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Choose a GPU when your workload needs broad model support, training as well as inference, long-context prefill, or high aggregate throughput. Start with an LPU when interactive, decode-heavy service needs consistently low first-token, per-token, or tail latency. For demanding systems, a hybrid can use GPUs for prefill and attention and an LPU for latency-sensitive feed-forward network (FFN) or mixture-of-experts (MoE) work. Measure your own model and traffic before committing: neither accelerator is universally faster or cheaper.
What is the practical difference between GPU and LPU inference?
A GPU is a flexible parallel processor used across many compute workloads, including model training and inference. An LPU is a specialized language-inference accelerator designed to execute inference workloads with a focus on predictable, low-latency token generation. The practical choice is not about which chip wins a generic speed contest; it is about which part of your inference workload is limiting the user experience or operating capacity.
Inference itself has distinct phases. NVIDIA defines prefill as processing the input tokens and building the key-value (KV) cache, and decode as generating the answer one token at a time. Prefill determines time to first token and is compute-bound for most reasonable prompt lengths; decode determines the cadence after generation begins and is usually memory-bound. The balance changes with prompt length, output length, concurrency, and the serving setup. [c003]
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This distinction explains why a system can have excellent benchmark throughput yet feel sluggish in a chat window. A long system prompt, retrieved documents, or conversation history increases prefill work before the first token. Once generation starts, every additional token requires another decode step, and the user experiences the interval between those steps. A voice assistant, coding copilot, or agent that emits tool calls may be judged primarily by those intervals, while an offline summarization pipeline may care almost entirely about total completion time and cost per batch.
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- For streaming chat, coding, voice, and agents: users notice how long the first token takes and how quickly tokens continue to arrive. Decode latency and jitter can matter as much as peak throughput.
- For applications that wait for a complete answer: time to last token, or total response time, is the more relevant user-facing measure.
- For offline work: jobs such as embeddings, moderation, or bulk generation can often trade responsiveness for high utilization and larger batches.
A latency target has an economic consequence: NVIDIA notes that requiring lower latency can significantly reduce the throughput available from a system. Set the target to match the product experience you actually need rather than optimizing a latency number in isolation. [c003] For example, a service that promises a tight tail-latency ceiling may need spare capacity or smaller batches, whereas an offline queue can keep devices busy with larger batches and tolerate waiting.
When should I choose a GPU?
Choose a GPU as the starting point when you need flexibility, broad model and framework support, training alongside inference, long-context prefill, or efficient high-volume serving. GPUs are also a practical default when workloads are varied or changing and the system must support more than one model family.
GPU workloads that tend to fit well
- Training and inference on the same platform: GPUs remain a flexible workhorse for both activities in NVIDIA’s described heterogeneous design. [c001]
- Many models or frameworks: broad software and model support can reduce the risk that a specialized serving target will limit future choices.
- Long-context prompts and large KV-cache workloads: the memory capacity and flexible parallel compute of a GPU can be valuable for prefill and attention-heavy work.
- Batch-heavy or offline work: larger batches and high utilization can make aggregate throughput the priority instead of per-request responsiveness.
- Decode attention and high-concurrency serving: NVIDIA identifies GPUs as suitable for decode attention and high-concurrency serving in its proposed heterogeneous architecture. [c001]
GPUs are particularly useful when the model roster changes frequently. A team can retain one serving stack while testing different model revisions, quantization choices, or task-specific models, instead of waiting for a specialized compiler or runtime to support each change. That flexibility has operational value even when a specialized accelerator posts a higher token rate on one fixed model.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Large prompts also favor a platform that can handle substantial prefill and KV-cache work. Input tokens include the system prompt and retrieved context, not just the user’s final question. If those inputs vary widely, a GPU’s general parallel compute and memory options can make it easier to accommodate the long tail of context lengths. The right configuration still depends on the model and memory footprint; “GPU” is not a guarantee that every context will fit or that every request will meet a target.
A GPU is not automatically the best option for every interactive request. Small-batch decode can expose memory movement, contention, or scheduling effects as visible jitter. That is a workload effect rather than a universal GPU limitation: the model, quantization, context, concurrency, and serving configuration all influence the result. Benchmark with the conditions your users will actually see, including bursts and the p95 or p99 latency that your service-level objective (SLO) specifies.
When is an LPU a better starting point?
Start with an LPU when an online service is decode-heavy and the main requirement is a responsive, predictable experience for each user. This is especially relevant when first-token time, inter-token cadence, or tail latency must meet a strict SLO. An LPU’s value is therefore tied to the serving contract: a lower and more predictable per-user delay can matter more than a higher aggregate rate that leaves individual streams uneven.
