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Choose a GPU when you need broad model and framework support, training as well as inference, long-context prefill, flexible batching, or high aggregate throughput. Choose a language-processing unit (LPU) when an interactive service is dominated by token-by-token decoding and must hold strict first-token, inter-token, and tail-latency targets. A hybrid GPU–LPU design can split those phases. The right answer depends on your model, prompt and output lengths, concurrency, quantization, latency objective, and price target—not on a headline tokens-per-second number.
Measure the exact workload on each candidate backend before committing to hardware. The guide below explains what to measure, how to benchmark fairly, when hosted inference is safer than an on-premises purchase, and where the current evidence for LPU performance is useful but not universal.
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What are GPU and LPU inference accelerators?
A GPU is a general-purpose parallel processor with large external memory and a mature software stack. An LPU is a specialized language-inference accelerator designed to execute neural-network operations through a more constrained, compiler-orchestrated path. Both can run language models, but they optimize different bottlenecks.
What a GPU contributes
GPUs expose a wide range of kernels, libraries, precisions, memory sizes, and interconnects. That flexibility supports training, fine-tuning, embeddings, multimodal models, retrieval pipelines, and inference for many model families. It also lets an operator trade batch size, model replicas, quantization, and parallelism as demand changes.
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The trade-off is that a GPU serving stack has many scheduling choices. Dynamic batching, memory movement, kernel selection, contention between requests, and queueing can make an interactive request’s latency vary even when average throughput is high. That variability is a workload effect, not a universal GPU defect; a well-sized GPU deployment can meet tight service-level objectives.
What an LPU contributes
An LPU is built around language-inference execution rather than a broad range of workloads. The design described for Groq 3 LPX uses deterministic, compiler-orchestrated execution and high on-chip SRAM bandwidth. The goal is to reduce timing variability in latency-sensitive decode work, especially feed-forward network (FFN) and mixture-of-experts (MoE) expert execution.
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How do prefill and decode determine the hardware choice?
Inference is not one uniform operation. A request normally has a compute-heavy prefill phase followed by a memory-sensitive, token-by-token decode phase, and the phase that dominates your user experience should drive the accelerator decision.
Prefill builds the context
During prefill, the system processes the system prompt, user input, retrieved passages, and other input tokens, then builds the key-value (KV) cache used by subsequent generation. NVIDIA’s LLM Inference Sizing guidance (2024) describes prefill as generally compute-bound for reasonable prompt lengths. Its duration largely determines time to first token (TTFT).
Decode generates the answer
Decode produces output one token at a time. Each step reads the existing KV cache and computes the next-token distribution. Inter-token latency is usually memory-bound and depends on both input and output length. Streaming users notice this as the gap between visible tokens; a sequential application that waits for completion cares about total time to the last token.
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|---|---|---|---|
| Prefill | Processes the full input and creates the KV cache | Time to first token | Compute, memory capacity for long context, and parallelism |
| Decode | Generates one token per step while reading the KV cache | Inter-token latency, tokens per second per user, and tail latency | Memory bandwidth, scheduling, contention, and deterministic execution |
| Complete response | Runs prefill plus all decode steps | Time to last token | Input length, output length, queueing, and batching policy |
Offline jobs can accept a longer wait in exchange for large batches and high utilization. Online chat, coding, voice, and agent systems expose TTFT and token cadence directly. NVIDIA notes that imposing a latency requirement significantly reduces the throughput available from the same hardware, so write down whether your objective is responsiveness, aggregate throughput, or a defined balance.
Which workloads usually favor a GPU?
Start with a GPU when flexibility, model breadth, context handling, or total work completed matters more than the lowest possible decode latency for one user.
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- Training plus inference: One accelerator family can serve experimentation, fine-tuning, evaluation, and production instead of maintaining separate specialized fleets.
- Many model families: Rapidly changing checkpoints, custom operators, multimodal models, and unusual quantization formats benefit from broad framework support.
- Long-context prefill: Large prompts and retrieval-heavy requests place substantial pressure on compute and memory capacity. A GPU or a hybrid path is usually the safer starting point.
- High-concurrency serving: Large batches can keep GPU compute units busy and amortize scheduling overhead, improving aggregate tokens per second.
- Batch embeddings, moderation, and offline generation: These workloads can be queued and packed for utilization rather than optimized for a single request’s tail latency.
- Uncertain demand: A hosted GPU service lets you test traffic and model mix before purchasing a fixed on-premises configuration.
