Is Accelerated Serving Truly a Beautiful Technology? What I Learned from Calling the API Directly
What If We Could Run the Same Model Much Faster?
When developing AI models or running services, you inevitably run into performance bottlenecks. No matter how capable a model is, if users have to wait dozens of seconds for a response, the user experience will inevitably degrade. Especially in domains where response latency dictates practical viability—such as real-time dialogue, speech recognition, and clinical environments—speed ceases to be just a raw performance metric and becomes the core competitiveness of the product itself.
That is why I became interested in the field of inference acceleration.
What sparked my curiosity was not simply using faster hardware or shrinking the model. What if we could preserve the existing model’s full capabilities while processing the exact same inference workloads substantially faster?
Reducing model size or swapping in a model with fewer parameters can boost speed. But strictly speaking, that is different from accelerating the same model. When the underlying model changes, its performance characteristics can shift as well.
What I look forward to is a slightly different direction: technology that dramatically improves execution efficiency without sacrificing model intelligence.
If this becomes possible, the very way we design AI services could change. Until now, teams often had to accept slow responses for high quality, or settle for smaller models when fast responses were required. But if we can increase speed while preserving quality, we can narrow the compromise between these two choices.
I found this possibility remarkably compelling.
Of course, being technically feasible and being consistently realized in production services are two entirely different matters. So I decided to call the API directly.
What I Wanted to Check Before the Documentation
When looking up technology overviews or benchmarks online, it is easy to encounter impressive figures—claims of being several times faster than existing baselines or demonstrating massive throughput gains. But from the perspective of someone building real-world services, it is difficult to judge on such numbers alone.
Achieving high throughput in a synthetic benchmark environment and having the API I call respond quickly every single time are two different problems.
For example, even if an infrastructure can process thousands of tokens per second, if my request has to queue before processing, the perceived speed can be slow. Conversely, even if overall throughput is modest, if the initial response for a single request arrives quickly and consistently, it can be far more suitable for real-time services.
In the end, what I wanted to know was straightforward:
- When making actual API calls, how quickly does the first response arrive?
- How consistent are response times when called repeatedly under identical conditions?
- How does performance diverge between short requests and long requests?
- Are inference-related configurations properly reflected in the actual response?
- And above all: when speed increases, is model quality preserved?
The last question is the most critical. However, this experiment did not verify that final item. I first examined the API’s response times and configuration behavior.
This test is not a comprehensive benchmark intended to evaluate a specific company’s overall technology. It is a scoped, empirical experiment observing phenomena one might encounter when using the API in actual services.
Calling OpenGateway (OGW) Directly
The target evaluated in this test was the OpenGateway (OGW) API provided by Sionic AI Inc., a Korean corporation. The invoked model was deepseek/deepseek-v4.1-flash-ultrafast, and requests were configured with the PAID_STANDARD traffic class.
Taking care to ensure API keys were never exposed in payloads or logs, I measured response times. The metrics tracked closely here were TTFB (Time to First Byte), total response time, and the x-envoy-upstream-service-time header included in the response.
TTFB is the duration from sending the request to receiving the first byte. In streaming responses, it helps gauge the time until the first token reaches the user. Total response time is the elapsed duration from dispatching the request until the response completes entirely.
The two values may seem similar, but their implications differ.
For instance, even if the first token arrives in 0.4 seconds, if generating the remainder of the response takes 20 seconds, the user sees initial output early but must wait a long time for the task to finish. Conversely, even if the first response is delayed, if subsequent tokens are generated rapidly, the overall response time can remain relatively short.
Therefore, it is difficult to evaluate an API using a single “speed” number alone.
Large Variances Observed in Short Requests
First, with reasoning disabled, I made five streaming calls with the short prompt Hello.
| Run | TTFB | Total Response Time |
|---|---|---|
| 1 | 2.585s | 3.541s |
| 2 | 5.924s | 6.935s |
| 3 | 7.181s | 7.200s |
| 4 | 0.421s | 0.475s |
| 5 | 0.300s | 0.354s |
In the fastest run, the initial response arrived in about 0.3 seconds. In another run, however, it took over 7 seconds to receive the first response.
