Inference Endpoint Benchmarking: Accuracy vs. Throughput at Production Scale

Introduction 

When companies start using AI for real instead of just testing, people notice if it’s quick and reliable. Most users don’t care about the details behind the scenes – they just want it to work, without weird delays or glitches.

Inference Endpoint benchmarking becomes critical once AI systems move from testing into real production environments, where concurrency, latency, and cost behavior are exposed under real user load.

It’s important to benchmark how inference actually performs in practice, hence. A single quick response doesn’t tell the full story; real problems appear when many people use the system at once – stretching resources and slowing things down. 

These benchmarks are meant to show how things really work when the pressure is on. Rather than perfect lab tests, they focus on Llama 3.1 and 3.3 Instruct models of all sizes, running on all sorts of hardware and facing real challenges. What matters is how the whole setup handles a busy day, not just how a model does alone. 

This kind of real-world testing helps teams see aspects that  really matter – how many users can access  the system at a time, how fast it reacts, what it’ll cost as things get busier, and where it might break. That way, businesses don’t get caught off guard when they go live. 

We compared Llama 3.1 and Llama 3.3 using two representative models:

• Llama-3.1-8B-Instruct
• Llama-3.3-70B-Instruct

What stands out are two distinct operating regimes:

• Llama-3.1-8B-Instruct models: fast and affordable, good for quick responses 
• Llama-3.3-70B-Instruct models: bigger, better at tough problems, but need more resources 

All our tests used a 128k context window, similar to what businesses actually need when: 

• Searching big documents for answers (RAG systems)
• Handling long conversations
• Dealing with long PDFs, legal, or financial files
• Running internal assistants that need lots of info at once

Each configuration was evaluated using: 

• 1,000 different prompts 
• Inputs with 1,000 tokens 
• Outputs of about 100 tokens 
• Tested with 10, 50, and 100 users at once 
• Used 1 to 8 NVIDIA H100 SXM GPUs, depending on the setup

We also tried out two ways of running the models: 

• Full precision (FP16) 
• Lower precision (FP8) for better efficiency 

We picked metrics that actually matter for businesses, like: 

• How many words/tokens the system can handle per second (throughput)
• How quickly the first answer appears (TTFT) 
• The total time from start to finish (latency) 
• How much memory each GPU needs for the model 

Interpreting Accuracy in Real-World Context

The benchmarks reinforce a clear pattern. Llama-3.1-8B-Instruct models are “good enough” for a large majority of production workloads, especially when paired with retrieval-augmented generation. In support bots, internal copilots, summarization pipelines, and multilingual chat interfaces, smaller models deliver strong results because the heavy lifting is often done by the retrieved context rather than the model’s internal reasoning alone. 

Whereas Llama-3.3-70B-Instruct models consistently outperform in scenarios that demand deeper reasoning or broader contextual understanding. These include analytical workflows, complex synthesis across long inputs, handling rare symbols or encodings, and high-stakes environments where errors carry real consequences – such as KYC automation, compliance checks, or executive decision support. The real takeaway is that performance comes from fit, not from brute scale. Using a 70B model for a simple chatbot rarely improves outcomes enough to justify the cost, while using an 8B model for a high-risk workflow can introduce unacceptable failure modes.

Precision and Quantization: Performance Without Compromise

One of the most consequential findings from this study is the impact of FP8 quantization on inference workloads. 

Across both Llama-3.1-8B-Instruct and Llama-3.3-70B-Instruct, moving from FP16 to FP8 roughly halves model memory per GPU:

• For 8B, model memory drops from ~15 GiB per GPU (FP16) to ~8.5 GiB (FP8) on a single H100
• For 70B, model memory drops from ~32–33 GiB per GPU (FP16) to ~16–17 GiB (FP8), enabling configurations that are otherwise infeasible

These memory savings translate directly into performance gains. For example:

• At concurrency 10 on 8 GPUs, 8B FP8 delivers ~2,516 tokens/sec compared to ~1,983 tokens/sec for FP16, while reducing TTFT from ~48 ms to ~15 ms
• At concurrency 50 on 8 GPUs, 70B FP8 achieves ~3,266 tokens/sec versus ~2,978 tokens/sec for FP16, with slightly lower TTFT

In practical terms, FP8 emerges as a default choice for production inference. FP16 remains valuable for training and select validation workflows, but for serving models at scale, Quantization delivers materially better efficiency without sacrificing user experience. 

Throughput, Latency, and the Cost of Concurrency

In Inference Endpoint benchmarking, understanding how throughput and latency change under concurrency matters more than single-user performance numbers.

As concurrency increases, throughput scales – but never linearly. Understanding where that scaling bends is critical for production planning. 

