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There is no reliable conversion from a model’s parameter count—or checkpoint file size—to a universal price per token. A smaller model can be cheap to serve, while a larger one may need multiple accelerators; but the actual bill also depends on precision, memory for context and concurrent requests, hardware, software, throughput, and utilization. To compare models meaningfully, define the workload first, then compare the memory and cost of serving that same workload.
What does “model size” actually measure?
A model’s size can refer to several different things: parameter count, the bytes occupied by its weights at a chosen numerical precision, the size of a downloadable checkpoint, or the full memory footprint while it is serving requests. Those figures are related, but they are not interchangeable.
For a dense model, parameter count is a useful first clue about weight memory. Lower-precision formats store each parameter in fewer bits, reducing the memory needed for weights. For example, Amazon Web Services (AWS) gives these approximate weights-only estimates:
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| Model 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 weight requirements, not complete serving-memory totals. Quantization can reduce memory requirements, but comparing a model at one precision with another at a different precision does not by itself establish equivalent quality or performance. AWS’s estimates are a sizing guide, not a guarantee of a particular hardware fit or output quality.
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Mixture-of-experts models complicate the count
For a mixture-of-experts (MoE) model, total parameters and active parameters per token describe different things. Google’s Gemma documentation says Gemma 4 26B A4B activates 4B parameters per token, but still loads all 26B parameters for fast routing. The smaller active count should not be mistaken for the total weight memory needed to load the model.
Why model weights are not the full serving footprint
A running inference system needs memory beyond the model’s weights. AWS identifies weights, activations, the key-value (KV) cache, and runtime overhead as major components. The cache holds information used to continue generating a response; its demand changes with context length and the number of concurrent requests, among other architecture-specific details.
AWS’s Mistral-7B example shows how sharply cache needs can change as a workload grows:
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| Context length | KV cache for one request | KV cache for four concurrent requests |
|---|---|---|
| 1,000 tokens | 0.12 GB | 0.49 GB |
| 16,000 tokens | 1.95 GB | 7.81 GB |
These are cache estimates for AWS’s stated example, not total model memory or a general value for every 7B model. They illustrate why a memory estimate based only on weights can understate what a chosen context length and request concurrency require.
A small published footprint is still conditional
Google AI for Developers lists Gemma 4 E2B inference-memory estimates of 11.4 GB in BF16, 5.7 GB in SFP8, and 2.9 GB in Q4_0. The page also lists mobile variants at 1.1 GB and 0.84 GB for text-only use. Google says the estimates include 20% overhead and may vary by inference tool and environment. Treat them as estimates for the documented model and formats, not a universal measure of what any model of a similar parameter count will require.
How to decide whether a model fits
Memory fit is the first deployment question; speed and price come after it. AWS Prescriptive Guidance puts it this way: “Memory sizing establishes the set of viable instance size options before throughput and latency considerations are evaluated.”
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- Set the workload. Specify the model, precision, expected input and output lengths, context length, and number of simultaneous requests.
- Estimate total memory. Account for weights, cache, activations, and runtime overhead. Do not treat a weights-only estimate as the required accelerator capacity.
- Choose eligible hardware. Confirm that the model and runtime fit in available memory. If they do not fit on one device, determine whether the deployment can be sharded across multiple GPUs or nodes.
- Measure performance on that setup. Compare throughput and latency under the intended concurrency and request pattern, not just a headline tokens-per-second result.
- Calculate cost for the same workload. Use the serving cost and actual tokens produced at the observed utilization and performance. State whether the hardware is rented or owned and include the workload assumptions.
AWS describes a 70B model at FP16 as a multi-GPU deployment example and says frontier-scale models may require multiple GPUs or nodes. The actual configuration depends on available accelerator memory and the serving setup. Google Cloud’s GPU table, for example, lists the RTX PRO 6000 with 96 GB of GDDR7 and identifies ML inference and training as suitable uses. That is one hardware example, not a universal recommendation or proof that a particular model and workload will fit.
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What do models actually cost to run?
There is no single price for “running a 7B model” or “running a trillion-parameter model.” A cost-per-token figure only makes sense with its benchmark setup and workload. It can shift with the hardware, serving software, request mix, concurrency, latency target, utilization, and whether the calculation includes rented infrastructure or owned hardware.
NVIDIA’s Blackwell inference page reports $0.123 per million tokens for a GB300 NVL72 at 116 tokens per second per user, based on SemiAnalysis InferenceX benchmarks as of April 2026. This is an attributed benchmark result under that stated interactivity condition, not a general market rate or a quote for all workloads. On the same page, NVIDIA reports that GPT-OSS-120B cost on B200 fell from $0.11 to $0.02 per million tokens within two months, also citing SemiAnalysis InferenceX as of April 2026. That comparison shows how much software optimization can affect a benchmarked figure; it does not establish a cost for other models, hardware, or serving conditions.
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Do not compare an API provider’s list price, a cloud GPU’s hourly rate, and a self-hosted benchmark cost per token as if they were the same measure. They describe different billing models and may include different parts of the service. For an apples-to-apples comparison, define the input and output lengths, concurrency, response-time target, and utilization, then report both the resulting performance and what costs are included.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why the “100 KB to 2.5 TB” spectrum needs qualification
The endpoints “100 KB” and “2.5 TB” frame an exceptionally broad range, but the available official sources do not verify them as a single, comparable span of model artifacts or serving deployments. A file of a given size is not necessarily a model with a directly inferable parameter count, and a deployment’s memory footprint is not the same as a model file’s size. The documented examples here instead show small on-device inference estimates, weights for 7B and 70B models, and accelerator-scale systems.
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That distinction matters most at the extremes. A compact artifact might be a compressed or specialized model rather than a directly comparable full-precision checkpoint. At the other end, a large multi-accelerator deployment may include far more than model weights. Without a documented artifact, precision, hardware configuration, and workload, neither endpoint tells a reader what a model will cost to serve.
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Which constraint limits inference?
Inference is not always limited by the same resource. Georgetown University’s Center for Security and Emerging Technology explains in its 2024 report that inference can be constrained by compute, loading parameters into memory, or communication between processors. Which bottleneck dominates depends on factors including the model architecture, batch size, and processor count.
- Memory capacity: The model and its runtime requirements do not fit on the available device or device group.
- Memory movement: Performance is limited by how quickly parameters can be brought into use, even when enough capacity is available.
- Compute: The processors cannot perform the required calculations quickly enough to meet the throughput or latency target.
- Communication: A model distributed across processors spends time moving data between them.
This is why parameter count alone cannot rank real-world serving cost. A deployment can be memory-feasible but too slow for its target, or fast enough only with hardware and utilization that change the economics.
A practical comparison checklist
When comparing two model deployments, put their assumptions side by side rather than comparing headline sizes:
- Model: Record the model and architecture, including total and active parameter counts for MoE systems.
- Artifact and precision: State whether the number is checkpoint size or weight memory, and identify the format, such as FP16, FP8, or INT4.
- Memory: Include weights, context length, concurrency, KV cache, activations, and runtime overhead.
- Hardware: Name the accelerator, memory capacity, device count, and whether the model is sharded.
- Performance: Report throughput and latency with the software, batch or concurrency, and input/output conditions.
- Economics: State cost per token or total serving cost alongside utilization and whether the hardware is rented or owned.
For a useful decision, compare candidate deployments on the same workload. A smaller weight file is not automatically the lower-cost option if it needs more hardware to meet the latency target; a larger model is not automatically uneconomical if its quality, throughput, or utilization makes the target workload viable.
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