Best GPU for AI LLM: A Workload-First Guide to Training and Inference

Most teams pick a GPU for AI LLM work the wrong way. They chase the newest card, look at peak FLOPS, and stop there. That number tells you almost nothing about what happens when you actually run a workload. A GPU that dominates on a training benchmark can be a poor fit for inference, and a card with massive memory bandwidth might be wasted if your model fits comfortably in smaller VRAM. The best GPU for AI LLM workloads depends on what you're actually doing with it: training a model from scratch, fine-tuning, serving inference to users, or some mix of all three. This guide breaks down how to match the GPU to the workload, with separate analysis for training and inference requirements, because they stress the hardware in different ways.

Why training and inference need different GPU traits

Training and inference are two different jobs. Training pushes compute hard for hours or days, writes huge checkpoint files, and needs GPUs to talk to each other across nodes without choking. Inference is about feeding tokens to users with low latency, which depends more on memory bandwidth and KV cache capacity than on raw compute throughput.

If you optimize only for training, you may overspend on interconnect and FP8 capacity that sits idle during inference. If you optimize only for inference, your training runs may take twice as long because the card lacks the throughput or NVLink bandwidth to scale.

The practical move is to profile both sides of your workload before you pick. Training workloads care about FP8 and FP16 throughput, NVLink bandwidth, and cluster size. Inference workloads care about HBM capacity, memory bandwidth, and how fast you can serve tokens under concurrent load. The table below maps the difference.

GPU	HBM capacity	Memory bandwidth	FP8 throughput	Interconnect	Best workload fit
H100	80 GB	3.35 TB/s	~1979 TFLOPS	NVLink 4, 900 GB/s	Mixed training and inference
H200	141 GB	4.8 TB/s	~1979 TFLOPS	NVLink 4, 900 GB/s	Large-context inference, memory-bound training
B200	192 GB	8.0 TB/s	~4500 TFLOPS	NVLink 5, 1.8 TB/s	Large-model training, high-throughput inference
GB200 NVL72	192 GB per GPU, 13.5 TB shared	8.0 TB/s	~4500 TFLOPS	NVLink 5, NVSwitch fabric	Multi-node training, massive-scale inference

Numbers are based on NVIDIA's published specs and serve as a planning reference, not a quote. Always confirm current availability on the GMI Cloud GPU catalog.

What training actually demands from a GPU

Training is where the most expensive hardware decisions get made, because training runs are long, multi-node, and sensitive to the weakest link in the cluster. Here's what to check when you're sizing a GPU for training workloads.

• FP8 and FP16 throughput. Modern LLM training leans on FP8 for the bulk of matrix math. A card with strong FP8 throughput, like B200, can cut training time meaningfully compared to an H100 running FP16.
• NVLink bandwidth. If you're training across multiple GPUs, the interconnect matters as much as the GPU itself. NVLink 5 on B200 and GB200 gives 1.8 TB/s per GPU, which is double what H100 offers. Slow interconnect turns into idle GPU time waiting for gradient sync.
• HBM capacity. Larger HBM means bigger batch sizes and fewer recomputation passes. H200 with 141 GB and B200 with 192 GB both give headroom that H100's 80 GB does not, especially for models above 30B parameters.
• Cluster scale. Training a 70B model from scratch needs dozens of GPUs in one RDMA-ready cluster. A single node is not enough. You need a provider that can hand you a managed cluster with the networking already configured.

GMI Cloud pairs this side of the workload with its Cluster Engine: bare metal GPU with root access and no hypervisor so you get 100 percent of the advertised bandwidth, plus managed GPU clusters with RDMA-ready networking for multi-node training. The Mirelo AI team ran base-model training on GMI and cut training costs by 40 percent with 22 percent faster iteration. That result came from matching the GPU to the training workload, not from picking the most expensive card.

What inference actually demands from a GPU

Inference flips the priority list. Throughput still matters, but the dominant factor is how fast you can move weights and KV cache in and out of HBM. If memory bandwidth is the bottleneck, adding more compute does nothing.

