Best GPU for LLM: A Selection Framework by Model Size

The best GPU for LLM workloads is not the fastest card on the market. It's the card that fits your model size, your traffic pattern, and your cost ceiling without leaving memory or bandwidth stranded. Most teams pick a GPU the wrong way: they look at peak FLOPS, pick the top number, and then discover at deployment that the card doesn't have enough HBM to hold the model weights, or that the interconnect can't keep up with multi-GPU tensor parallelism. A GPU for LLM inference and training has to be evaluated on four variables together: memory capacity, memory bandwidth, interconnect, and cost per token. This guide gives you a framework for working through those variables and mapping them to the model you actually need to run.

What a GPU for LLM actually needs

Running a large language model is memory-bound before it's compute-bound. The GPU has to hold the model weights, the KV cache, and the activation buffers, and it has to move them through memory fast enough to hit your target latency. Peak TFLOPS matters for training throughput, but for inference the bottleneck is almost always how fast you can stream weights from HBM to the processing cores.

Four specs determine whether a card is a good GPU for LLM work:

• Memory capacity (HBM): The total VRAM available to hold weights, KV cache, and activations. A 70B parameter model in FP16 needs roughly 140 GB just for weights, before you allocate any KV cache. If the card doesn't have enough HBM, you're forced into multi-GPU sharding, which adds interconnect overhead.
• Memory bandwidth: How fast data moves from HBM to compute. This is the single biggest factor in inference latency. A card with 3 TB/s bandwidth serves tokens faster than one with 2 TB/s, even if both have the same compute.
• Interconnect (NVLink / PCIe): How GPUs talk to each other when a model spans multiple cards. NVLink gives 900 GB/s between GPUs; PCIe Gen5 gives 64 GB/s. For tensor parallelism across 8 GPUs, that gap is the difference between linear scaling and a 40 percent efficiency drop.
• Cost per token: The number that actually matters. A cheaper GPU-hour on a card that produces fewer tokens per second can cost more per token than a pricier hour on a faster card.

Here's how the current NVIDIA data center GPUs compare on those four dimensions:

GPU	HBM capacity	Memory bandwidth	Interconnect	Approx. hourly cost
H100 SXM	80 GB	3.35 TB/s	NVLink 900 GB/s	from $2.00
H200 SXM	141 GB	4.8 TB/s	NVLink 900 GB/s	from $2.60
B200	192 GB	8.0 TB/s	NVLink 1.8 TB/s	from $4.00
GB200 NVL72	192 GB per GPU	8.0 TB/s	NVLink 1.8 TB/s	from $8.00

Pricing reflects GMI Cloud's current rates and should be verified on the GMI Cloud pricing page before procurement decisions.

Match the GPU to model size

Model size is the first filter. A 7B model and a 405B model have completely different GPU requirements, and picking a card without sizing it to the model first is the most common mistake in GPU selection.

7B to 13B models

Models in the 7B to 13B range (Llama 3 8B, Mistral 7B, Qwen 7B) fit comfortably on a single 80 GB card. Weights in FP16 take 14 to 26 GB, leaving 50-plus GB for KV cache and batch. A single H100 SXM handles these models well: 3.35 TB/s bandwidth is enough for sub-100ms time-to-first-token at reasonable batch sizes, and you avoid interconnect overhead entirely because nothing is sharded.

For lower-throughput prototypes, a single H100 is often more than enough. For production traffic with high QPS, you might run multiple H100s, each serving a replica, rather than sharding one model across GPUs. That keeps latency low and simplifies ops.

70B models

A 70B model in FP16 needs about 140 GB for weights alone. No single H100 has that much HBM, so you're looking at multi-GPU setups. Two H100s with NVLink give you 160 GB of combined HBM, enough for weights plus a reasonable KV cache. Two H200s give you 282 GB, which means a much larger KV cache and higher concurrency before you hit OOM.

This is where interconnect starts to matter. Tensor parallelism across two GPUs splits each layer's computation and requires constant communication between cards. NVLink at 900 GB/s keeps that overhead low. If you tried the same setup over PCIe, you'd see throughput drop noticeably because the interconnect becomes the bottleneck.

For 70B inference, H200 is the stronger choice when you need high concurrency or longer context windows. H100 works if you're willing to shard and keep context moderate. Both are viable; the decision depends on your throughput target.