GPU performance still depends on model size, precision, context distribution, batch policy, and memory pressure. Benchmark the production quantization and concurrency rather than assuming a larger theoretical FLOPS figure will solve a serving bottleneck.
When does an LPU make more sense?
An LPU is most compelling when users are waiting interactively and your service-level objective emphasizes TTFT, steady inter-token cadence, and p95 or p99 tail latency.
- Interactive chat and coding: A fast first token and smooth streaming response improve perceived responsiveness.
- Voice and real-time agents: Jitter can be more damaging than a modest increase in average completion time because pauses are immediately noticeable.
- Decode-heavy traffic: Short prompts followed by long outputs spend a large share of time in token-by-token generation.
- Sparse MoE decode: A hybrid or LPU path can accelerate latency-sensitive expert execution while another device handles attention.
- Strict tail SLOs: Deterministic scheduling and large on-chip SRAM bandwidth may reduce variability when a conventional GPU deployment is meeting its average target but missing p99.
An LPU is not automatically faster for every model. Long prompts can make prefill the dominant cost, and unsupported operators or a model conversion step can erase the advantage. Confirm compilation, tokenizer behavior, context-window limits, streaming APIs, and fallback behavior before treating an LPU as a production option.
What does the current LPU performance evidence actually show?
Published figures demonstrate what a particular platform can target or achieved in a particular test; they do not establish a universal GPU-versus-LPU ranking.
Announced LPX rack specifications
NVIDIA announced a Groq 3 LPX rack specification for 2026 of 315 PFLOPS FP8 inference compute, 128 GB of total SRAM, 40 PB/s of on-chip SRAM bandwidth, 256 chips, and 640 TB/s of scale-up bandwidth. Those are vendor specifications for the announced platform, not an independent benchmark and not a characteristic shared by every LPU.
Vendor-listed per-user rate
Groq’s current platform page lists 1,000 tokens per second per user. Treat that as a vendor-listed target whose result depends on model, prompt and output lengths, decoding settings, concurrency, and service availability.
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Independent test reported by the provider
Groq reported an ArtificialAnalysis.ai test of its Llama 2 Chat 70B API at 241 tokens per second and 0.8 seconds to receive 100 output tokens. 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 hosted service can be very fast under that configuration, but it is not an apples-to-apples comparison with every GPU deployment. The model revision, prompt, output, network path, queueing, and measurement window all matter.
Should you build a hybrid GPU–LPU inference path?
A heterogeneous design can be the best compromise when GPUs are efficient for prefill and attention but an LPU is better at latency-sensitive FFN or MoE decode work.
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NVIDIA describes a two-engine decode loop in which GPUs process full-context attention and other throughput-sensitive work while LPX accelerates FFN and MoE execution. In principle, this keeps GPU flexibility and AI-factory throughput while improving interactive responsiveness. It is most relevant when one accelerator type alone forces a choice between high aggregate utilization and predictable per-user latency.
What the hybrid path must coordinate
- KV-cache ownership: Decide where cache data lives and how it moves between engines. Transfers can become the new bottleneck.
- Token-step synchronization: Every decode step must exchange the intermediate data in the correct order without adding queueing jitter.
- Interconnect capacity: The advertised scale-up bandwidth must be available in the deployed topology, not only in a rack specification.
- Compiler and runtime compatibility: Both engines need matching tensor layouts, precision, operators, and error handling.
- Failure and fallback behavior: Define what happens when one pool is full, an LPU compilation path is unavailable, or a request uses an unsupported operation.
Hybrid infrastructure adds orchestration, observability, deployment, and procurement complexity. Prove an end-to-end improvement with production-shaped traffic before accepting that complexity.
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Write a workload card before requesting hardware or API quotes. NVIDIA’s sizing checklist calls for the model, average input tokens including the system prompt and retrieved context, average output tokens, peak requests per second, and the first-token or last-token latency limit. Add the operational measurements below.
| Measurement | Why it changes the decision | How to record it |
|---|---|---|
| Model revision and tokenizer | Different revisions can use different operators, memory footprints, and token counts | Pin the exact checkpoint, tokenizer files, and serving build |
| Input and output distribution | Prefill and decode scale differently with sequence length | Record median, p95, and maximum tokens, not only averages |
| Concurrency and peak requests per second | Determines batching, queueing, and capacity headroom | Test steady state and bursts at the expected peak |
| TTFT and time to last token | Separates interactive responsiveness from complete-answer time | Measure client-visible timestamps for every request |
| Inter-token latency and tokens per second per user | Shows whether streaming feels smooth under contention | Report p50, p95, and p99 token gaps |
| Quantization and decoding settings | Precision, temperature, top-p, speculative decoding, and stop rules alter work | Keep settings identical across backends |
| Context-window distribution | Long contexts increase KV-cache capacity and prefill cost | Replay the real mix of short and long requests |
| Utilization, power, and price per million tokens | Average speed without operating cost can be misleading | Measure energy and provider charges at the same service level |
Capture p50, p95, and p99 rather than only an average. A system that meets an average TTFT but violates p99 during a burst may fail the user-facing SLO. Record rejected requests, queue time, timeouts, out-of-memory events, thermal throttling, and compiler failures as well as successful tokens.
