Token Economics Across Traffic Profiles on Dedicated GPUs

What this is

A DigitalOcean engineering tutorial (2026-07-08) that does the one thing the rest of this topic's price sources cannot: it measures cost to serve, in dollars, per million output tokens, on named hardware at a named as-of date. It is the topic's first genuine cost instrument (everything else — Epoch price-trends, the Token Price Index, Evans, Patel — measures published price). It is analysis-grade, not peer-reviewed, and covers a single SKU/model/framework configuration; treat the numbers as a reproducible reference point, not a universal cost.

Measured configuration (as-of 2026-07-08)

• GPU: NVIDIA H200 (141 GB VRAM), on-demand $3.44/GPU-hour (DigitalOcean GPU Droplet list rate)
• Model: llama3.3-70b-instruct, FP8 quantization
• Serving framework: vLLM 0.24.0
• Workload: 1,024-token inputs and outputs, max_model_len 4096
• Saturated peak: ~2,036 tok/s at a saturating batch

Measured throughput → cost per million output tokens

The load-bearing fact: identical hardware moves from $20.32/M at batch=1 to $0.45/M at batch=128 — a ~44x cost swing driven purely by traffic shape / concurrency, before any change of model, quant, GPU, or framework version. This is an independent, dollar-anchored reproduction of Chitral Patil's (arXiv 2606.11690) $0.21–$15.25/M utilization spread — different SKU (H200 vs H100), different rate, same mechanism.

Utilization cost model

The tutorial generalizes with:

effective_cpm = (hourly_rate ÷ (peak_tps × 3600 × utilization)) × 1,000,000

Worked, measured examples at the H200's peak throughput:

• 73% utilization → $0.643/M output tokens — roughly break-even with DigitalOcean's own Serverless inference at $0.65/M
• 40% utilization → $1.173/M — ~80% more expensive than the serverless equivalent

So the "own-a-GPU vs buy-serverless" crossover for this config sits near ~73% sustained utilization. Below it, dedicated capacity loses to per-token serverless; the cost of a token is a function of how full the box stays, exactly as Patil argued and as the independent-provider literature ("idle time is an existential margin problem") corroborates.

Stated limitations (author's own)

"Results will vary with a different model, quantization level, GPU type, serving framework version, input/output token length, or system load. Treat the measured numbers as a reproducible reference point for this specific configuration, not as universal performance guarantees."

Single-H100 measurements are noted separately at 236–335 tok/s (lower, KV-cache-constrained) — a further data point that the H100/H200 generational gap alone moves cost materially.

Why it matters here

• Closes the topic's #1 gap — the KB now holds a measured $/M-token cost series, dated and hardware-named, not just a price series.
• Anchors the margin debate — Evans' 40–50% and Patel's 70%+ inference gross margins can now be sanity-checked against a real cost floor (~$0.45–$1.17/M for a 70B open model at plausible utilization), though only for this one open-model/SKU point.
• Reproduces the utilization thesis in dollars — the 44x concurrency swing here and Patil's 36.3x are the same finding on different silicon, strengthening the claim that any single "cost per token" number without a stated utilization is meaningless.

Source: Token Economics Across Traffic Profiles on Dedicated GPUs by Vinayak Baranwal, DigitalOcean, 2026-07-08