By total response time as well, the fastest run completed in 0.354 seconds, while the slowest took 7.2 seconds—roughly a 20-fold difference.
Because this was a short request, the time spent generating output tokens was relatively minor. If total time varied this dramatically despite that, at least within these measurements, response times cannot be explained solely by the speed at which the model generates tokens.
Request queuing, routing, server-side processing, concurrent load, inference server state, and various other factors could have played a role. However, this experiment alone cannot pinpoint which factor was the primary cause.
Here, a distinction must be drawn: A single fast response proves that the API is capable of responding quickly, but it is not proof that it will always be fast.
In production services, the distribution of response times matters just as much as peak speed. Even if the average is low, if a portion of requests experiences extreme delays, users will inevitably feel that latency. In voice conversations or real-time interfaces in particular, a few worst-case requests can define the impression of the entire service.
Server-Side Processing Times Were Also Inconsistent
I also separately checked the x-envoy-upstream-service-time header values.
| Run | Upstream service time |
|---|---|
| 1 | 6110ms |
| 2 | 4616ms |
| 3 | 932ms |
| 4 | 509ms |
| 5 | 341ms |
This value spanned from approximately 341ms to 6110ms. At the very least, the server-side processing segment indicated by this header also exhibited substantial variance across runs.
Of course, this header alone cannot reveal exactly what transpired inside the server. How the duration was split between request queue wait times and actual model inference, or whether different runs were handled by the same server or identical hardware, cannot be confirmed from this data alone.
Nonetheless, the observation itself holds value. It cannot simply be dismissed as a client or network issue, because substantial differences appeared directly within the upstream interval recorded by the response header.
Relying on the model name ‘ultrafast’ alone was not enough to expect consistently blazing-fast responses.
Long Requests Exhibited Even More Extreme Differences
Short requests alone are insufficient to properly evaluate generation speed. Thus, I also tested a longer prompt requesting an essay of approximately 150 words. Streaming was used here as well, with reasoning turned off.
| Run | TTFB | Total Response Time | Output Length |
|---|---|---|---|
| 1 | 1.092s | 4.327s | 746 chars |
| 2 | 0.370s | 7.660s | 874 chars |
| 3 | 5.664s | 79.954s | 833 chars |
The first run finished in about 4.3 seconds. In the second run, the initial response arrived in 0.37 seconds, but the full response took 7.66 seconds. In the third run, reaching the first response took about 5.7 seconds, and total completion time stretched to nearly 80 seconds.
Considering that the output lengths were comparable at 746 to 874 characters, this is a remarkably wide variance.
One cannot definitively diagnose the cause from this data alone. Server load, queue delays, mid-generation throttling, or transient infrastructure anomalies are all plausible explanations. Nor can three runs be claimed to represent general performance.
Yet from the perspective of an actual API user, an important fact remains: when sending requests of similar length to the same endpoint, the time required to complete a response could vary drastically.
As a side note, roughly estimating token counts by dividing character count by four yields an approximate generation speed of 2.6 to 43 tokens per second for these long responses. However, this is not a measurement derived from an actual tokenizer. Because the relationship between character count and token count shifts across languages and sentence structures, and because total response time includes non-generation overhead, this should not be taken as an exact TPS metric.
Comparing precise generation speed requires recording actual output token counts and separating the time to the first token from the active generation window. If the server provides throughput metrics, those should be checked separately as well.
What these results illustrate is less about a strictly defined model generation speed, and more about the overall response experience observed when consuming the API.
Does Disabling Reasoning Actually Make a Difference?
Just as intriguing as the wide variance in response times were the reasoning-related configurations.
Certain models execute a separate reasoning process prior to composing their final answer. While this process can aid in solving complex problems, depending on the task and configuration, it may demand additional tokens and latency.
Thus, for straightforward greetings or short queries that do not require deep reasoning, disabling reasoning can be a rational choice.
In this test, I compared the following configurations:
reasoning_effort: "none"thinking: { "type": "disabled" }enable_thinking: false
According to the observations, requests specifying reasoning_effort: "none" yielded 0 characters in reasoning_content, and requests using thinking: { "type": "disabled" } likewise exhibited no reasoning output. In contrast, when specifying enable_thinking: false, reasoning content continued to be generated.