For 8B models, scaling is relatively graceful. On 8 GPUs, throughput increases significantly as concurrency rises from 10 to 100, while median end-to-end latency remains comfortably under a second. This makes smaller models particularly well-suited for high-traffic, latency-sensitive applications. 

70B models behave differently. Without careful configuration, latency increases sharply as concurrency rises. In FP16 configurations with fewer GPUs, end-to-end latency can balloon to multi-second or even tens-of-second levels under load – unacceptable for most user-facing systems. 

Quantization and additional GPUs mitigate this effect. A 70B FP8 deployment on 8 GPUs handles higher concurrency far more gracefully, delivering materially better throughput and keeping latency within reasonable bounds. The difference between a usable and an unusable deployment often comes down to these architectural choices rather than the model itself. 

GPU Scaling and Diminishing Returns 

Another consistent pattern across the benchmarks is diminishing returns from GPU scaling. 

For Llama 3.1 Instruct 8B (FP16) at concurrency 50:

• Scaling from 2 → 4 GPUs increases throughput from ~3,188 to ~7,773 tokens/sec (~2.4×)
• Infrastructure cost roughly doubles, but tokens/sec per rupee improves

At concurrency 100:

• Scaling from 2 → 4 GPUs improves throughput from ~3,844 to ~5,352 tokens/sec (~1.4×)
• Cost still doubles, indicating diminishing returns

At low concurrency (10), the same scaling yields only ~1.25× throughput improvement, with GPUs often sitting under-utilized.

Doubling the number of GPUs does not double throughput. At moderate concurrency, scaling from 2 to 4 GPUs often delivers strong gains and improves cost efficiency. At very high concurrency, the incremental benefits shrink as queueing effects and token contention begin to dominate. 

At low concurrency, additional GPUs often sit underutilized, offering little improvement in latency or throughput while significantly increasing costs. This highlights an important operational principle: GPU count should be driven by expected token load and concurrency, not peak theoretical capacity. 

Over-provisioning infrastructure without matching demand is one of the fastest ways to burn budget without improving user experience. 

Practical Insights and Deployment Guidance

Taken together, the benchmarking data points to a few clear patterns that teams can apply immediately. 

For most production systems, especially chatbots, RAG pipelines, and internal tools – Llama 3.1 Instruct 8B running in FP8 on 2 to 4 GPUs offer the best balance of responsiveness, throughput, and cost efficiency. 

For premium or high-risk workloads that genuinely require deeper reasoning, Llama-3.1-8B-Instruct and Llama-3.3-70B-Instruct in FP8 on 8 GPUs represent a practical sweet spot. It significantly improves performance with fewer GPUs while keeping memory usage manageable. 

Quantization should be treated as a standard inference optimization, not an optional tweak. The gains in throughput and efficiency consistently outweigh the marginal accuracy trade-offs for most enterprise use cases. 

Finally, concurrency planning matters more than raw user counts. Inference systems scale on tokens, not people. Understanding input length, output length, and burst behavior is essential to avoiding saturation and unpredictable latency.

Choosing the Right Configuration, Not the Biggest Model 

The data points toward clear usage patterns. 

If you want a system that’s fast, affordable, and can talk to lots of people at once, like help bots or team assistants – 8B hits the sweet spot. 

If you need smarter answers or extra safety, 70B is the way to go – but only if you’ve got the hardware and budget for it. 

But adding more GPUs doesn’t always mean you get twice the results for twice the money. Sometimes, you’re paying extra just to keep things running smoothly, not to handle more work overall. 

Why This Matters for Inference Infrastructure 

All these tests make one thing clear, how well AI works isn’t just about the model. It’s about how everything – hardware, memory, software, and more comes together when people use it for real. 

That’s why Neysa looks at the whole system, not just the model. Velocis helps teams see these trade-offs so they can make choices that actually fit how they’ll use AI – not just what looks best on paper. 

By matching hardware and pricing to how AI really works in practice, Neysa helps teams focus on what counts – staying fast, staying affordable, and handling real-world demand. 

Conclusion

Benchmarking isn’t about flexing peak numbers in perfect conditions, rather about seeing what breaks when traffic surges, latency jitters, and real users start clicking faster than your dashboards can refresh.

The takeaway is simple: good architecture beats raw horsepower. Right-sized models, sensible limits, and realistic concurrency planning will outperform brute-force scaling every time. Test for the messy middle of production, not the happy path, and your benchmarks will actually mean something.

This study shows why Inference Endpoint benchmarking must reflect real traffic patterns, precision choices, and deployment constraints to remain meaningful in production because nobody cares who ran the biggest model. They care about who stayed fast, stable, and sane when things got busy.