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
RTX 6000 Ada cloud rental prices
Every provider offering RTX 6000 Ada, normalised to hourly on-demand pricing with VRAM and region details.
Research by GPUCloudCompare Research
48 GB VRAM
Ada Lovelace
960 GB/s
1 offers
About the RTX 6000 Ada
- Memory
- 48 GB
- Architecture
- Ada Lovelace
- Memory bandwidth
- 960 GB/s
- Dense FP16 throughput
- 280 TFLOPS*
Vendor-published figures, with sparsity where the vendor states it. Always confirm on the official datasheet before sizing a cluster.
Live GPU price comparison
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| Provider | GPU model | VRAM | GPUs | vCPU | Region | On-demand /hr | Spot /hr | Actions |
|---|---|---|---|---|---|---|---|---|
| RunPod | RTX 6000 Ada | 48 GB | 1 | — | Global (secure + community cloud) | $0.7400 Best price | — | Rent this GPU |
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Which GPUs work well with the RTX 6000 Ada?
Use the LLM VRAM calculator to check whether a specific model fits in one RTX 6000 Ada, and the cost estimator to turn a hourly rate into a monthly budget including storage and egress.
RTX 6000 Ada compared with other GPUs
Frequently asked questions
Everything about renting cloud GPUs, affiliate tracking and how we collect pricing data.
Which GPUs are best for LLM inference?
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.
How much VRAM do I need to fine-tune a model?
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.
Is it cheaper to rent or to buy a GPU?
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.
Why do the same GPU models have different prices?
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.