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Benchmark a self-hosted AI assistant by measuring three things separately: how quickly an individual user gets a response, how much work the server handles under load, and how much electricity and money that useful output costs. A single tokens-per-second figure cannot answer all three. Keep the model, workload, measurement boundaries, and quality checks consistent, then report the conditions alongside the results.
What to measure—and why one speed number is not enough
For an interactive user, the first wait and the pace of streamed output matter. For a server handling multiple requests, aggregate throughput and request capacity matter. Energy efficiency and cost add another dimension, and a faster configuration is not a useful improvement if it fails the assistant’s tasks.
| Question | Useful measures | What the measures tell you |
|---|---|---|
| How responsive is one request? | Time to first token (TTFT), inter-token latency (ITL), and end-to-end latency | How long the user waits to see a response, how quickly streamed output arrives, and how long the full request takes. |
| How much work can the server handle? | Aggregate output tokens per second and requests per second at stated concurrency or request rate | System-wide capacity across requests, not the speed experienced by one user. NVIDIA AIPerf distinguishes per-user from aggregate throughput in its metrics reference. |
| How much energy does generation use? | GPU energy or power; whole-system wall energy; energy per output token or tokens per joule | Efficiency within the stated measurement boundary. GPU energy excludes the rest of the host. |
| Does the configuration do the job? | A fixed task-quality measure and an explicit acceptance threshold | Whether the measured speed and capacity produce useful answers at an acceptable quality level. |
Fix the configuration before comparing runs
Record the full setup so a later result can be reproduced and differences can be attributed to a change rather than an undocumented setting. vLLM’s benchmarking CLI documentation illustrates the need to specify details such as model, endpoint, backend, dataset, and request count.
- Assistant and model name, exact model revision, and tokenizer.
- Serving engine and version; GPU and CPU models; RAM; and accelerator count.
- Precision or quantization, context limit, and generation settings.
- Batching, caching, and other relevant serving options.
- Streaming mode, prompt and output lengths, request rate or concurrency, and the dataset or workload used.
Hold these settings constant when comparing configurations unless the purpose of the comparison is to test one of them. If you change multiple settings at once, the benchmark may show that performance changed without showing why.
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- 64GB DDR5 RAM + 1TB SSD - The EVO-T1 is equipped with Dual 32GB (Total 64GB) SO-DIMM DDR5 5600MHz memory sticks. 2TB PCIE 4.0 SSD Drive with 3x M.2 2280 Expansion slots. Each slot capable of reading up to 4TB. (12TB MAX)
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Build a workload that resembles real use
Prompt length, answer length, request arrival pattern, concurrency, and streaming behavior all affect performance. Use representative prompts only when privacy and licensing allow. Otherwise create a synthetic workload that matches typical prompt and response lengths, and label it as synthetic rather than implying it represents production traffic.
Measure at least one low-load interactive case and a range of concurrency or request rates through the point where the system approaches saturation. Include a warm-up period, a sufficiently long steady measurement interval, and multiple runs. Keep the output-length limits and sampling configuration consistent: they influence both latency and token rates. The vLLM documentation describes dataset-driven serving benchmarks and percentile reporting controls.
Measure responsiveness and serving capacity separately
Per-request responsiveness
- TTFT: elapsed time from request submission to the first streamed output. It captures the initial wait.
- ITL: time between streamed outputs. It describes the pacing a user sees once generation begins.
- End-to-end latency: total duration of a request. Report its distribution, not only its mean.
For each tested load, report a median and tail percentiles such as p95 or p99 when there are enough observations to make those percentiles meaningful. State precisely where timing begins and ends, especially if client-side and server-side timestamps are both available.