At least across this specific endpoint and model combination, parameters with similar-sounding names did not all behave identically.
A caveat here is that reasoning content not appearing in the response does not necessarily mean all internal reasoning computations were eliminated. What was observed in this test is the behavior of the configurations and the presence of reasoning output in the response. Direct measurements of internal compute or GPU utilization were not conducted.
Furthermore, parameter support can vary across API providers. One should never assume that the settings supported by a base model, the settings forwarded by the gateway, and the behaviors reflected in the final response will always align.
In real-world services, such discrepancies matter more than one might expect. Attempting unverified parameters absent from documentation or using flags that merely sound similar can lead to desired behaviors failing to take effect.
Ultimately, one must verify the actual response rather than rely on parameter names. If the goal is to disable reasoning, it is better to confirm whether reasoning-related fields disappear, how response times shift, and how output quality on relevant tasks is affected.
That said, this experiment did not compare output quality between states with reasoning enabled versus disabled. Therefore, one cannot conclude that disabling reasoning is always superior or that it leaves quality untouched.
Does Streaming Make a Model Faster?
This experiment also prompted me to reconsider the meaning of streaming.
Streaming is not necessarily a technique that speeds up the model’s underlying computation. The crux lies in delivering generated results incrementally as they become ready, rather than holding everything until full completion.
In non-streaming approaches, the client typically receives nothing until the entire response is completed. In streaming, by contrast, once the first token is ready, results can be presented while the remaining generation continues in the background.
This distinction directly shapes the user experience.
For instance, even for a task taking 8 seconds total, if the first token arrives in 0.4 seconds, the user perceives that the system responded immediately. Conversely, if one waits 8 seconds for the first response only to have all the text appear at once, the exact same total duration feels significantly more sluggish.
However, using streaming should never lead one to assume total generation time has shrunk. In this experiment as well, requests whose initial responses arrived quickly were not necessarily the ones that finished first overall.
When evaluating API performance, therefore, at least three metrics must be separated:
- TTFB: The time until the user first sees a result.
- Generation speed: How rapidly output flows after the first token.
- Total completion time: The duration from dispatching the request until the entire result is complete.
While these three metrics are correlated, they are not the same. Streaming offers an advantage in surfacing initial output quickly, but it cannot solve issues where generation stalls or the server incurs prolonged delays.
In production services, streaming should be adopted as the default, yet handling strategies for extended responses must also be designed. Approaches such as timeouts, request cancellation, retries, and failovers to fallback endpoints may be necessary.
Retries in particular are not a capability to apply unconditionally. If a connection drops mid-generation and the entire request is retransmitted, API costs may duplicate, or users who already received partial output might see repeated responses. Implementations must align with the nature of the request and the provider’s retry policies.
Did Accelerated Serving Itself Fall Short of Expectations?
A distinction is required here.
What was observed in this test represents the response times and configuration behavior of a specific API endpoint. From these results alone, one cannot conclude that inference acceleration technology itself is ineffective.
An API’s actual performance is shaped by multiple layers. Beyond the model architecture and inference engine, hardware, batching mechanisms, request queues, routing, networking, concurrent user volumes, and provider operational policies all exert influence.
Even if a provider leverages superior acceleration algorithms, lengthy wait times in the serving layer will drag down the perceived speed experienced by users. Conversely, even with modest differences in acceleration tech, disciplined infrastructure operations can deliver a crisp response experience.
Interpreting these findings as a performance flaw in a specific algorithm is therefore inappropriate. What this test established is simply that the actual responses received through the endpoint were not as consistent as expected.
This article was not written to criticize OpenGateway. I feel glad and proud that Sionic AI Inc., a Korean corporation, is building this kind of AI inference-serving service. The reason I recorded these measurement results is out of hope and expectation that the service will become faster and more stable.
Even so, these differences cannot be ignored. Users do not directly experience the internal architecture of an inference engine; they interact with the service through an API. From the vantage point of shipping real products, regardless of algorithmic potential, the stability of a service’s response time is what truly matters.
I believe technological promise and service maturity must be evaluated separately.
How Do We Verify ‘Same Quality, Faster Speed’?
