Groq 3 LPX benchmark interpretation
NVIDIA Groq 3 LPX Performance: Benchmarks and TPS Explained
Groq 3 LPX has a notable third-party result: Artificial Analysis measured 3,431 output tokens per second on Gemma 4 31B with a 100,000-token context. The number is impressive, but it is meaningful only with the model, context and benchmark method attached. This guide shows how to turn benchmark TPS into response-time scenarios without pretending it is a universal capacity rating.
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Groq 3 LPX TPS and Completion-Time Calculator
Enter your own workload or capacity assumptions. Public LPX benchmarks are workload-specific, and this calculator does not certify application performance, electrical design, cooling or OEM compatibility.
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Quick answer
The published benchmark is 3,431 output tokens per second on a specific test
NVIDIA reports that Artificial Analysis measured 3,431 output TPS on Gemma 4 31B at 100K context. NVIDIA's press release rounds that to 3,400 and says the result was 4x faster than the nearest alternative platform in that test. A separate SPEED-Bench result cited by NVIDIA reports 4,767 median output TPS for coding-oriented tasks. These figures should not be mixed or extrapolated to unrelated models.
Benchmark the service shape you will actually sell
Use vendor and third-party results to identify promising hardware, then replay your own prompt distribution, context lengths, output lengths and concurrency. The calculator translates a measured TPS into completion time and request capacity so you can compare like with like.
Published Groq 3 LPX performance references
Keep the benchmark identity attached to every number. Different tests answer different performance questions.
| Reference | Reported result | Workload detail | Safe interpretation |
|---|---|---|---|
| Artificial Analysis 100K context | 3,431 output TPS | Gemma 4 31B, 100K context | Evidence of very high interactivity on that benchmark |
| NVIDIA press release summary | 3,400 output TPS | Rounded version of the Artificial Analysis result | Do not treat rounding as a separate benchmark |
| NVIDIA comparison statement | 4x faster responsiveness | Nearest alternative platform in the cited test | Specific comparison, not 4x versus every GPU |
| SPEED-Bench | 4,767 median output TPS | Coding-oriented benchmark reported in NVIDIA technical material | Different benchmark; do not average with 3,431 |
| Production service | Your measured TPS | Exact model, context, quality and concurrency | Use this number for capacity planning |
Before you use the result for procurement
Benchmark the exact workload
Keep the model, context length, output length, concurrency, precision and serving-software version beside every TPS result.
Separate context from generation
LPX is positioned around low-latency token generation; Rubin and Rubin CPX handle different context-heavy work. Measure both phases.
Treat rack specs as architecture
LPUs, SRAM and internal bandwidth explain the system design but do not by themselves predict application throughput.
Verify the current OEM design
Use qualified system documentation for rack power, cooling, networking, serviceability and supported configuration before procurement.
Why 3,431 output TPS is significant
Artificial Analysis measured 3,431 output tokens per second on Gemma 4 31B while the benchmark carried a 100,000-token context. High output speed is harder to maintain when a service also has substantial context, so the result supports NVIDIA's claim that LPX is aimed at interactivity under long-context conditions.
The result is especially relevant to agentic workloads because a coding or research agent may wait on many sequential generations. Faster output can shorten the critical path even when total fleet throughput is already high.
The exact model name belongs beside the number
Token-generation speed changes dramatically with model size and architecture. Gemma 4 31B is therefore part of the benchmark identity, not a footnote. A smaller model could produce far more tokens per second, while a larger mixture-of-experts model can have very different compute and communication behavior.
Any internal performance deck should write the model, context, precision and software next to the TPS number. Removing those details creates a misleading “hardware speed” metric that cannot be reproduced.
The 100K context condition is also essential
Long context increases the work required around attention and cache management. NVIDIA highlights the 100,000-token condition because agentic sessions accumulate prior turns, files, tool output and retrieved information. Maintaining high generation speed at that context is more informative than a short-prompt demonstration for the target use case.
Your service may operate at 8K, 128K, 500K or one million tokens. Performance can shift materially across that range. Build a context-length curve instead of testing only one point.
Output TPS is not the same as time to first token
A user experiences at least two phases: waiting for the first generated token and then receiving the rest of the response. Output TPS measures the generation rate once output is flowing. Time to first token is strongly influenced by prefill, queueing, routing and context processing.
An agent can still feel slow if prefill takes several seconds even when decode is extremely fast. Capture TTFT and generation rate separately, then combine them into total request completion time. The calculator includes a non-generation overhead field for this reason.
Per-user interactivity and fleet throughput are different metrics
A server can produce a large number of total tokens by serving many requests at once while each individual request advances slowly. Conversely, it can deliver very high per-request speed with lower aggregate utilization. NVIDIA positions LPX around the ability to keep individual interactions responsive without giving up scale.
When benchmarking, report both the aggregate output rate and the rate observed by one request at the target concurrency. That makes it clear whether an optimization improves user experience, operator economics or both.
Concurrency can change the meaning of a TPS measurement
If 3,431 TPS was measured for one stream, dividing it evenly among 100 simultaneous requests is only a mathematical scenario, not a prediction of how the system schedules concurrency. Real serving can batch, partition or route requests in ways that change both total and per-request speed.
