There is no defensible exact price for reproducing 100 generated tokens per second from a 125-billion-parameter model on one GPU without knowing the model, quantization, context length, software stack and whether the speed is for one user or multiple users combined. A simple memory-bandwidth calculation suggests that 100 tokens/s is an ambitious target: a dense 125B model at nominal 4-bit weights needs about 62.5GB just for its raw weights, and would require at least 6.25TB/s of effective memory bandwidth under idealized assumptions. A workstation GPU listed at $13,250 is a useful price reference, not a demonstrated way to achieve the target.
What the 100-token/s target actually depends on
“125B” describes the model’s total parameter count, not the hardware, memory use or speed needed for a particular inference run. The cost question is underspecified until several workload choices are fixed:
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- Architecture: The calculations below assume a dense model, where all weights contribute to each generated token. A mixture-of-experts model can have many total parameters but use only some experts for a given token, changing the computation and throughput picture.
- Weight format: Four bits per parameter is a nominal size assumption, not a guarantee that a quantized model occupies exactly that amount. Format metadata and other overhead add memory use.
- Context length: The key/value cache (KV cache) stores information used to continue generation. Its memory use and traffic grow with context and can constrain capacity or speed.
- Meaning of “100 tok/s”: Single-stream decode speed for one user is not interchangeable with aggregate throughput across a batch of users. Batching can raise combined throughput while changing the speed experienced by each user.
- Software and measurement: Runtime, version, benchmark conditions and decoding techniques affect results. For example, speculative decoding changes the work involved and needs its own workload-specific evidence.
These distinctions matter because the target is a measured inference result, not just a question of whether the model’s weights fit in GPU memory.
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Using decimal units, 125 billion parameters at four bits each amount to 500 billion bits, or 62.5GB of raw packed weights. At eight bits per parameter, the corresponding raw weight size is 125GB. Neither figure includes quantization overhead, the KV cache, runtime allocations or other memory needs.
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- Professional Intel Arc Pro B70 GPU: Built on the Intel Xe2-HPG architecture, it features 32 Xe cores and 256 XMX engines, designed to accelerate AI, rendering, and complex visualization workloads.
- Massive 32GB GDDR6 VRAM: Equipped with 32GB of high-speed GDDR6 memory on a 256-bit bus, running at 19 Gbps, which allows for handling large AI models and complex datasets locally.
- High-Performance Engine Clock: Delivers an engine clock of 2540 MHz, providing the compute power needed for demanding professional applications and AI inference.
That makes a 96GB GPU a plausible capacity comparison for 4-bit weights, but not proof that a full inference setup will fit comfortably: the raw 4-bit weights alone leave only about 33.5GB before those other allocations. At 8-bit weights, 125GB of raw weights exceed 96GB.
What bandwidth implies for decode speed
For dense autoregressive decoding, a useful first-order estimate is to divide GPU memory bandwidth by the bytes of weights assumed to be read for each generated token. Under the simplified 4-bit assumption, the full 62.5GB weight set is read once per token. This produces a theoretical ceiling from published bandwidth specifications, not a measured benchmark:
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| GPU comparison | Published memory specification | Idealized 4-bit weight-read ceiling |
|---|---|---|
| RTX PRO 6000 Blackwell workstation GPU | 96GB GDDR7; 1.8TB/s bandwidth, according to NVIDIA’s product information and workstation datasheet | About 28.8 tokens/s, or roughly 29 |
| H200 | 141GB HBM3e; 4.8TB/s bandwidth, according to NVIDIA’s H200 launch documentation | About 76.8 tokens/s, or roughly 77 |
| B200 | 180GB HBM3e; 8TB/s bandwidth, according to Google Cloud’s GPU types documentation | About 128 tokens/s |
The arithmetic is bandwidth divided by 62.5GB: for example, 1.8TB/s ÷ 62.5GB ≈ 28.8 tokens/s. Reaching 100 tokens/s on this simplified model would require at least 6.25TB/s of effective bandwidth. The B200 specification clears that idealized threshold, but its roughly 128-token/s ceiling leaves limited room for the other memory traffic and runtime work that the calculation excludes. It does not establish that a real system will sustain 100 tokens/s.
Actual speed may be lower than these ceilings because inference also involves KV-cache traffic, quantization details, computation and software overhead. Workload-specific techniques can change the result, too. NVIDIA’s TensorRT-LLM performance guide distinguishes output-token throughput and describes benchmark conditions; comparisons are meaningful only when the workload definitions match.
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What does a one-GPU setup cost?
A dated workstation-GPU price reference
Tom’s Hardware reported on June 13, 2026, that NVIDIA’s marketplace listed the RTX PRO 6000 Blackwell workstation GPU at $13,250; the report also noted that retailer prices differ. This is a dated US GPU-only price, not the cost of a complete workstation. Its published 1.8TB/s bandwidth corresponds to an idealized ceiling of about 29 tokens/s in the dense 4-bit calculation, so this listing does not show what it costs to meet the 100-token/s target.
Why the B200 is not a priced answer
The B200’s published 8TB/s bandwidth makes it a specification-based candidate for investigating the target, but the cited GPU documentation does not provide a current price for a complete single-GPU host. Nor does the specification establish 100 tokens/s on a particular 125B model and workload. No supported total can therefore be given for the GPU, compatible system and operation.
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The figures available do not establish a suitable rental price, electricity cost, networking cost or operating cost for a qualifying single-GPU setup either. Any all-in price would require those details as well as an exact hardware configuration.
What to specify before comparing prices or benchmarks
To make the reproduction question answerable, define the run first, then compare systems against it. A useful specification includes:
- Model: exact model and architecture, including whether it is dense or mixture-of-experts.
- Weights: exact quantization format and resulting model size, rather than nominal bits alone.
- Generation workload: prompt and context length, output length, and whether the target is single-user decode speed or aggregate throughput at a stated concurrency.
- Hardware: exact GPU SKU, memory capacity and bandwidth, plus the compatible host needed to run it.
- Software: inference runtime and version, along with any decoding technique that affects the result.
- Benchmark: measured output-token speed under the same workload definition, and a clear statement of whether the figure is per stream or aggregate.
- Cost scope: GPU-only purchase price, complete system price, or a defined rental and operating-cost period.
Without those matching details, a peak throughput figure, a memory-bandwidth estimate and a GPU listing answer different questions; none alone provides a reproducible cost for the title’s target.
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