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There is no reliable conversion from a model’s file size or parameter count to a single cost to run it. The deployment footprint also depends on numerical precision, context length, concurrent requests, runtime overhead, hardware, and the speed users expect. Treat “100 KB to 2.5 TB” as a framing for a broad spectrum—not a verified, continuous range established by the sources cited here—and compare costs only for a defined workload.
What does “model size” actually measure?
The phrase can refer to different things: a checkpoint file’s size on disk, a model’s parameter count, the parameters active for a particular token, or the memory needed to serve it. Those measurements are related, but they are not interchangeable. A compact file does not by itself tell you how much memory an inference server needs or how much a request will cost.
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Parameter count gives a useful first estimate of weight memory when the numerical format is known. AWS’s approximate weights-only estimates show how much precision changes that estimate:
| Parameter count | FP16 weights | FP8/INT8 weights | INT4/NVFP4 weights |
|---|---|---|---|
| 7B | 14 GB | 7 GB | 3.5 GB |
| 70B | 140 GB | 70 GB | 35 GB |
These are approximate model-weight requirements from AWS Prescriptive Guidance, not total serving-memory figures. Actual needs depend on the model and inference setup.
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Total parameters are not always active parameters
Mixture-of-experts (MoE) models make parameter counts harder to interpret. Google’s Gemma documentation describes a 26B A4B model that activates 4B parameters per token. That does not mean only 4B parameters need to be loaded: Google notes that all 26B are loaded for fast routing. Compare both total and active parameters, and check the model’s documented loading requirements, rather than using the active count as a memory estimate.
Why serving memory is larger than the weights
Inference memory also has to account for activations, the key-value (KV) cache, and runtime overhead. The cache holds information used to generate later tokens; its demand rises with context length and the number of simultaneous requests. Architecture and serving configuration also affect the result.
AWS’s Mistral-7B example illustrates how workload assumptions change the KV-cache estimate:
| Context length | One request | Four concurrent requests |
|---|---|---|
| 1,000 tokens | 0.12 GB | 0.49 GB |
| 16,000 tokens | 1.95 GB | 7.81 GB |
These figures are cache estimates for AWS’s stated example, not total memory requirements for Mistral-7B. They show why quoting a model’s weights alone can substantially understate a real serving footprint. See AWS’s memory-sizing guidance for the example and its assumptions.
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How the spectrum changes the deployment decision
The useful question is not simply whether a model is “small” or “large”; it is whether its full workload fits the available hardware, then whether that hardware can deliver the required latency and throughput. AWS recommends establishing viable instance sizes from memory needs before evaluating throughput and latency. Its guidance puts the sequence plainly: “Memory sizing establishes the set of viable instance size options before throughput and latency considerations are evaluated.”
Small and on-device models
Smaller models can fit in a more limited memory budget, but the format and inference environment still matter. Google’s Gemma 4 model overview lists E2B inference-memory estimates of 11.4 GB in BF16, 5.7 GB in SFP8, and 2.9 GB in Q4_0. It also lists mobile variants at 1.1 GB and 0.84 GB for text-only use. Google says these estimates include 20% overhead and can vary with the inference tool and environment. They are useful as model-specific examples, not guarantees for every device or deployment.
Models in the tens of billions of parameters
At this scale, weight memory alone can exceed a single accelerator’s capacity, especially at higher precision. AWS’s approximate 70B FP16 weight estimate is 140 GB, which it describes as a multi-GPU deployment example. Lower-precision weights reduce that estimate, but the KV cache and runtime still need room. A setup that barely fits the weights may not support the intended context length or concurrency.
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For still larger models, deployment may span multiple GPUs or multiple nodes. Splitting a model across devices can make it fit, but it introduces communication between processors as another consideration. Georgetown University’s Center for Security and Emerging Technology notes that inference can be constrained by compute, loading parameters into memory, or communication between processors; the dominant bottleneck depends on architecture, batch size, processor count, and other factors. Consequently, parameter count alone cannot predict how quickly or economically a model will serve requests.
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What hardware capacity tells you—and what it does not
Hardware memory is a feasibility check, not a cost-per-token answer. For one concrete example, Google Cloud’s GPU machine table lists the RTX PRO 6000 with 96 GB of GDDR7 memory and identifies ML inference and training as suitable uses. That documents a configuration and workload category; it does not establish that this GPU is the right or cheapest choice for a particular model.
Before choosing hardware, account for the full model footprint, runtime, context length, and expected concurrency. Then measure or obtain throughput and latency results for the specific model and software stack. A GPU’s advertised memory capacity cannot by itself establish how many tokens per second a deployment will produce.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What AI models actually cost to run
There is no universal price per token for a model size. A useful comparison specifies the model, input and output workload, concurrency or batch conditions, performance target, software configuration, hardware, and utilization. It also distinguishes a provider’s API price, rented hardware expense, and the operating cost of owned equipment: those are different measures and should not be compared as if they were equivalent.
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NVIDIA’s vendor page reports $0.123 per million tokens for a GB300 NVL72 at 116 TPS/user interactivity, attributing the result to SemiAnalysis InferenceX benchmarks as of April 2026. NVIDIA also reports that GPT-OSS-120B on B200 fell from $0.11 to $0.02 per million tokens within two months, citing SemiAnalysis InferenceX as of April 2026. These are vendor-published, attributed benchmark results—not general market rates or independently verified operating costs. The reported change also illustrates that software optimization can materially affect a benchmarked cost.
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For a useful comparison, keep the workload constant and record the assumptions beside each result:
- Model and format: identify the model, parameter count, and precision or quantization.
- Memory: report weight memory separately from KV cache and runtime overhead; state context length and concurrent requests.
- Hardware: name the accelerator, device count, and whether the model is split across devices or nodes.
- Performance: state throughput and latency targets alongside the benchmark’s software and workload conditions.
- Economics: clarify whether the figure is an API price, rented infrastructure cost, or an estimate for owned hardware, and account for utilization.
A cloud GPU’s hourly rate alone does not reveal cost per token: the result depends on how much useful work the hardware completes and how consistently it is utilized. Georgetown CSET’s 2024 report discusses inference bottlenecks and cost methodology in Scaling AI: The Cost and Performance of AI at the Leading Edge.
A practical way to compare deployments
- Define the workload. Specify the model, input and output lengths, context window, concurrency, and latency target.
- Estimate the full memory footprint. Start with weights at the intended precision, then include cache, activations, and runtime overhead. Treat published estimates as model- and environment-specific.
- Check hardware fit. Confirm that the complete deployment fits, including any sharding across GPUs or nodes, before comparing speed.
- Compare performance under matching conditions. Use throughput and latency results for the same model and a comparable workload; avoid treating a benchmark from a different batch size or interactivity target as directly comparable.
- Calculate cost for that workload. Keep API pricing, rented compute, and owned-hardware operating estimates distinct, and state utilization assumptions with any cost-per-token figure.
Why the headline’s endpoints need qualification
The available official sources provide examples ranging from compact mobile inference footprints to multi-GPU and multi-node deployments, along with specific model-memory estimates and attributed benchmark costs. They do not independently verify 100 KB and 2.5 TB as the endpoints of one continuous, measured range. Those figures should therefore be understood as the framing of a broad spectrum, not as a single standardized comparison. Any claim about a particular model or deployment is more useful when it names what “size” means and gives the workload and hardware assumptions.
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