Estimate an LLM’s weight memory first, then budget separately for its KV cache, activations, runtime allocations and workload. For a rough per-GPU weight estimate, multiply the parameter count by bytes per parameter and divide by the tensor-parallel degree. That gives a starting point—not a guarantee that the model will fit or a prediction of its cost per token.
Estimate model-weight memory per GPU
For a first-pass estimate, use:
Weight memory per GPU ≈ total parameters × bytes per parameter ÷ tensor-parallel degree
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NVIDIA’s versioned NIM 2.0.13 sizing guide uses these approximate bytes-per-parameter values:
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- FP8: 1 byte per parameter
- INT4 or NVFP4: 0.5 byte per parameter
These are sizing heuristics, not a promise that a checkpoint’s files or runtime allocations will equal the result. Quantization metadata and scales, unquantized layers, packing, and implementation details can change actual memory use.
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| Example | Estimated weight memory | Source and qualification |
|---|---|---|
| Llama 3.1 8B, BF16, TP=1 | 16 GB total | NVIDIA NIM 2.0.13 guide; estimated weights only |
| Llama 3.3 70B, BF16, TP=4 | 35 GB per GPU | NVIDIA NIM 2.0.13 guide; estimated weights only |
| Llama 3.3 70B, FP8, TP=2 | 35 GB per GPU | NVIDIA NIM 2.0.13 guide; estimated weights only |
For example, the 8B calculation is 8 billion × 2 bytes = 16 billion bytes, or 16 decimal GB. NVIDIA’s guide names a 24 GB GPU, including an RTX 4090, as an example that can accommodate this weight estimate while leaving capacity for cache and overhead. That is an illustrative configuration, not a fit guarantee for every context length, model revision, or runtime.
Keep units consistent. A decimal GB is 1,000,000,000 bytes; a GiB is 1,073,741,824 bytes. Thus, a reported 16 GB corresponds to about 14.9 GiB. Leave room for runtime allocations instead of choosing a GPU whose advertised capacity barely matches a rounded weights-only figure.
Budget for everything besides weights
Weights are only one part of GPU memory use. TensorRT-LLM documentation identifies weights, activations, and I/O tensors—especially the KV cache—as major contributors. Depending on the engine and model, runtime use can also include communication buffers, CUDA graph capture, adapters, and multimodal state or reservations. NVIDIA notes that allocation order and accounting vary by backend version and model.
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KV cache grows with context and concurrency
The KV cache stores keys and values for prior tokens so the model can continue generation without recomputing the entire history. Its size increases with the tokens retained and the number of simultaneous sequences. Parameter count alone does not determine the cache requirement: architecture, layer and attention structure, cache precision, context length, concurrency, and serving-engine behavior all matter.
Activations and configured limits matter too
Activations use memory during computation. TensorRT-LLM documentation says engine activation memory depends on maximum shapes and build-time limits such as batch and token counts. If configured maxima are much larger than typical requests, the engine can reserve or require capacity for those maxima anyway. A model may load successfully and still run out of memory when a request asks for a longer context or larger cache.
Build a realistic memory estimate
- Identify the exact checkpoint. Record the model revision, parameter count, architecture, and checkpoint metadata. A family name alone is not enough to establish precise memory use.
- Use the deployed weight format. Apply the precision or quantization actually used by the checkpoint and runtime. Treat bytes-per-parameter arithmetic as an estimate, not a substitute for file and engine metadata.
- Divide across GPUs only when the deployment shards the weights. Tensor-parallel degree is a useful initial divisor, but topologies and implementations do not necessarily distribute every allocation evenly.
- Add runtime memory separately. Account for KV cache, peak activations, communication and runtime buffers, graph capture, and any adapter or multimodal state relevant to the deployment.
- Set the workload limits. Specify maximum input/context length, output length, batch size or concurrency, and latency target. These influence both capacity and throughput.
- Validate on the target engine and configuration. Inspect startup logs and allocator measurements, then test representative requests at the intended concurrency and context. The actual measurement is more useful than a weights-only calculation.
- Keep headroom. A memory-utilization setting controls a budget; it does not create physical VRAM. vLLM warns that reserving more memory can increase KV-cache capacity but can also cause an out-of-memory failure.
Estimate inference cost for the billing model
There is no universal current cost per million tokens. The calculation depends on the provider’s billing terms, the deployed configuration, and measured throughput for the workload.
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Self-hosted or rented GPUs
Find the billable instance or GPU rate and billing interval, then measure output tokens produced during a representative interval. A basic estimate is:
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Cost per generated output token = compute charges for the measurement interval ÷ generated output tokens in that interval
Multiply that result by 1,000,000 for cost per million generated output tokens. State what the charge includes: GPU instance, CPU, RAM, storage, network, idle time, replicas, discounts, and operational overhead. Track input and output token volumes separately when both matter; one blended figure can conceal a workload’s cost drivers.
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Managed endpoints and per-token APIs
Use the provider’s current rate and the billing unit that applies to the product. Hugging Face documents an endpoint calculation based on rate × duration × replica count and says its displayed hourly rates are billed per minute. DigitalOcean describes dedicated inference billed per GPU-hour. These are examples of different billing models, not universal terms.
For either model, record the token mix or replica runtime that actually drove the bill. Verify the current pricing page and terms for the selected product rather than assuming a displayed rate applies to every region, GPU configuration, or usage pattern.
Benchmark before comparing cost per token
For a GPU deployment, compare options using the same model revision, output quality target, serving engine, and representative workload. Measure useful throughput and latency at the concurrency and context lengths you expect to serve. A low hourly rate does not guarantee low cost per token if throughput or utilization is poor.
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NVIDIA’s 2024 LLM Inference Sizing presentation says that, in its evaluated serving context, “The cost and the latency are usually dominated by the number of output tokens.” The presentation is historical, and the observation should not be treated as universal. Long prompts, low utilization, strict time-to-first-token or inter-token latency targets, batching, and concurrency can change the economics. Latency constraints can also reduce achievable throughput.
When comparing providers or instances, capture the provider, region, instance configuration, GPU count, operating system, billing mode (such as on-demand, reserved, or spot), and the date you checked the price. AWS notes that Capacity Blocks rates are updated with supply and demand. An hourly price without measured throughput and utilization cannot establish a cost per token.
Quick Recap
Use the same comparison checklist for each option
- Available VRAM against the combined weight, cache, activation, and runtime budget
- Weight and KV-cache precision, including any quality impact that needs evaluation for the task
- Maximum context and concurrent requests at the required latency
- Measured input and output throughput with the chosen batching and scheduling configuration
- Cost per request or per million input and output tokens at realistic utilization
- Region, availability, billing granularity, commitment or interruptibility, and additional instance charges
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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