How Much VRAM Do I Need for LLM Training and Inference?
A practical engineer's guide to VRAM for LLM training, fine-tuning, and inference. Learn how to estimate VRAM for 7B and 70B...
2026-10-25 Read guideGPU price index
Every provider offering RTX 3090, normalised to hourly on-demand pricing with VRAM and region details.
Research by GPUCloudCompare Research
Vendor-published figures, with sparsity where the vendor states it. Always confirm on the official datasheet before sizing a cluster.
Filter by provider, GPU or region — filters run locally in your browser.
| Provider | GPU model | VRAM | GPUs | vCPU | Region | On-demand /hr | Spot /hr | Actions |
|---|---|---|---|---|---|---|---|---|
| RunPod | RTX 3090 | 24 GB | 1 | — | Global (secure + community cloud) | $0.2200 Best price | — | Rent this GPU |
| Vast.ai | RTX 3090 | 24 GB | 1 | — | Distributed (global hosts) | $0.2422 | $0.1867 | Rent this GPU |
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Use the LLM VRAM calculator to check whether a specific model fits in one RTX 3090, and the cost estimator to turn a hourly rate into a monthly budget including storage and egress.
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For models that fit in memory, memory bandwidth matters more than raw FLOPS, so cards like the L40S, RTX 4090 or A100 often give better tokens per dollar than an H100. Once a model needs multi-GPU tensor parallelism, NVLink-equipped H100 or H200 instances usually win on throughput.
Full fine-tuning needs roughly the parameter count times the bytes per parameter for weights, plus optimiser state which can be several times larger again. LoRA and QLoRA reduce the optimiser footprint dramatically. Use the LLM VRAM calculator to size a specific configuration.
Renting usually wins unless you can keep a device busy for a large fraction of the year. Cloud rental also shifts the cost of hardware refreshes onto the provider, which matters when new generations arrive every twelve to eighteen months.
Configuration differs: number of GPUs per instance, system RAM and vCPU allocation, local disk, region, and whether the capacity is interruptible. We normalise to a single GPU so you can see through those differences.