Instance Suits Your GenAI Workload?
Indeed, you can pick faster GPUs later. But you should first confirm your workload fits and stays stable under load. Here’s what you should consider:
- VRAM capacity determines whether weights, activations and caches can stay on GPU without offloading. Offloading shifts work to system memory or storage, which adds latency and lowers throughput for every request.
- Memory bandwidth often gates training and high-throughput inference because reads and writes can dominate compute. When kernels stall on memory, extra TFLOPS do not help until bandwidth improves.
- Tensor Core capability and precision support matter because BF16, FP16 and FP8 change speed and memory usage. Lower precision works when numerics remain stable, which you should validate with accuracy tests for your model.
- Interconnect matters when you need more than one GPU per node and want efficient parallel training. If GPUs exchange activations slowly, scaling adds cost without adding throughput.
Common tiers to consider
- 48GB-class GPUs are often a practical tier for heavier diffusion pipelines and mid-sized LLM serving. The RTX A6000 datasheet lists 48GB GDDR6 and 768 GB/s memory bandwidth, which is enough for many single-GPU workflows. The L40S is another common option and NVIDIA lists FP8 at 1,466 TFLOPS, FP16 at 733 TFLOPS and 350W max power.
- 80GB-class GPUs are common when you want larger LLMs, higher concurrency or serious fine-tuning. NVIDIA lists A100 80GB HBM2e bandwidth up to 2,039 GB/s, which supports higher batch sizes and faster data movement. NVIDIA lists H100 with 80GB and 94GB variants, which expands your options for weights and KV cache.
What most teams miss?
Your bottleneck changes by workload, which means one “best GPU” does not exist for every team. Diffusion is commonly VRAM-bound, LLM serving is VRAM plus KV-cache bound and training adds bandwidth and interconnect pressure. In our opinion, you should profile one realistic run first, then adjust one constraint at a time instead of changing everything together.
Which Cloud GPU Instance Type Suits Stable Diffusion Models?
In our experience, diffusion workloads feel fast when the full pipeline stays on GPU and avoids repeated transfers. Here’s what you can do to achieve the same:
- You typically want the text encoder, the UNet or transformer, the VAE and safety steps resident in VRAM.
- Each GPU-to-CPU transfer adds synchronization points, which reduces images per minute even when compute remains available.
- Tensor throughput matters. However, VRAM is the first gate you must clear.
- If VRAM is insufficient, optimizations usually trade quality, latency or engineering effort rather than fixing the root constraint.
What VRAM tier to target for Stable Diffusion style workloads?
- A useful baseline is “24GB or more” for heavier modern pipelines, especially when you want higher resolutions and batching.
- Hugging Face Diffusers notes SD3 uses three text encoders including T5-XXL, which makes it challenging under 24GB VRAM even with fp16.
- You can sometimes run with less VRAM using attention slicing, CPU offload or quantization.
- However, those techniques often reduce throughput because they add extra memory movement or reduce kernel efficiency.
Our recommendation
- Creator and prototyping usually works best on a single GPU with comfortable VRAM, then iterate on quality settings. This approach reduces distributed complexity and makes failures easier to debug and reproduce.
- High-throughput inference often starts with one strong single GPU, then scales out with more replicas behind a queue. Scale-out helps because diffusion requests are independent, which makes horizontal parallelism efficient for most serving stacks.
- Training diffusion or LoRA at scale can justify multi-GPU nodes when your trainer benefits from fast GPU-to-GPU exchange. You should confirm scaling efficiency with a short multi-GPU run, since poor communication can erase expected speedups.
Best Cloud GPU for Stable Diffusion
In our opinion, L40 Sand RTX A6000 are two cloud GPUs best suited for stable image diffusion workload. L40S is commonly used for “AI plus graphics” style pipelines, where both tensor and rendering features matter. On the other hand, RTX A6000 provides 48GB and 768 GB/s bandwidth, which can be a strong fit for memory-heavy single-GPU diffusion work.
Which Cloud GPU Instance Type Suits LLM Inference and Serving?
LLM serving usually fails first on memory planning, then succeeds after you treat VRAM like a budget you must allocate.
- You pay VRAM for model weights and you also pay VRAM for KV cache, which grows with context and concurrency. Even if weights fit, KV cache can push you into out-of-memory when you raise max tokens or parallel requests.
- A practical workflow is to estimate VRAM needs before you pick an instance type. For weights, you can approximate bytes as parameters times bytes-per-parameter under your chosen precision or quantization.
What serving engines do with GPU memory and why it matters?
- vLLM exposes –gpu-memory-utilization and its guide states the default is 0.9 when you do not specify a value. This cap matters because it leaves headroom for fragmentation, runtime allocations and imperfect memory profiling.
- Community guide also warns against setting it to 1.0 because OOM errors can occur, while 0.9 to 0.95 is often safer. You should treat this as an operational control, then tune it using load tests that match your real prompt lengths.