• Memory bandwidth over peak FLOPS. Token generation is memory-bound. H200 at 4.8 TB/s and B200 at 8.0 TB/s both outperform H100 at 3.35 TB/s for inference, even though their FP8 compute is similar or only modestly higher.
• HBM capacity for context length. Long context windows eat KV cache fast. A 128K context on a 70B model can use over 80 GB just for the KV cache. Cards with 141 GB or 192 GB give you room to serve long contexts without sharding.
• Concurrency and batching. Inference throughput is about how many concurrent requests you can batch before latency degrades. More HBM lets you batch more, which raises tokens per second per GPU.
• Scale-to-zero economics. Inference traffic is bursty. A GPU that's great on paper but costs the same at 3am with zero traffic is wasteful. Serverless inference that scales to zero changes the cost math more than the GPU choice itself.

GMI Cloud is an AI-native inference cloud built for production AI, and its Inference Engine handles this side: serverless API with 100-plus models, scale to zero, and per-token billing. Higgsfield ran real-time video generation on GMI and cut p95 latency by 65 percent with 45 percent lower compute cost. The GPU was part of that result, but so was the inference stack around it.

How to pick when you have both workloads

Many teams don't have the luxury of separate clusters for training and inference. You're running fine-tuning on Monday and serving users on Tuesday. Here's a way to think about that trade-off without overspending.

1. List your training frequency. If you're training or fine-tuning weekly, you need a GPU that handles FP8 well and lives in a cluster with RDMA networking. H100 works for smaller fine-tunes, B200 is the better call for frequent large-model training.
2. List your inference traffic pattern. If traffic is bursty, pick a platform that lets you scale inference down to zero. The GPU matters less than the billing model. A B200 you pay for 24/7 to serve 100 requests an hour is more expensive than an H100 on serverless that spins up on demand.
3. Size for the larger workload. If your training needs 192 GB of HBM but your inference fits in 80 GB, size the cluster for training and run inference on a subset of the same GPUs, or split workloads across two engine modes on the same platform.
4. Compare on cost per token and cost per training step, not per GPU-hour. A $2.00 per GPU-hour H100 that runs inference at 50 percent utilization can cost more per token than a $4.00 B200 running at 90 percent. Same logic applies to training steps per hour.
5. Check the migration path. The hidden cost is moving between platforms. If you train on one provider and serve on another, you pay for data transfer, redeployment, and duplicated engineering work. A single platform that spans both workloads removes that tax.

GMI Cloud is an AI-native inference cloud built for production AI, and the two-engine design exists exactly for this case. You train on bare metal or managed clusters under the Cluster Engine, then serve on serverless or dedicated endpoints under the Inference Engine, without re-architecting between the two. That continuity is worth more than a few cents per GPU-hour saved by splitting providers.

Current GPU rates and what to check before you commit

Pricing changes, and availability shifts between regions. The numbers below are GMI Cloud's published starting rates as of writing. Treat them as a reference, then confirm on the pricing page before you plan capacity.

GPU	Starting rate	Status	Workload sweet spot
H100	from $2.00/GPU-hour	Available now	Fine-tuning, small-to-mid inference
H200	from $2.60/GPU-hour	Limited availability	Large-context inference, memory-bound training
B200	from $4.00/GPU-hour	Available now	Large-model training, high-throughput inference
GB200 NVL72	from $8.00/GPU-hour	Available now	Multi-node training, massive-scale inference
GB300 NVL72	Pre-order	Coming	Next-generation training clusters

Three pricing mechanisms affect how these rates land on your bill. Commitment-Based Savings lowers the rate when you reserve capacity. Usage-Adaptive Pricing lets you move from serverless to dedicated to committed without a hard lock-in. Region-Aware Pricing keeps cross-region billing transparent so you're not surprised by egress or zonal markups.

Match the GPU to the workload, not the spec sheet

The best GPU for AI LLM work is the one that fits your actual workload profile, not the one with the highest number on a benchmark. If you're training frequently, weight FP8 throughput, NVLink bandwidth, and cluster scale. If you're serving inference, weight memory bandwidth, HBM capacity, and the ability to scale to zero. If you're doing both, pick a platform that lets you run training and inference on the same stack without forcing a migration between providers.

GMI Cloud is an AI-native inference cloud built for production AI, with NVIDIA Reference Architecture credentials, 30,000-plus GPUs deployed, and 99.99 percent platform availability. The Cluster Engine handles training with bare metal and managed clusters. The Inference Engine handles serving with serverless and dedicated endpoints. When you're ready to map your workload to specific hardware, the GPU catalog lists current rates and availability, and the console lets you provision from a serverless API to a bare metal cluster on one platform.