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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesShort answer: Cerebras and Groq are the leading candidates when your priority is very low decode latency through a hosted API. NVIDIA is the stronger choice when you need to run models yourself, choose the serving stack, support unusual models or kernels, batch heavily, or control data and capacity. There is no single apples-to-apples benchmark covering all three, so the fastest or cheapest option depends on your model, context length, concurrency, precision, and service tier.
The practical difference between the three options
These are not identical products. Cerebras and Groq primarily offer hosted inference endpoints: you send requests to a managed service and pay according to the provider’s usage and service terms. NVIDIA supplies the GPU platform used in cloud instances, private servers, and managed services; you or an infrastructure provider then select the model runtime, hardware configuration, batching policy, and commercial arrangement.
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| Option | What you are buying | Where it is strongest | Main trade-off |
|---|---|---|---|
| Cerebras Inference | Hosted inference for supported open models | Very fast token decoding with simple API deployment | Model availability, configuration, and published speed figures are service-specific |
| GroqCloud | Hosted, token-metered API with selectable service tiers | Low-latency access and tier choices for latency, throughput, and reliability | Exact current per-model prices and capacity vary by model and tier |
| NVIDIA GPUs | Hardware or cloud GPU capacity that you operate directly or through a provider | Model portability, self-hosting, batching, custom kernels, and operational control | You must account for infrastructure, software, utilization, and engineering costs |
For an interactive assistant where users wait for each generated token, compare time to first token (TTFT) and decode tokens per second at the concurrency you actually expect. For a batch job, aggregate throughput, queueing, and cost per completed request matter more than the fastest single stream.
Which is fastest for AI inference?
Cerebras publishes model-specific hosted results above 2,000 tokens per second. Its pricing material lists GPT-OSS 120B at approximately 3,000 tokens per second, while its documentation lists Qwen 3 32B at approximately 2,100 tokens per second. A Cerebras product statement also describes more than 2,000 tokens per second for Scout. Those are useful indicators of the service’s potential, not a universal speed guarantee for every model or prompt.
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Cerebras explicitly warns that observed improvements over GPU systems vary with workload, configuration, date, and model. A short prompt at low concurrency can produce a very different result from a long-context request serving hundreds of simultaneous users.
GroqCloud is also designed for fast hosted decoding. Its documentation describes multiple service tiers so customers can tune latency, throughput, and reliability. The tier you select, the model, current capacity, request size, and concurrency all affect the result. Without a common benchmark and current model-by-model figures, it is not defensible to declare Groq faster than Cerebras in every case.
NVIDIA performance is determined by the complete deployment rather than by a GPU name alone. GPU generation, precision, model implementation, TensorRT-LLM or vLLM configuration, kernel choices, batch size, parallelism, and memory limits can change both TTFT and sustained decode speed. NVIDIA’s benchmark material exposes these fields precisely because headline numbers do not transfer automatically to another workload.
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Latency metrics that matter
- Time to first token: includes request handling, scheduling, prompt processing, and the first generated token. It dominates perceived responsiveness for short answers.
- Decode tokens per second: the generation rate after the first token. This is the metric behind many impressive vendor headlines.
- Tail latency: p95 or p99 TTFT and completion time under load. A fast median is not enough if users encounter long queues.
- End-to-end latency: add network travel, authentication, client processing, and any retrieval or tool calls around the model.
Published speed and cost figures
| Provider and example | Published speed | Input price | Output price | How to interpret it |
|---|---|---|---|---|
| Cerebras, GPT-OSS 120B | Approximately 3,000 tokens/s | $0.35 per million tokens | $0.75 per million tokens | Model-specific hosted figures and rates; not a universal Cerebras result |
| Cerebras, Qwen 3 32B | Approximately 2,100 tokens/s | $0.40 per million tokens | $0.80 per million tokens | Documentation example for this model and service |
| NVIDIA, H100 with vLLM | Not stated as a single universal rate | About $0.09 per million tokens in the cited GPT-OSS-120B setup | SemiAnalysis InferenceX figure cited on NVIDIA’s H100 material; configuration-specific | |
| NVIDIA, B200 | Not stated as a single universal rate | About $0.02 per million tokens in the cited GPT-OSS-120B setup | Same cited benchmark context; not a guaranteed rental or operating price | |
| GroqCloud | Tier- and model-dependent | Exact current model prices not established here | Use the applicable GroqCloud rate card and service tier when calculating your workload | |
The NVIDIA figures are cost-per-token benchmark results for a stated setup, not an all-in monthly bill. They should not be compared with a hosted API list price without matching what is included: GPU rental or depreciation, power, host server, storage, networking, software, support, idle capacity, and engineering time.