NVIDIA describes Groq 3 LPX as using deterministic, compiler-orchestrated execution and high on-chip SRAM bandwidth to reduce timing variability. In NVIDIA’s proposed arrangement, LPX targets latency-sensitive FFN and MoE expert execution, while GPUs handle prefill and attention. That description points to a specialized role within an inference system, not a claim that an LPU replaces GPUs for every stage or every kind of model workload. [c001]
There are platform figures, but they need to be read as vendor specifications rather than independent comparisons. NVIDIA announced Groq 3 LPX rack specifications of 315 PFLOPS FP8 inference compute, 128 GB total SRAM, 40 PB/s on-chip SRAM bandwidth, 256 chips, and 640 TB/s scale-up bandwidth in 2026. Groq’s platform page lists 1,000 tokens/sec/user and repeats the rack figures. These are vendor-listed specifications or targets; how they apply depends on the model, serving configuration, and availability. [c002] [c005]
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Those specifications describe the platform’s intended resources, not the response a particular API call will receive. A model may have unsupported operators, a different memory requirement, or a context and output distribution unlike the demonstration workload. Confirm model availability, supported quantization and decoding features, regional service availability, and concurrency limits with the provider or deployment team. If the LPU is accessed through a hosted endpoint, network and queueing time are part of the user’s first-token experience even when device execution is highly predictable.
These numbers alone cannot predict the latency or throughput your workload will achieve. A platform specification is not a substitute for measuring a particular model revision, prompt distribution, output length, decoding configuration, and concurrency target. Treat “tokens per second per user” as a workload-dependent result, not a promise that every model will stream at that rate.
Which accelerator is the better starting point for my workload?
Use the workload’s dominant constraint to choose a first system to evaluate. The matrix is a starting point for testing, not a guarantee that one device will win every deployment.
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| Workload condition | Better starting point | Reason |
|---|---|---|
| Training plus inference, or many model families | GPU | Broad software and model support |
| Long-context prefill and large KV-cache work | GPU or hybrid | Large memory and flexible parallel compute matter |
| Interactive chat, coding, voice, or agents | LPU or hybrid | Decode latency, jitter, and tail SLOs dominate |
| Batch embeddings, moderation, or offline generation | GPU | Utilization and aggregate throughput dominate |
| Sparse MoE decode with strict responsiveness | Hybrid | GPU can handle attention while LPU handles latency-sensitive FFN/MoE |
| Uncertain demand or fast-changing models | Hosted GPU/LPU API | Avoid committing before measuring traffic and model mix |
If a workload spans rows, do not force it into one label. For example, long prompts can make prefill important even in an interactive chat product, while its generated answer may still be limited by decode cadence. The system should be measured against the full request path and the product’s latency target. A single application may also have separate paths: an interactive assistant, a nightly batch, and an embedding service can rationally use different backends.
How do I benchmark GPU and LPU inference fairly?
Benchmark the same workload on each candidate backend and make the service target explicit. A comparison is useful only if it represents your actual model, request mix, decoding settings, and expected load—not a provider’s best-case speed claim or a single synthetic prompt.
Record the workload before testing
NVIDIA’s sizing checklist calls for the model, average input tokens including system prompt and retrieved context, average output tokens, peak requests per second, and the first-token or last-token latency limit. [c003] Add these details to make the result operationally meaningful:
- Concurrency and batch policy, including whether requests are dynamically batched or run one at a time.
- Context-window distribution, not only an average, where requests vary substantially. Record short, typical, and long prompts.
- Quantization and decoding settings, including the same sampling or deterministic settings on both systems.
- Whether responses stream to the client or are returned only when complete.
- p50, p95, and p99 latency, tokens per second per user, and total response time.
- Power, utilization, and price per million tokens for the system or service being evaluated.
- Peak requests per second and the concurrency reached at that rate, rather than one number in isolation.
Run a controlled, end-to-end comparison
- Fix the input conditions. Use the same model revision, prompt samples, input and output lengths, quantization, and decoding settings on every backend. A different system prompt or maximum-output setting can change both prefill and decode work.
- Test at expected concurrency. Include the real batch policy and peak request rate; low-concurrency and high-concurrency tests can produce different conclusions.
- Measure both steady state and bursts. A system that meets its SLO under constant traffic may behave differently when requests arrive together. Record queueing and service time separately when possible.
- Capture the complete latency profile. Record first-token latency, inter-token cadence, time to last token, and p50/p95/p99 rather than relying on one average. For streaming, inspect pauses between tokens, not only the final average rate.
