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There is no supportable exact price for reproducing a 125B-parameter model at 100 generated tokens per second on one GPU. The model, weight format, context length, inference software and meaning of “100 tok/s” are unspecified, and no matching single-GPU benchmark or complete system price is established. A simple bandwidth calculation shows why the result cannot be inferred from a GPU’s price or specifications alone: it puts a 96GB RTX PRO 6000 Blackwell workstation card at about 29 tokens/s, while an 8 TB/s B200 reaches about 128 tokens/s only in an idealized calculation—not a demonstrated result.
Why there is no single reproduction price
“125B parameters at 100 tok/s on one GPU” is not a complete hardware or benchmark specification. A credible cost estimate needs to identify at least:
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- The exact model and whether it is dense or mixture-of-experts (MoE).
- The quantization format and actual bytes used per parameter, including format overhead.
- The context length and whether the measured rate is for one active user or aggregate throughput across a batch.
- The GPU SKU, inference runtime and version, and benchmark conditions.
- Whether the requested cost means the GPU alone, a complete host, rental, or total operating cost.
Without those details, the available figures support a feasibility estimate from published memory-bandwidth specifications, not a quoted price for a system that achieves the target.
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How much memory the weights need
For a dense 125-billion-parameter model, nominal weight storage is about 62.5GB at 4 bits per parameter, or 125GB at 8 bits, using decimal units. These are arithmetic estimates before quantization metadata, alignment, runtime allocations, or the key-value (KV) cache. The GPU must have room for the actual model representation and the rest of the inference workload; a capacity number alone does not establish that a model will fit in practice.
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For an MoE model, total parameter count can substantially exceed the parameters active for each token. Its active computation and memory behavior therefore cannot be inferred from the dense-model calculation below.
What published bandwidth implies—and does not
In dense autoregressive decoding, a useful first-order model assumes the GPU reads the entire 4-bit weight set once for every generated token. Dividing the published memory bandwidth by 62.5GB gives an idealized ceiling. It is a calculation from specifications, not an observed benchmark; it excludes KV-cache traffic, quantization overhead, computation, and runtime inefficiencies.
| GPU reference | Published memory | Published bandwidth | Idealized 4-bit weight-read ceiling | Cost evidence |
|---|---|---|---|---|
| RTX PRO 6000 Blackwell workstation GPU | 96GB GDDR7, per NVIDIA’s product information | 1.8 TB/s, per NVIDIA’s workstation datasheet | About 29 tokens/s (1.8 TB/s ÷ 62.5GB) | Tom’s Hardware reported on June 13, 2026, that NVIDIA’s US marketplace listed the GPU at $13,250. GPU only; retailer prices can differ. |
| NVIDIA H200 | 141GB HBM3e, per NVIDIA’s launch documentation | 4.8 TB/s, per NVIDIA’s launch documentation | About 77 tokens/s (4.8 TB/s ÷ 62.5GB) | Not stated in the cited source material. |
| NVIDIA B200 GPU type | 180GB HBM3e, per Google Cloud’s GPU types documentation | 8 TB/s, per Google Cloud’s GPU types documentation | About 128 tokens/s (8 TB/s ÷ 62.5GB) | A complete one-GPU system price is not established. |
The RTX PRO 6000 example illustrates the difference between capacity and speed: its listed 96GB exceeds the nominal 62.5GB 4-bit weight size, but the simplified bandwidth estimate is well below 100 tokens/s. The H200 estimate is also below target. B200’s calculated ceiling is above target, but with little theoretical headroom and no allowance for real inference overhead. None of these calculations proves a 100-token/s result.
What the $13,250 figure actually buys
The $13,250 figure is a dated US marketplace listing for the RTX PRO 6000 Blackwell workstation GPU reported by Tom’s Hardware on June 13, 2026. It is not the price of a complete workstation, a rental quote, or evidence that this GPU reproduces the target throughput. It should not be used as the cost of a qualifying 100-token/s setup.
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No complete B200 host price, suitable single-GPU rental price, electricity cost, networking cost, or operating-cost estimate is established here. Consequently, the evidence does not support a total purchase or deployment budget for the stated target.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to verify a reproduction claim
Before comparing a quoted result or buying hardware, ask for a benchmark that matches the workload you intend to run. NVIDIA’s TensorRT-LLM performance guide distinguishes output-token throughput and documents benchmark conditions; compare results only when their workload definitions align.
- Model: exact model, architecture, and whether the parameter count is total or active per token.
- Weights: quantization format and actual memory footprint, not just nominal bits per parameter.
- Speed metric: generated tokens per second for one stream versus aggregate tokens per second across concurrent requests.
- Context and concurrency: prompt and output lengths, batch size, and number of users. Longer contexts add KV-cache memory and traffic; batching can raise aggregate throughput without preserving single-user speed.
- Software: inference runtime, version, and any techniques such as speculative decoding, with separate evidence for their effect.
- System and price: exact GPU, host configuration, region and date of the quote, and whether the figure includes the host and ongoing costs.
A benchmark that reports aggregate throughput cannot by itself substantiate 100 tokens/s for one user. Likewise, an idealized bandwidth ceiling is a screening calculation, not proof of measured performance.
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