Is Cerebras cheaper than NVIDIA GPUs?
There is no universal answer. Cerebras gives you a predictable hosted input/output price for supported models and removes the work of provisioning GPUs, deploying a runtime, monitoring capacity, and absorbing idle periods. That can be the lower total cost for a small, variable, or rapidly changing workload even when the nominal token rate is higher.
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An owned or rented NVIDIA deployment can achieve a lower modeled cost per million tokens when utilization is high and the serving stack is efficient. The cited NVIDIA comparison reports about $0.09 per million tokens for an H100 with vLLM and about $0.02 for a B200 in a GPT-OSS-120B setup. Those values are tied to the benchmark’s model, batching, precision, hardware, and accounting assumptions; they are not promises for another model or a lightly used GPU.
Calculate fully loaded NVIDIA cost
Use a consistent accounting period, such as one month:
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- Add the engineering and operations cost required to deploy, update, secure, and monitor the service.
- Measure input and output tokens actually served, including rejected, retried, or wasted work if those consume capacity.
- Divide the total cost by the tokens served, then report input and output economics separately if your workload has different prices or lengths.
- Run the calculation at several utilization levels. A GPU that is economical at near-continuous load can be expensive when it sits idle between bursts.
For Cerebras, start with the published input and output rates, then add any applicable platform, network, or application costs. Compare the resulting bill with the fully loaded NVIDIA figure, not with GPU rental alone.
How GroqCloud pricing and service tiers change the decision
GroqCloud uses token-metered billing and offers performance, on-demand, flex, and auto service tiers.
- Performance: intended for the lowest latency for enterprise users.
- On-demand: the standard tier for predictable speed.
- Flex: prioritizes throughput on a best-effort basis.
- Auto: selects the best available tier.
These choices let you trade latency, throughput, and reliability without changing your application model. They also mean that a cost comparison must name the tier. A request routed through a low-latency enterprise tier is not economically equivalent to a best-effort batch request.
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Because an exact, current Groq model-price table is not established here, do not infer that Groq is cheaper or more expensive than Cerebras from speed claims alone. Record the price, included limits, rate limits, and tier used for your test.
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When NVIDIA is the better platform
You need to self-host or control data flow
NVIDIA lets you keep inference in your own cloud account, data centre, or private environment, subject to the deployment you choose. This is important when data residency, network isolation, custom authentication, or internal observability rules prevent sending prompts to a third-party hosted endpoint.
You need model and software portability
The NVIDIA ecosystem supports a broad range of model-serving choices. You can select GPU generation, numerical precision, quantization, TensorRT-LLM or vLLM, batching policy, tensor or pipeline parallelism, and custom kernels. That flexibility is valuable when the model is not available from a hosted specialist or when you need to alter the runtime.
You need high sustained utilization
Batching and continuous request scheduling can make a large difference to cost per token. A well-utilized NVIDIA fleet can spread fixed capacity costs over many requests, whereas a hosted API charges directly for usage. The trade-off is that you own capacity planning, scaling, failures, upgrades, and security.
You need reproducible benchmark controls
NVIDIA benchmark tables commonly specify model, precision, hardware, batch or concurrency settings, and serving software. That detail helps you reproduce a test, but it also makes clear why one published result cannot be transplanted to a different prompt mix or model.
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When Cerebras or Groq is the better platform
Latency-sensitive interactive applications
Choose a hosted specialist first when a user is watching the response stream and the main objective is rapid decoding. Cerebras’ published examples above 2,000 tokens per second make it a strong candidate for supported models. GroqCloud’s performance tier is aimed at the same latency-sensitive class of workload.
Small teams and variable demand
Hosted APIs avoid buying or reserving GPUs for peak demand. You can begin with metered usage, monitor real traffic, and move to a self-managed fleet only when volume and utilization justify the operational burden.
Fast experimentation with supported models
If your model is offered by the service and its context window, tool-calling behavior, safety controls, and rate limits fit the application, an API can reduce deployment time. The limitation is that you cannot assume every checkpoint, quantization, adapter, or custom kernel will be available.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why vendor speed numbers are not directly interchangeable
A tokens-per-second value is meaningful only with its test conditions. A fair comparison needs the same model weights, prompt and completion lengths, context window, precision or quantization, concurrency, sampling settings, warm-up policy, and definition of “tokens per second.” Hosted services may also include or exclude queueing and network time differently.
- Model size: a 32B model and a 120B model have different memory and compute demands.