- Track capacity and operating measures. Log per-user token rate, total throughput, utilization, power, and cost per million tokens under the same conditions.
- Repeat for the workload segments that matter. Separate long-context requests, typical requests, and unusually long outputs if they represent materially different user experiences.
- Verify correctness and feature coverage. Confirm that tool calls, streaming, structured output, batching, and the exact model behavior are equivalent before comparing speed.
Interpret the results against a stated SLO. If interactive users require a specific first-token and tail-latency ceiling, a higher aggregate tokens-per-second result is not enough if that ceiling is missed. Conversely, if the job is offline and can be batched, paying for a latency advantage that does not affect completion requirements may not be justified.
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Do not compare a hosted LPU result that excludes network and queueing with a self-hosted GPU result that includes them, or vice versa. Do not use a tiny prompt to represent a retrieval-heavy application, and do not infer burst behavior from a single-user test. Averages can hide a small percentage of requests that violate the SLO, so percentile reporting is essential. Finally, repeat tests after changing quantization, context distribution, or concurrency; those changes can move the bottleneck from prefill to decode or the reverse.
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Can a GPU and LPU work together?
Yes. A heterogeneous design can assign different inference stages to the processor that best fits them. NVIDIA describes a two-engine decode loop in which GPUs process full-context attention and other throughput-sensitive work, while LPX accelerates latency-sensitive FFN and MoE execution. [c001]
This approach is most relevant when one accelerator type forces a difficult compromise between aggregate throughput and predictable per-user responsiveness. It may let a system retain GPU capacity for broad, throughput-sensitive work while using LPU execution for the decode portions where latency variability matters most. The split follows the phase distinction: GPUs can build or use the KV cache and perform attention, while the specialized engine handles the portions identified as latency-sensitive.
The trade-off is added orchestration, interconnect, and operational complexity. Data must move between engines at the right point in the decode loop, and the handoff itself can erase an apparent device-level advantage if it is not engineered carefully. Capacity planning must cover both sides; an under-sized GPU or LPU becomes the new bottleneck. A hybrid should earn its place through an end-to-end benchmark: measure the full request path, including any handoff between devices, rather than adding together isolated component speeds. If the measured responsiveness gain does not justify that complexity for the actual traffic mix, a single accelerator type may be the better operational choice.
Should I use hosted inference or buy hardware?
For uncertain demand, a fast-changing model mix, or an unproven latency requirement, a hosted GPU or LPU API is a useful way to measure real traffic before committing to owned infrastructure. A hosted API can also make a specialized LPU accessible without buying a rack. Groq’s platform page lists its hosted inference service and vendor performance targets. [c005]
Hosted service does not remove the need for measurement. Check rate limits, concurrency behavior, model versions, geographic availability, streaming semantics, and how usage is billed. Include network round-trip and provider queueing in the first-token and total-response measurements. A hosted endpoint may be the right long-term choice when utilization is uncertain, while a sufficiently large and stable workload may justify comparing owned or reserved capacity.
For a fixed, substantial workload, compare the full operating case rather than hardware purchase price alone. Relevant factors include power, utilization, software engineering and orchestration effort, model support, cloud pricing, and the cost of meeting the latency SLO. No neutral, current total-cost benchmark establishes a universal GPU-versus-LPU winner across these variables. A device that is cheaper per hour can still cost more per successful response if it needs additional capacity or engineering to satisfy the same tail-latency target.
Likewise, an external benchmark can be informative without being decisive. Groq reported that ArtificialAnalysis.ai tested Llama 2 Chat 70B at 241 tokens per second and 0.8 seconds to receive 100 output tokens in 2024. Groq said the test used roughly 100-token prompts and roughly 200 output tokens, and was updated every three hours. It is evidence that an LPU-backed service can be fast under that test; it is not an apples-to-apples universal GPU comparison, nor a forecast for a different model or configuration. [c004]
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What requirements and pitfalls should I check before selecting a platform?
Confirm the practical requirements, not only the advertised compute figure. First, verify that the exact model revision and required operators are supported. A platform that handles one model family well may not support another model, quantization mode, or serving feature with the same maturity. If your roadmap includes several models, account for the cost of validating each one.
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Next, map memory and context needs. Record the distribution of input tokens, including system prompts and retrieved context, and the expected output distribution. Long-context requests increase prefill and KV-cache pressure; short prompts with long generated answers emphasize decode. Quantization can change memory use and performance, so test the quantization you intend to operate rather than assuming results transfer from another precision.
