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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Neither Cerebras nor Groq is a proven universal winner for AI inference. Cerebras emphasizes wafer-scale processors with on-chip memory, while Groq offers an LPU-based inference cloud with selectable service tiers. Their published speed figures use different models and contexts, so they are not a current head-to-head comparison. Choose by testing the same active model and workload, then comparing latency, quality, features, cost, capacity, and contractual guarantees.
How Cerebras and Groq differ
Both companies offer hosted inference APIs, but the available vendor documentation describes their hardware and services at different levels of detail. Cerebras discusses its processor architecture; Groq’s reviewed materials focus more on its model catalog, API, rate limits, and service tiers. The available information does not support a precise chip-to-chip architectural comparison.
| Provider | Published approach | What the figures do—and do not—show |
|---|---|---|
| Cerebras | Cerebras describes its WSE-3, used in the CS-3 system, as a wafer-scale processor with 900,000 AI-optimized cores, 44 GB of on-chip SRAM, and 21 petabytes per second of memory bandwidth. The company says keeping memory on the wafer can reduce data movement and interconnect bottlenecks during autoregressive decoding. These are vendor-published specifications, not independent benchmark results. Cerebras architecture and AWS integration | In an August 2026 discussion of CS-4 and its Nexus rack-scale platform, Cerebras described CS-4 as the platform’s first system and reported 53.5 petabytes per second of aggregate on-wafer fabric bandwidth for WSE-3T. That is a separate fabric-bandwidth figure; it is not interchangeable with the WSE-3 memory-bandwidth figure. Cerebras Hot Chips 2026 |
| Groq | Groq offers an LPU-based inference cloud. Its reviewed documentation describes supported models and service options, rather than hardware specifications at the same level of detail as Cerebras’s materials. Groq supported model catalog · Groq service tiers | The available material supports comparing the services and their published claims, but not drawing a precise architectural or chip-level performance conclusion against Cerebras. |
What the published speed figures mean
Tokens per second can be useful for orientation, but the figures below are not a controlled, like-for-like test. They refer to different models and publication contexts, and provider-published rates should not be treated as independently verified results.
| Provider and source | Published figure | Important qualification |
|---|---|---|
| Cerebras, August 2024 launch announcement | 1,800 tokens per second on Llama 3.1 8B; 450 tokens per second on Llama 3.1 70B | Historical provider-reported figures from the launch announcement. Its comparison with Groq describes that point in time, not current standings. Cerebras launch announcement |
| Groq model documentation, accessed in 2026 | 560 tokens per second listed for Llama 3.1 8B Instant; 280 tokens per second listed for Llama 3.3 70B Versatile | These are rates listed by Groq, not neutral test results. The 70B model is Llama 3.3, not the Llama 3.1 70B in Cerebras’s announcement. Groq’s deprecation documentation says its Llama 3 8B and 70B models were shut down for free and developer tiers in August 2026, with replacements recommended. Check the live catalog and your account tier before relying on either listing. Groq model catalog · Groq deprecation schedule |
These figures do not establish which service is faster today. Even a same-model comparison can mislead if prompt length, output length, concurrency, streaming, or service tier differs. A hosted API’s user-visible latency also includes network time: Groq’s latency guide distinguishes that from server-side latency. Groq latency guide
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How to compare inference speed fairly
Test the exact models and features you expect to deploy, using a workload that resembles production. Record results by request rather than relying on one average.
- Confirm the model. Select the same currently supported model ID and revision on both services where possible. Match precision and generation settings; if the models differ, treat the result as a comparison of those complete offerings, not just their hardware.
- Fix the request shape. Use identical prompts and context lengths, the same requested output length, and the same streaming mode. Include the tools, structured output, or other API features the application actually needs.
- Match operating conditions. Run at realistic concurrency and repeat requests at representative times. Keep the service tier and relevant account settings consistent, and note any capacity errors or retries.
- Measure the user experience and the service. Record time to first token, inter-token latency, total response time, throughput under load, and error rate. Include client network latency for user-facing measurements, and distinguish it from server-side latency where possible.
- Compare quality and cost with performance. Check task-specific answer quality, then calculate the cost for the same input/output mix. A faster result may not be the better choice if it requires a different model, misses a necessary feature, or costs more at the expected traffic level.
Availability, service tiers, and guarantees
Access and reliability depend on the provider’s current offering and the customer’s account tier. The published details below are not a guarantee that any particular model or region is continuously available.
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Cerebras access
On October 13, 2025, Cerebras announced self-serve pay-per-token access and said developers could start with a $10 deposit. The announcement also described Cerebras Code Pro and Max subscriptions, plus production subscriptions and enterprise tiers with higher capacity, priority routing, and dedicated support. Treat the deposit as an announcement detail, not a current price quote; check active models, onboarding, and prices in the live service. Cerebras pay-per-token announcement
Groq service tiers
Groq documents on-demand as its default tier. Flex is a higher-throughput, best-effort option that can return capacity errors; Auto is a routing option; Performance is an enterprise tier. Groq’s Performance documentation states a 99.9% availability SLA and a 99% low-latency guarantee, with specifics in the customer’s offline agreement. The Performance service is sold through provisioned-throughput bundles rather than ordinary per-token pricing. Those guarantees should not be applied to free, developer, or on-demand accounts. Groq service tiers · Groq Performance tier
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Which provider is cheaper?
The available figures do not establish a current price winner or support a workload cost estimate. Compare each provider’s live pricing for the exact model, including input and output tokens separately where applicable. For provisioned capacity, include minimum commitments and the cost of idle capacity; for request-based traffic, account for retries and the actual input/output mix. Cerebras’s announced $10 starting deposit is not a token price, and Groq Performance’s provisioned-throughput model is not directly comparable to ordinary per-token billing.
API compatibility and switching providers
Migration may be easier than adapting a completely different API, but compatible request formats do not guarantee full feature or behavior parity. Cerebras has described its inference API as using the familiar OpenAI Chat Completions format. Groq says its API is mostly compatible with OpenAI client libraries: developers can configure the base URL and API key, but Groq also documents unsupported OpenAI features. Cerebras API announcement · Groq OpenAI compatibility
Rank #4
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Before moving production traffic, verify current model IDs, deprecations, limits, and supported features in the provider documentation. Run application tests for response formatting, tool calls, structured output, streaming, and failure handling; then validate capacity and latency under the intended account tier. Model availability and behavior can change independently of API compatibility. Groq model deprecations
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to check before choosing
- Model and quality: Confirm that an active model meets the workload’s quality target and supports the required features.
- Latency profile: Compare first-token delay, inter-token speed, completion time, and high-percentile latency under realistic load.
- Context and API capability: Check context and output limits, tools, structured output, multimodal inputs, and streaming against the application requirements.
- Capacity and reliability: Review rate limits, burst behavior, capacity errors, support, and the exact scope of any service-level agreement.
- Cost: Price the same traffic pattern and include provisioned capacity, minimum commitments, retries, and idle capacity where relevant.
- Geography and data terms: Confirm supported service regions and any residency, retention, or contractual requirements directly with the provider. The cited materials do not establish a complete region-by-region comparison.
Bottom line
Cerebras and Groq make different architectural and service-tier propositions, but their published speed numbers do not prove a current winner. Select the active models that meet your quality and feature needs, benchmark them under the same workload, and make the decision on measured latency, cost, capacity, and the guarantees available to your account.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