- Prompt length: long prefills can dominate TTFT even when decode is fast.
- Concurrency: one stream measures responsiveness; many streams measure capacity and scheduling.
- Precision: FP16, BF16, FP8, and other quantization choices change speed, memory use, and output behavior.
- Batching: larger batches may improve aggregate throughput while making an individual request wait longer.
- Accounting: input and output tokens can have different hosted prices, while an owned GPU incurs cost during idle and queue time.
For these reasons, the Cerebras and Groq service figures should be treated as model-specific hosted observations, while NVIDIA figures should be treated as configuration-specific deployment results.
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How to run a fair Cerebras, Groq, and NVIDIA test
- Choose one identical model. Use the same checkpoint or the closest officially supported version. Record the model revision and date.
- Fix precision and runtime settings. Document quantization, context limit, maximum output, sampling parameters, parallelism, and serving framework.
- Create a representative prompt set. Include short and long contexts, normal and worst-case outputs, retrieval prompts, tool calls, and any structured-output requirement.
- Test several concurrency levels. At minimum, measure one stream, your expected average load, and a peak level. Warm the services before recording results.
- Capture TTFT and decode separately. Record median, p95, and p99 values, not just one headline average. Include end-to-end time for the user experience.
- Measure throughput and failures. Record aggregate input and output tokens per second, queue time, timeouts, rate-limit responses, retries, and incorrect or truncated outputs.
- Apply the actual price model. For Cerebras and Groq, use input and output token rates and the selected service tier. For NVIDIA, include all fixed and variable costs for the test period.
- Repeat at different context lengths. A provider that wins on a short prompt may lose when the prompt includes a large document or conversation history.
- Check output equivalence. Compare quality, tool-call validity, formatting, and determinism before selecting the fastest result. A cheaper token is not useful if it creates extra retries or human review.
Decision guide by workload
| Your priority | Start with | Why | What to verify |
|---|---|---|---|
| Fastest-feeling streamed answers | Cerebras and GroqCloud | Both focus on hosted low-latency inference | TTFT, decode rate, p95 latency, rate limits, and model availability |
| Private deployment or regulated data | NVIDIA GPU deployment | You control placement, networking, runtime, and retention | Total operating cost, security work, utilization, and incident response |
| Custom model, adapter, or kernel | NVIDIA | Broader software and hardware control | Memory fit, framework support, quantization quality, and maintenance effort |
| Unpredictable early-stage traffic | Cerebras or GroqCloud | Metered capacity avoids paying for idle GPUs | Per-token price, quotas, burst behavior, and data terms |
| High, steady production volume | Benchmark NVIDIA against both APIs | High utilization may lower fully loaded cost | Real concurrency, batching efficiency, amortization, and power or hosting charges |
| Batch generation where latency is flexible | Groq flex or an optimized NVIDIA fleet | Throughput and utilization can matter more than single-request latency | Queueing, completion deadline, failure recovery, and effective cost per token |
Common mistakes that produce a misleading winner
Comparing different models
A fast result from a smaller model cannot establish that its hardware or provider is faster for a larger model. Keep model size and implementation constant.
Using output price as total request cost
Long system prompts, conversation history, and retrieved documents can make input tokens a substantial share of the bill. Calculate input and output separately before combining them.
Ignoring concurrency and batching
Single-user decode speed may collapse under queueing, while a batched GPU may look slow per request but deliver excellent aggregate throughput. Test both user-facing and fleet-level metrics.
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GroqCloud’s performance, on-demand, flex, and auto tiers make different promises. Record the tier alongside every latency and cost result.
Treating a benchmark as a quotation
The cited NVIDIA H100 and B200 cost figures come from a specific GPT-OSS-120B setup. Hardware rental prices, electricity, utilization, software versions, and model behavior can all change the result.
Forgetting network and operational overhead
A hosted API can have network distance and rate limits; a self-hosted service can have deployment delays, failed nodes, upgrades, and on-call work. Include those effects in the decision rather than comparing raw decode rates alone.
Bottom-line recommendation
Use Cerebras or GroqCloud first when your application is built around fast streaming responses and the required model is supported. Compare both with the same prompts and concurrency; neither vendor’s headline speed proves a universal win.
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Use NVIDIA when control is the requirement: self-hosting, model portability, custom serving code, private networking, or high sustained utilization. At production scale, run a full cost-per-million-token model that includes infrastructure and operations. The right choice is the platform that meets your latency and quality target at your real concurrency and utilization—not the one with the largest isolated tokens-per-second claim.
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