Define the user-facing contract in measurable terms: time to first token, inter-token cadence, time to last token, and p50/p95/p99 limits. State whether the requirement applies at average load, peak requests per second, or burst conditions. Remember that imposing a strict latency requirement can reduce available throughput, as NVIDIA notes. [c003] If the product has no such requirement, avoid paying for one by default.
Plan for availability and operations. Vendor specifications for LPX and the 1,000-token-per-second-per-user figure are not guarantees for every model or region. [c002] [c005] A hosted API also introduces dependency on service availability and network path. Owned infrastructure introduces hardware procurement, power, cooling, software integration, and utilization risk. Include those operational considerations in the comparison even when the initial benchmark is device-local.
What should I decide before selecting a platform?
Write down the constraint that matters most, then test the candidate system against it. The decisive question is whether the deployment is constrained by flexibility, long-context prefill, high aggregate throughput, per-user decode latency, or some combination.
- Prefer GPU evaluation first if training, diverse models, long-context prefill, or batch utilization lead the requirements.
- Prioritize LPU evaluation if interactive decode responsiveness and predictable latency are the defining product requirements.
- Test a hybrid if attention and throughput fit GPUs but latency-sensitive FFN/MoE decode is the bottleneck, and the added system complexity can be justified.
- Use a hosted API to learn when demand, model mix, or the value of lower latency is not yet established.
Do not select from chip specifications or a single published token-rate number. The workload profile, SLO, and measured results together determine whether the difference matters to your users and operations. Revisit the decision when the model, context distribution, concurrency, or product latency promise changes.
As NVIDIA’s Kyle Aubrey and Farshad Ghodsian put it, “Inference isn’t a single, uniform workload.” [c001] Treat GPU and LPU selection as an architecture and measurement decision, not a permanent ranking of processor types.
Frequently Asked Questions
What does tokens per second per user tell me that total throughput does not?
Total throughput aggregates work across requests, while per-user token rate describes the generation pace an individual request experiences. A service can post strong aggregate throughput while a user-facing stream still feels slow, so interpret both alongside latency percentiles and concurrency.
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Only if the model, prompt and output lengths, decoding settings, and serving conditions closely match your deployment. The reported Llama 2 Chat 70B result is tied to its stated test conditions and should not be treated as a general comparison across backends.
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How should I compare the cost of GPU and LPU inference?
Compare cost under the same workload and latency target, including utilization, power, software and orchestration effort, and service pricing. A lower hardware or token price by itself does not establish lower total cost if the system needs different capacity or engineering work to meet its SLO.
Which latency metrics matter for a streaming application?
Measure time to first token, inter-token cadence, and p50, p95, and p99 latency. Also record time to last token because a stream can start quickly yet finish slowly. Test at the concurrency and burst rates your service expects.
Why can a long prompt change the GPU-versus-LPU decision?
Long prompts increase prefill and KV-cache work, which NVIDIA characterizes as compute-bound for most reasonable prompt lengths. A GPU or hybrid may therefore fit better even when the generated answer is decode-heavy. Include system and retrieved-context tokens in the measurement.
When does a hybrid GPU-LPU design make sense?
Consider it when GPUs are effective for full-context attention and throughput-sensitive work, while FFN or MoE decode has a strict responsiveness target. NVIDIA describes this two-engine pattern for LPX. Benchmark the complete handoff and operations; isolated component rates are not enough.
Are NVIDIA’s LPX and Groq token-rate figures guarantees?
No. The 315 PFLOPS FP8, 128 GB SRAM, 40 PB/s SRAM bandwidth, 256-chip, and 640 TB/s figures are announced platform specifications, and 1,000 tokens/sec/user is a vendor-listed platform figure. Actual results depend on model, configuration, traffic, and availability.
Should a startup begin with hosted inference?
Hosted GPU or LPU APIs can be a sensible starting point when demand, model mix, or the value of lower latency is uncertain. Use production-like prompts and concurrency, include network and queueing time, and compare the resulting cost and SLO performance before buying hardware.
What is the main mistake when comparing a GPU and an LPU?
Choosing from a single peak token number. Different prompt lengths, output lengths, quantization, batching, model revisions, and latency targets can reverse the result. Compare identical conditions and report both throughput and latency percentiles.
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Bottom line: Start with a GPU for flexibility, training, long-context prefill, and high aggregate throughput; start with an LPU for predictable, low-latency decode. Consider a GPU-LPU split when both throughput and interactive tail latency matter, and validate every choice with an end-to-end benchmark using your model, traffic, and SLO.
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