The calculator therefore labels concurrency sharing as an estimate. For production, run the exact concurrency levels expected at p50, p95 and peak demand and record the full latency distribution.
The 4x claim is tied to a specific competitive comparison
NVIDIA's August announcement says Groq 3 LPX delivered four times faster responsiveness than the nearest alternative platform for the cited workload. That statement should not be generalized to “LPX is 4x faster than GPUs” because the competitor, model and benchmark condition define what was measured.
Responsible comparison pages preserve those boundaries. If a buyer wants to compare LPX with a particular GPU platform, run the same model, context, output quality and service-level target on both systems.
SPEED-Bench answers a different question
NVIDIA's long-context technical blog cites a 4,767 median output TPS result on SPEED-Bench for coding-oriented work. This is useful additional evidence that the platform can perform strongly across agentic scenarios, but it is a separate benchmark with its own prompt distribution and methodology.
Do not average 4,767 and 3,431 or use the larger number as a replacement for the 100K-context result. Keep each benchmark attached to the workload it represents.
Precision and quality settings have to stay aligned
Inference systems can increase speed by changing numerical precision, quantization, speculative decoding or model variants. A benchmark is only useful for business planning when the resulting model quality still meets the application requirement. Faster tokens that cause more agent errors or retries can make completed tasks slower.
Benchmark reports should therefore include accuracy or quality checks alongside performance. For coding, that could include test pass rates or task success; for support agents, it could include groundedness and escalation rate.
Translate tokens per second into a user-visible completion time
A 2,000-token answer at 3,431 output TPS would take well under a second for pure generation if one request received the full measured rate. Real service adds prefill, network and scheduling overhead. The calculator makes those assumptions explicit so a team can see how much of total latency is actually being addressed by faster decode.
This translation is often more persuasive than a raw TPS figure because product teams think in seconds per answer or minutes per agent task. It also reveals when another stage dominates the critical path.
Capacity planning requires sustained, not peak, performance
A single fast benchmark run does not tell you how many production requests a rack can serve continuously. Thermal conditions, software stability, model mix, long-tail context, maintenance and redundancy all reduce the amount of peak capacity that should be sold.
Use sustained measurements over representative periods, then apply utilization and headroom. The separate LPX rack calculator converts that defensible performance number into whole-rack capacity.
Reproduce before you procure
For a high-value infrastructure decision, the best use of published benchmarks is to create a reproducible acceptance test. Specify the model, context distribution, output length, concurrency, quality threshold and service metrics before hardware arrives. Then run the same test on the candidate platform and the current baseline.
If the result cannot be reproduced because the software or model is unavailable, treat the published number as directional rather than contractual. Procurement should tie guarantees to vendor documents or benchmark acceptance criteria that both parties can verify.
Methodology and sources
Cloudzat cites the 3,431 TPS Artificial Analysis result with its model and 100K context, distinguishes NVIDIA's rounded 3,400 figure and separate SPEED-Bench result, and avoids treating any benchmark as a universal LPX capacity specification.
- NVIDIA Groq 3 LPX full-production announcement
- NVIDIA technical blog: LPX long-context interactivity
- NVIDIA Groq 3 LPX product page
- NVIDIA technical blog: inside Groq 3 LPX
- NVIDIA Vera Rubin NVL72
- NVIDIA blog: LPX extends Vera Rubin inference
As an Amazon Associate, Cloudzat may earn from qualifying purchases. Marketplace listings on these pages cover adjacent networking, storage, memory or power hardware only. They are not represented as LP30 chips, LPX trays or qualified Vera Rubin rack components. Verify exact models, warranty, interfaces and current OEM documentation before purchase.
Frequently asked questions
What is the published Groq 3 LPX tokens-per-second result?
Artificial Analysis measured 3,431 output tokens per second on Gemma 4 31B with a 100,000-token context.
Why does NVIDIA also say 3,400 TPS?
The press release rounds the 3,431 result. They refer to the same benchmark, not two separate tests.
Is Groq 3 LPX 4x faster than every GPU?
No. NVIDIA's 4x statement is tied to the nearest alternative platform in the cited latency-sensitive benchmark.
What is SPEED-Bench?
NVIDIA cites a separate coding-oriented SPEED-Bench result with a 4,767 median output TPS figure. It should be treated as a distinct benchmark.
Does output TPS include time to first token?
Not necessarily. TTFT and output-token generation rate are different latency components and should be measured separately.
Can I multiply 3,431 TPS by the number of racks?
You can create a rough mathematical scenario, but production throughput will not necessarily scale linearly. Use sustained measured rack performance with utilization, network and redundancy assumptions.
Does context length affect TPS?
Yes. Context changes attention and cache work and can materially change performance.
Does higher TPS always mean a better agent?
No. Model quality, task success, tool latency and retries determine completed-task performance. Speed is only one dimension.
How should I compare LPX with another platform?
Use the same model, context distribution, precision, output quality, concurrency and software maturity, then compare TTFT, output rate, tail latency and cost.
Which number should I put in the rack-sizing calculator?
Use your own sustained production or acceptance-test TPS per rack. Vendor benchmarks are useful placeholders only until you have workload-specific data.