405B and frontier models

Models like Llama 3 405B and other frontier-scale models need 800-plus GB of VRAM just for FP16 weights. That means a minimum of 8 H200s (1.1 TB combined) or 8 B200s (1.5 TB combined). At this scale, the interconnect is not optional, it's the system. B200's NVLink at 1.8 TB/s gives near-linear scaling across 8 GPUs, while H200's 900 GB/s still works but with more overhead per node.

For 405B, the B200 is the clear pick when budget allows. The combination of 192 GB HBM, 8 TB/s bandwidth, and 1.8 TB/s NVLink means fewer GPUs, less sharding overhead, and better throughput per dollar at frontier scale. The GB200 NVL72 system extends this to 72-GPU pools with a shared NVLink domain, designed for exactly this class of workload.

A framework for choosing

Working through the GPU decision in the right order prevents overspending on a card you don't need or underprovisioning one that can't serve your model. Here's the sequence:

1. Size the model to memory. Calculate FP16 weight size (parameters times 2 bytes), add KV cache for your target concurrency and context length, and add 20 percent headroom for activations. That gives you minimum HBM. If it fits on one card, stop here. If not, figure out how many GPUs you need.
2. Check bandwidth against your latency target. For inference, time-to-first-token is roughly proportional to weights divided by bandwidth. A 70B model on a 3.35 TB/s H100 moves weights in about 42ms. On a 4.8 TB/s H200, that drops to 29ms. If your latency target is below 50ms, bandwidth may dictate the card choice more than HBM does.
3. Evaluate interconnect for multi-GPU setups. If the model spans GPUs, NVLink is mandatory for production. PCIe works for prototyping but loses 30 to 40 percent of effective throughput under tensor parallelism. Count the NVLink bandwidth as part of the system spec, not an afterthought.
4. Compare on cost per token, not cost per GPU-hour. A $2.00/H100-hour that serves 2,000 tokens/second costs less per token than a $1.50 card that serves 800 tokens/second. Run the math on your actual model and batch size, not the vendor's benchmark.
5. Factor in scaling headroom. If you expect traffic growth, a card with more HBM and bandwidth gives you room to increase batch size and concurrency without re-provisioning. Buying slightly more GPU than you need today is cheaper than migrating to new hardware in six months.

Where cloud GPU access fits

Not every team wants to own GPUs, and for most LLM workloads the calculus favors cloud access over procurement. Owning 8 H200s means a six-figure capital expense, months of lead time, and a team to manage networking and maintenance. If your traffic is bursty or you're still iterating on model choice, that's a poor match.

This is where the deployment model matters as much as the card choice. GMI Cloud is an AI-native inference cloud built for production AI, and it offers H100, H200, and B200 GPU options without the hypervisor tax that general-purpose clouds add. GMI Cloud provides bare metal GPU with root access so you receive 100 percent of the advertised bandwidth, managed GPU clusters with RDMA-ready networking for multi-node tensor parallelism, and a serverless Inference Engine that scales to zero for variable traffic. The GMI Cloud GPU catalog lists available NVIDIA hardware and current rates.

For teams that want to start with a serverless API, move to dedicated endpoints as traffic grows, and scale into bare metal clusters for sustained production load, a single platform that spans that range removes the migration cost that usually hides in GPU projects. GMI Cloud backs this with 30,000-plus GPUs deployed, 99.99 percent platform availability, and sub-200ms average cross-region latency.

Pick the card that fits, not the one that tops the spec sheet

The best GPU for LLM workloads is the one that holds your model in HBM, streams it through bandwidth fast enough to hit your latency target, and scales across NVLink without breaking your budget. For 7B models, a single H100 is usually sufficient. For 70B, H200 gives you the memory and bandwidth headroom to serve at concurrency. For 405B and frontier scale, B200 is the card designed for the job. Work through the framework, size to your model, compare on cost per token, and the card choice becomes a decision you can defend with numbers rather than a guess based on peak FLOPS.

GMI Cloud offers the full range of NVIDIA data center GPUs, from H100 to B200 and GB200 NVL72, on bare metal and managed clusters with RDMA-ready networking. Whether you're running a 7B model on a single card or sharding a 405B model across a multi-node cluster, the platform lets you match the GPU to the workload without re-architecting as you scale. When you're ready to map your workload to specific hardware, the GMI Cloud models page shows which GPUs are available for each deployment option.