Best Cloud GPUs for LLM Serving & Deployment

What LLM serving actually demands from a rented GPU

Serving a large language model is a fundamentally different workload from training one. Training is throughput-bound and tolerant of latency; serving is latency-sensitive, memory-bound, and bursty. When you rent a GPU to deploy an LLM behind an API, the bottleneck is rarely raw FLOPS. It is how much of the model and its KV cache you can hold in VRAM, how fast that memory streams, and how many concurrent requests you can batch before tokens-per-second per user collapses.

The KV cache is the part most people underestimate. Every active request stores the attention keys and values for its context, and that footprint grows with sequence length and the number of concurrent users. A model that fits comfortably at idle can run out of memory the moment you push real traffic with long prompts. This is why serving deployments often need more VRAM headroom than the model weights alone suggest.

How to size the GPU to the model

The practical first question is whether the model fits on one GPU or has to be split across several. Reading the comparison above against your model, weigh these factors:

• VRAM capacity drives which models fit. A quantized 7B–13B model in FP8 or INT8 can serve from a single mid-tier accelerator, while a 70B model in BF16 generally needs a high-memory card or a multi-GPU node. The very large frontier-class models effectively require multiple top-end GPUs linked together.
• Memory bandwidth sets your token generation speed. Autoregressive decoding reads the full set of weights for every token produced, so HBM-class memory (as found on data-center cards) generates tokens far faster than GDDR-based consumer cards at the same model size. For interactive chat, bandwidth often matters more than compute.
• Supported precisions determine how aggressively you can shrink the model. Cards with FP8 and INT8 tensor support let you serve larger models on less VRAM and at higher throughput, provided your serving stack and the model’s quantization scheme are compatible.
• Interconnect matters once a model spans multiple GPUs. Tensor-parallel serving exchanges activations between GPUs on every layer, so NVLink-class links inside a node deliver materially better latency than PCIe-only configurations. For single-GPU deployments, interconnect is irrelevant.

Single-GPU versus multi-GPU serving

If your model and its peak KV cache fit on one GPU, keep it there. Single-GPU serving avoids cross-device communication overhead entirely and is simpler to operate. Only move to tensor parallelism or multi-node serving when the model genuinely cannot fit, because every GPU you add introduces synchronization cost and complicates autoscaling. When you do need multiple GPUs, prefer instances in the list above that pair high-memory cards with a fast in-node interconnect rather than stitching together loosely coupled cards.

Provider features that matter for serving, not training

Beyond the silicon, the provider’s operational model decides whether a deployment is viable. When scanning the comparison above for a serving workload rather than a training run, prioritize differently:

• Cold-start and setup time become first-class concerns. A serving endpoint that scales to zero between traffic spikes pays the cold-start cost on every scale-up, so fast provisioning and image caching directly affect tail latency.
• Billing granularity changes the economics of bursty traffic. Per-second or per-minute billing suits autoscaling endpoints that spin instances up and down; coarse per-hour billing punishes that pattern.
• On-demand reliability over spot is usually the right call. Interruptible or spot instances are excellent for training and batch jobs but risky for a user-facing endpoint, where a reclaim mid-request drops live traffic. Spot can still serve non-interactive batch inference well.
• Persistent storage and fast model loading reduce restart pain. Multi-gigabyte weights that reload from cold object storage on every restart add minutes of downtime; cached or attached volumes shorten that.
• Networking and egress affect cost at scale. High-throughput inference can move a lot of data out; check egress terms before committing to a high-traffic deployment.

Batch versus real-time, and where cost lands

Decide early which mode you are optimizing for. Real-time interactive serving values low time-to-first-token and steady per-user token rates, which favors high-bandwidth memory and modest batch sizes. Batch or offline inference values total throughput and can pack large batches onto cheaper or interruptible hardware, trading per-request latency for far better cost per token. Many teams run both: a responsive on-demand tier for live users and a spot-backed batch tier for bulk generation.

On rental cost, serving sits across the whole spectrum. A small quantized model on a mid-tier card is inexpensive and widely available; frontier models on multi-GPU high-memory nodes are scarce, pricier, and sometimes capacity-constrained during demand peaks. Because rates move constantly and vary by region and commitment, treat the live comparison above as the source of truth rather than any fixed figure, and compare instances on VRAM, bandwidth class, interconnect, and billing model together rather than headline price alone.

Frequently asked questions

How much VRAM do I need to serve an LLM?

Budget for the model weights at your chosen precision plus substantial headroom for the KV cache, which grows with context length and concurrent users. As a rough guide, quantized small and mid-size models serve from a single mid-tier card, while large models in higher precision need a high-memory card or several GPUs. Always size for peak concurrency, not idle.

Are spot or interruptible GPUs suitable for LLM serving?

For user-facing real-time endpoints, generally no, because a reclaim can drop live requests and force cold restarts. Spot instances are well suited to offline or batch inference, where interruptions only delay throughput rather than break an interactive session. Many teams keep on-demand capacity for live traffic and use spot for bulk jobs.

Why does memory bandwidth matter more than compute for serving?

Token-by-token decoding reads the model’s weights from memory for every token it generates, so generation speed is bound by how fast the GPU streams memory rather than its peak FLOPS. This is why HBM-equipped data-center cards produce tokens faster than consumer cards holding the same model, and why bandwidth is a key column to compare above.

Should I serve on one GPU or split across several?

Use a single GPU whenever the model and its peak KV cache fit, since that avoids cross-device communication and simplifies scaling. Move to multi-GPU tensor parallelism only when the model genuinely will not fit, and when you do, choose instances that combine high-memory cards with a fast in-node interconnect.