Each week, new fashions are launched, together with dozens of benchmarks. However what does that imply for a practitioner deciding which mannequin to make use of? How ought to they method assessing the standard of a newly launched mannequin? And the way do benchmarked capabilities like reasoning translate into real-world worth?
On this publish, we’ll consider the newly launched NVIDIA Llama Nemotron Super 49B 1.5 mannequin. We use syftr, our generative AI workflow exploration and analysis framework, to floor the evaluation in an actual enterprise downside and discover the tradeoffs of a multi-objective evaluation.
After inspecting greater than a thousand workflows, we provide actionable steerage on the use instances the place the mannequin shines.
The variety of parameters depend, however they’re not all the pieces
It ought to be no shock that parameter depend drives a lot of the price of serving LLMs. Weights have to be loaded into reminiscence, and key-value (KV) matrices cached. Greater fashions usually carry out higher — frontier fashions are nearly at all times large. GPU developments have been foundational to AI’s rise by enabling these more and more massive fashions.
However scale alone doesn’t assure efficiency.
Newer generations of fashions typically outperform their bigger predecessors, even on the similar parameter depend. The Nemotron fashions from NVIDIA are a great instance. The fashions construct on current open fashions, , pruning pointless parameters, and distilling new capabilities.
Which means a smaller Nemotron mannequin can typically outperform its bigger predecessor throughout a number of dimensions: sooner inference, decrease reminiscence use, and stronger reasoning.
We wished to quantify these tradeoffs — particularly in opposition to a few of the largest fashions within the present era.
How rather more correct? How rather more environment friendly? So, we loaded them onto our cluster and set to work.
How we assessed accuracy and value
Step 1: Determine the issue
With fashions in hand, we would have liked a real-world problem. One which checks reasoning, comprehension, and efficiency inside an agentic AI circulation.
Image a junior monetary analyst attempting to ramp up on an organization. They need to be capable of reply questions like: “Does Boeing have an enhancing gross margin profile as of FY2022?”
However additionally they want to elucidate the relevance of that metric: “If gross margin is just not a helpful metric, clarify why.”
To check our fashions, we’ll assign it the duty of synthesizing knowledge delivered by an agentic AI circulation after which measure their skill to effectively ship an correct reply.
To reply each varieties of questions appropriately, the fashions must:
- Pull knowledge from a number of monetary paperwork (comparable to annual and quarterly experiences)
- Evaluate and interpret figures throughout time durations
- Synthesize an evidence grounded in context
FinanceBench benchmark is designed for precisely this sort of activity. It pairs filings with expert-validated Q&A, making it a robust proxy for actual enterprise workflows. That’s the testbed we used.
Step 2: Fashions to workflows
To check in a context like this, you’ll want to construct and perceive the complete workflow — not simply the immediate — so you’ll be able to feed the fitting context into the mannequin.
And it’s important to do that each time you consider a brand new mannequin–workflow pair.
With syftr, we’re capable of run lots of of workflows throughout completely different fashions, shortly surfacing tradeoffs. The result’s a set of Pareto-optimal flows just like the one proven beneath.
Within the decrease left, you’ll see easy pipelines utilizing one other mannequin because the synthesizing LLM. These are cheap to run, however their accuracy is poor.
Within the higher proper are probably the most correct — however extra costly since these usually depend on agentic methods that break down the query, make a number of LLM calls, and analyze every chunk independently. Because of this reasoning requires environment friendly computing and optimizations to maintain inference prices in test.
Nemotron exhibits up strongly right here, holding its personal throughout the remaining Pareto frontier.
Step 3: Deep dive
To higher perceive mannequin efficiency, we grouped workflows by the LLM used at every step and plotted the Pareto frontier for every.
The efficiency hole is evident. Most fashions battle to get anyplace close to Nemotron’s efficiency. Some have hassle producing cheap solutions with out heavy context engineering. Even then, it stays much less correct and dearer than bigger fashions.
However after we swap to utilizing the LLM for (Hypothetical Doc Embeddings) HyDE, the story modifications. (Flows marked N/A don’t embrace HyDE.)
Right here, a number of fashions carry out effectively, with affordability whereas delivering excessive‑accuracy flows.
Key takeaways:
- Nemotron shines in synthesis, producing excessive‑constancy solutions with out added value
- Utilizing different fashions that excel at HyDE frees Nemotron to deal with high-value reasoning
- Hybrid flows are probably the most environment friendly setup, utilizing every mannequin the place it performs finest
Optimizing for worth, not simply dimension
When evaluating new fashions, success isn’t nearly accuracy. It’s about discovering the fitting steadiness of high quality, value, and match on your workflow. Measuring latency, effectivity, and general affect helps make sure you’re getting actual worth
NVIDIA Nemotron fashions are constructed with this in thoughts. They’re designed not just for energy, however for sensible efficiency that helps groups drive affect with out runaway prices.
Pair that with a structured, Syftr-guided analysis course of, and also you’ve acquired a repeatable method to keep forward of mannequin churn whereas conserving compute and price range in test.
To discover syftr additional, try the GitHub repository.