When to choose GPU partitioning (MIG) vs a whole Cloud GPU?
GPU partitioning helps when you have many small models, multiple endpoints or low-QPS services that waste a full GPU. NVIDIA explains MIG can partition supported GPUs into as many as seven isolated instances with their own memory and cache.
Whole-GPU serving fits best when one model needs large KV cache, high batch sizes or stable low latency. In that case, slicing can increase scheduling overhead and reduce peak throughput for the primary model.
Our recommendation
- If one model is your priority, you should usually pick a single GPU with enough VRAM for weights and KV cache.
- If utilization is low across many endpoints, you can use MIG slices or smaller GPUs to raise fleet efficiency.
- If the model fits but is slow, you should move to higher bandwidth or newer Tensor Core support, then retest tokens per second.
Which Cloud GPU Instance SuitsFine-Tuning and Training LLMs?
Training decisions get easier when you separate compute limits from data movement limits, then measure which one dominates.
- GPU memory bandwidth matters because training repeatedly streams activations, gradients and optimizer states. If bandwidth is insufficient, GPUs idle while waiting for data, which reduces your effective utilization.
- Multi-GPU communication matters when you use tensor parallelism, pipeline parallelism or large all-reduce operations.
- Storage and checkpoint throughput also matters as slow writes can stall epochs and extend wall-clock time.
When to choose multi-GPU nodes?
Multi-GPU nodes become worthwhile when your job spends enough time in parallel communication to justify fast interconnect.
- At AceCloud, we provide up to 8 x H100 GPUs and up to 900 GB/s NVSwitch interconnect, totaling up to 3.6 TB/s bisectional bandwidth.
- Similarly, Microsoft offers ND H100 v5 starting with 8 x H100 GPUs and up to 3.2 Tbps interconnect bandwidth per VM.
Ideally, you should still validate scaling using one representative training run, then compute dollars per training token. If scaling efficiency is low, fewer GPUs can finish earlier because they avoid communication overhead.
Our recommendation
A single 80GB GPU is often enough for fine-tuning, evaluation and experimentation when you use efficient methods. You should move to multi-GPU only after profiling shows stable high utilization and meaningful scaling from parallelism.
Which Cloud GPU Instance Type Suits Video Generation?
Video generation changes the memory profile because you manage more frames, longer runtimes and larger intermediate buffers.
- Video pipelines need temporal consistency, which often adds extra conditioning and more intermediate state.
- More frames also means more total compute, which increases both latency and the chances of memory fragmentation.
Stability AI’s Stable Video Diffusion work includes variants trained for 14 frames at 576×1024 and 25 frames at 576×1024. These targets help you reason about memory, since more frames generally increase activation storage and runtime buffers.
Prioritize instance flexibility as video gen moves fast
Some approaches try to reduce VRAM needs, which can lower the entry cost for experimentation. For example, the FramePack repository claims a minimal 6GB GPU memory requirement for generating a 60-second video at 30fps using a 13B model.
Tom’s Hardware reports and mentions around 0.6 frames per second on an RTX 4090 in one setup. You should treat these as workload-specific results, then validate speed and quality on your own prompts.
Many teams start on cheaper GPUs for iteration, then move to higher VRAM for higher resolution and more stable production runs.
Our recommendation
In our opinion, you should prefer higher VRAM when you want higher resolution, longer clips or higher concurrency. At the same time, you should prefer higher bandwidth when your profiling shows memory stalls, especially during attention-heavy parts of the pipeline.
Make Better Decisions with AceCloud Experts
There you have it. Choosing a cloud GPU gets easier when you treat it as a fit-and-bottleneck problem, not a brand decision.
- You should first confirm the full runtime fits in VRAM, then measure whether bandwidth, Tensor Core support, or interconnect is limiting throughput.
- After that, you can scale out replicas for serving or scale up to multi-GPU nodes for communication-heavy training.
If you want help validating the right tier for Stable Diffusion, LLM serving, or video generation, you can connect with AceCloud cloud GPU experts. Just book a free consultation session and run your workloads using free credits worth INR 20,000!
Frequently Asked Questions
Most of the time, yes. If weights and runtime buffers do not fit, performance collapses due to offloading and repeated transfers.
For heavier pipelines like SD3, Diffusers notes it can be challenging under 24GB VRAM without memory optimizations.
KV cache grows with context length and concurrent requests, which can exceed VRAM even when weights load successfully.
Start with –gpu-memory-utilization, since vLLM documents a default of 0.9 and guidance often recommends 0.9 to 0.95.
Pay for them when training or model-parallel jobs can use fast interconnect, such as AWS P5’s NVSwitch interconnect design.
Sometimes. FramePack claims 6GB minimum VRAM for certain workflows, but you should validate speed and quality for your prompts.