Rank #2
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- OCULINK PORT - The Oculink port on the rear interface enables higher bandwidth capabilities, better frame rates and lower lag. The standard also operates at PCIe x4 speeds, compared to Thunderbolt's x3. Gamers and content creators can benefit from Oculink's higher bandwidth, resulting in better performance and lower lag for eGPU setups
- DUAL NIC FAST 2.5GBE + WIFI 6E + BT 5.2 - Dual Ethernet 2.5GbE LAN port design provides more applications, such as firewall, multichannel aggregation, soft routing, file storage server. Built-in WIFI 6E / Bluetooth 5.2 is more stable and efficient to connect multiple wireless devices such as projector, printer, monitor, speakers and etc
System-wide capacity
Report aggregate output tokens per second and request throughput at each tested concurrency or request rate. Aggregate output tokens per second means output tokens across requests divided by the benchmark interval; it is not an individual user’s streaming speed. Keep it separate from per-user throughput, which answers a different question. The distinction is described in NVIDIA AIPerf’s metrics reference.
Name the timing formula and inspect server telemetry
Metric labels are not always calculated identically across tools. vLLM describes ITL as measured between streamed outputs and TPOT as request decode time amortized across output tokens. These can diverge with speculative decoding, where one output event may include multiple tokens. If you report TPOT, name the tool’s formula and measurement point; do not treat it as interchangeable with ITL. See the vLLM benchmarking CLI documentation.
Use server-side telemetry to explain results, not just rank configurations. Queue time, prefill time, decode time, prompt and generation token counts, and success, error, or abort counts can show where time and capacity are being spent. These interfaces vary by serving implementation and version; NVIDIA documents examples in its AIPerf server metrics reference.
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Measure electricity at a clearly stated boundary
GPU energy is not the same as electricity consumed by the complete machine. A GPU-level reading can help compare accelerator efficiency, but a whole-host cost estimate needs wall-energy measurement, for example with a suitable wall meter or metered power source. State which boundary you measured and whether the interval includes idle time, warm-up, and service overhead.
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Where the corresponding measures are available, calculate GPU energy per output token or output tokens per joule. NVIDIA AIPerf documents total GPU energy, energy per output token, and output tokens per second per watt in its metrics reference. Label these as GPU-level figures rather than whole-system energy.
- Measure whole-system wall energy over a representative interval using an appropriate meter.
- Record the interval and what it includes, along with useful output tokens generated during that same interval.
- Apply your actual electricity tariff to the measured energy. State the tariff and its units rather than assuming another reader pays the same rate.
- For a broader cost per million useful output tokens, define the costs included in the numerator, then divide by useful output over the same accounting period.
A broader operational estimate may include electricity, hardware amortization over an explicitly assumed service life, and recurring hosting or maintenance costs. Show electricity-only and fully loaded estimates separately if both are useful. Utilization and idle time can materially affect the result, so disclose how they were treated. A benchmark cost should not be transferred to a different machine, tariff, workload, or utilization level as though it were a universal rate.
Rank #4
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Include answer quality and choose an operating point
Run a fixed task-quality set alongside performance tests. Describe the evaluation method and the accuracy or acceptance threshold; do not call a faster setup better if it no longer meets the assistant’s intended task requirements. Choose a point on the throughput, latency, and energy trade-off curve that meets both the desired response-time and quality needs.
Published benchmark results are useful only with their context. MLPerf reporting includes system, workload, target accuracy, and dataset information; NVIDIA’s MLPerf AI Benchmarks page provides benchmark context, while its inference performance page covers workload-specific performance. Those results are not a substitute for testing your own workload and measurement boundary.
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Make comparisons reproducible
When comparing two or more systems, use the same workload and report the configuration and measurement boundary with every result. A useful comparison includes:
- TTFT and tail latency at interactive load.
- Aggregate output throughput and request throughput at stated concurrency or request rates.
- Energy and cost per useful output, with GPU-only versus whole-system measurement made explicit.
- Task quality or target accuracy under the stated evaluation method.
- Operational constraints such as memory capacity and stability.
Keep the report detailed enough that another reader can tell what was measured: model revision, engine version, hardware, workload, run interval, metric definitions, and cost assumptions. Without that context, a tokens-per-second result is difficult to interpret and cannot establish which setup is more useful or less expensive.
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