How do you benchmark GPU and LPU inference fairly?
Use the same model revision and decoding behavior, then vary only the backend and its documented serving configuration.
- Define the service objective. Specify whether the primary target is TTFT, inter-token latency, time to last token, aggregate tokens per second, or a weighted combination. Set p95 and p99 limits and a maximum queueing time.
- Build a representative trace. Include system prompts, retrieved context, short and long conversations, expected output lengths, stop conditions, and the real request-rate pattern. A synthetic fixed 100-token prompt can hide a long-context bottleneck.
- Pin software and model inputs. Use the same checkpoint, tokenizer, quantization, sampling parameters, maximum context, and output cap. Record runtime, compiler, driver, and API versions.
- Warm each system. Separate cold-start and compilation time from steady-state serving. Measure both if autoscaling or scale-to-zero is part of the design.
- Run a concurrency sweep. Test one user, then realistic steady-state concurrency, then the peak and burst levels. Keep the arrival pattern identical for each backend.
- Measure client-visible latency. Timestamp request submission, first byte, first token, every streamed token, final token, and any retries. For a hosted API, include network and queueing time because that is what the user experiences.
- Repeat long enough for stable tails. Collect enough requests to make p95 and p99 meaningful. Repeat at different times for a hosted service if capacity can vary.
- Report utilization and failures. Include accelerator utilization, memory use, power, throttling, queue depth, errors, and cost. A faster result that needs a second replica or has frequent failures is not a lower-cost solution.
- Test recovery. Kill a worker, exhaust memory, submit an unsupported sequence length, and send a burst. Verify timeout, retry, fallback, and user-visible error behavior.
Compare results by the same quality and latency envelope. If one backend uses a different quantization or decoding shortcut, label that as a different operating point rather than a pure hardware comparison.
Is on-premises or hosted inference the better starting point?
Hosted inference is usually the lower-risk way to validate an uncertain workload; on-premises hardware becomes attractive when demand, model support, data controls, and utilization justify the fixed investment.
| Factor | On-premises GPU or LPU | Hosted API |
|---|---|---|
| Capital cost | Requires servers, networking, power, cooling, and spares | Primarily usage or committed-capacity charges |
| Control | Maximum control over data path, versions, scheduling, and hardware placement | Provider controls hardware and may limit versions or regions |
| Scaling | Capacity is bounded by installed machines until expansion arrives | Can absorb experiments and bursts, subject to quotas and provider capacity |
| Model coverage | GPU stacks generally offer the broadest local flexibility; LPU coverage depends on compiler support | Depends on the provider’s published model catalog and API features |
| Latency | Removes internet and provider queueing, but local scheduling still matters | Must include network, TLS, provider queueing, and geographic distance in measurements |
| Operations | You own drivers, firmware, observability, upgrades, failures, and security | Provider operates the accelerator fleet; you manage API integration, quotas, and data policy |
For an LPU in particular, a hosted API can avoid buying a rack before you know whether your model compiles, whether the service is available in your region, and whether the latency advantage survives your prompts and concurrency. An enterprise deployment may still choose an on-premises or dedicated arrangement for data residency, predictable capacity, or sustained utilization.
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What hardware and software details matter beyond the accelerator label?
The name GPU or LPU is only the first filter. Evaluate the complete serving system.
Memory and interconnect
Model weights, KV cache, activations, and batching consume memory. Long-context traffic can exhaust capacity even when arithmetic throughput is ample. Check usable memory after runtime reservations, the bandwidth available to the actual topology, and whether tensor or pipeline parallelism adds communication on every token step.
Precision and quantization
FP8, FP16, BF16, INT8, and lower-bit formats change memory use, arithmetic rate, and sometimes output quality. The same nominal model may have materially different latency after quantization. Benchmark the format you will deploy and validate quality on representative prompts.