When assessing inference acceleration, perhaps the hardest question is not how much faster it became, but what was altered to achieve that speed.
For example, quantization can alter model weight representations to lower precision, trimming memory footprints and compute costs. However, depending on the quantization scheme, the model, and the task, differences in output quality can arise.
In contrast, properly implemented speculative decoding can accelerate the generation process by having a target model verify tokens proposed by a smaller draft model or similar mechanism. Under specific algorithmic conditions, it can boost generation speed while strictly preserving the output probability distribution of the target model. Yet this is not a property automatically guaranteed by all acceleration methods.
Here, the phrase ‘identical quality’ also requires precise definition.
Verification methodologies diverge depending on whether it means every single generated sentence across two runs must match verbatim, whether the identical output distribution is maintained, or whether practical task-solving capabilities remain statistically equivalent.
Even on the same model, differing sampling configurations and random seeds can alter output sentences. Therefore, output variation alone cannot substantiate a degradation in quality. Conversely, output similarity alone does not guarantee quality was fully preserved.
To genuinely benchmark quality, one must employ identical prompt sets and align model versions and configurations as closely as possible. For deterministic tasks with ground truths, accuracy or task completion rates should be measured; for open-ended generation, rubric criteria and blind evaluation protocols should be set in advance.
Speed, too, is more informative when evaluated across medians and tail latencies—namely P95 or P99—through a sufficient number of repeated measurements rather than relying solely on averages. Testing across short versus long requests, and under low versus high load conditions, further clarifies under what circumstances performance gaps emerge.
Of course, not every evaluation needs to be conducted all at once. One can start by identifying the request patterns vital to production services, and comparing the baseline model against the accelerated model under those conditions.
What I ultimately want to verify is precisely this: Not merely that speed increased, but how fast and how stably execution holds while fully maintaining quality.
Planning to Test Other Providers Next
Looking strictly at this experiment, the results did not meet expectations.
In the fastest run, a short response wrapped up in roughly 0.35 seconds, but in comparable requests, total response times exceeded 7 seconds. With longer requests, one run consumed nearly 80 seconds. Reasoning configurations also behaved inconsistently across parameters.
Naturally, because the number of runs was small and server load or internal processing states could not be controlled, these results cannot be generalized. It may have been a transient phenomenon during a specific time window, or an issue limited to the specific configuration tested.
Hence, this experiment serves as a departure point rather than a conclusion.
I plan to test APIs from other providers under comparable conditions. Where possible, using identical models or comparable baseline models, sending repeated short and long requests, and logging TTFB, total response times, output token counts, and latency distributions. Following that, I want to verify the practical benefits of acceleration with full quality benchmarks.
Only through that process can one distinguish operational quirks of a specific endpoint from the boundaries of acceleration technology itself.
Why I Still Hold High Hopes for This Technology
Just because these results left something to be desired does not mean my hope for the direction of inference acceleration has vanished.
Rather, it reinforced the feeling that identifying the gap between technological potential and real-world services is what truly matters. Whether technology demonstrating promise in research papers or tech demos delivers consistent performance across production APIs requires separate verification. Bridging that very gap is what transforms technology into viable products.
If we can execute computation more efficiently without diminishing model capabilities, trim unnecessary calculations and idle wait times, and enable more people to access AI faster—that, in my view, is a genuinely beautiful technology.
The race to scale model size alone is important, but how efficiently we can leverage models already trained is equally critical. Delivering faster responses on identical hardware and models impacts costs, power consumption, user experience, and service scalability all at once.
Yet the core questions remain to be verified:
Can we truly increase speed dramatically while preserving the exact quality of the same model? And does that performance reproduce reliably in production APIs?
The OpenGateway (OGW) API I tested did not deliver the consistent speed I had hoped for. But it is premature to judge the technology on this alone. I will need to test other providers and compare output quality before reaching a firmer conclusion.
I remain intrigued by this technology. And because my expectations are high, I want to verify it through empirical, measured data rather than marketing copy or cherry-picked records.
In the end, what I seek is not merely a fast API: It is an inference technology fast and reliable enough to trust in production services, without sacrificing model capabilities.
Whether such technology can be found—I look forward to the next test.