Compiler and operator coverage
A specialized accelerator may require an ahead-of-time compilation step and a supported operator graph. Check custom layers, rotary-embedding variants, MoE routing, speculative decoding, log-probability output, constrained decoding, tool calling, and multimodal operators before selecting it.
Serving features
Verify continuous batching, prefix caching, paged KV cache, streaming, cancellation, priorities, rate limits, metrics, and graceful degradation. A backend that is fast in a single-user test but lacks the feature needed by your application can increase total engineering time.
What are the common selection mistakes and how do you fix them?
Choosing from peak FLOPS alone
Peak compute does not reveal TTFT, token cadence, memory pressure, or tail behavior. Replace the comparison with a trace-based test that reports latency percentiles and cost at the required quality.
Using only average latency
Interactive users experience the slowest requests during contention. Set p95 and p99 objectives, inspect queue time separately, and test bursts instead of relying on a quiet, single-user run.
Benchmarking a toy prompt
A short prompt and short answer can hide prefill and KV-cache costs. Replay your system prompt, retrieval context, conversation history, and expected output distribution.
Ignoring model and runtime compatibility
An LPU may require conversion or may not support an operator used by your preferred checkpoint. Compile the exact revision early and keep a GPU fallback while validating quality and feature parity.
Comparing different decoding settings
Temperature, top-p, output caps, stop sequences, speculative decoding, and sampling implementation all affect work and output length. Lock them across backends or label the results as separate configurations.
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Power, cooling, networking, software maintenance, spare capacity, engineering time, and failed requests belong in the total-cost model. A neutral, current GPU-versus-LPU cost benchmark that applies to every deployment is not established; pricing depends on hardware, power, utilization, software work, cloud rates, and the latency SLO.
Assuming a hosted benchmark equals your client latency
Provider benchmarks may measure server-side generation while your users also pay for network distance, TLS setup, queueing, serialization, and retries. Measure from the same client regions and include those costs in the SLO.
Which accelerator should you choose for common workload patterns?
Use the following as a starting point, then confirm the choice with the benchmark procedure.
| Workload condition | Better starting point | Reason |
|---|---|---|
| Training plus inference or many model families | GPU | Broad software, framework, and model support |
| Long-context prefill and large KV-cache work | GPU or hybrid | Memory capacity 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 an LPU handles latency-sensitive FFN or MoE work |
| Uncertain demand or fast-changing models | Hosted GPU or LPU API | Measure traffic and model mix before committing to infrastructure |
What is the practical decision process?
Make the decision in this order so that a favorable benchmark cannot conceal a requirement mismatch.
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- Classify the application. Mark it as interactive streaming, sequential completion, or offline batch, and identify whether prefill or decode dominates.
- Write hard limits. Set model quality, maximum context, TTFT or time-to-last-token, p95 and p99 latency, peak requests per second, availability, region, and data requirements.
- Eliminate incompatible backends. Remove any option lacking the required operators, model revision, context length, precision, tool-calling behavior, or compliance controls.
- Benchmark GPU, LPU, and hybrid candidates. Use identical traces, settings, and reporting. Include hosted API tests if network service is an option.
- Calculate cost at the target SLO. Include replicas, idle headroom, power, networking, engineering, provider charges, and failure retries—not just accelerator purchase price.
- Choose the least complex option that meets the objective. Adopt a hybrid path only when measured end-to-end gains justify its orchestration and operational burden.
In practical terms, a GPU is the default for flexibility and broad throughput; an LPU earns its place when interactive decode latency is the limiting business requirement; and a hybrid deployment is a specialized answer for systems that genuinely need both behaviors.
Frequently Asked Questions
Can I move an existing GPU model to an LPU without changing the application?
Not always. The model may need compiler conversion, supported operators, and a compatible quantization, while features such as log probabilities or constrained decoding can differ. Keep tokenization, output validation, and a GPU fallback under test before switching production traffic.
Does dynamic batching always make GPU inference faster?
No. It commonly raises aggregate utilization, but the batching wait can increase TTFT and tail latency for short requests. Tune the maximum queue delay and batch size against the actual latency objective rather than maximizing batch size by itself.
How much does network distance matter for a hosted inference API?
It can materially affect first-byte and first-token measurements, especially for short responses where generation is quick. Measure from each important user region and include connection setup, serialization, provider queueing, and retries in the client-side SLO.
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What should I do if demand is too bursty to size confidently?
Use a hosted endpoint or an autoscaled pool while collecting a representative request trace. Define admission control, warm capacity, a queue limit, and a slower fallback so a burst does not turn into unbounded latency.
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