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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteNeither Cerebras nor Groq can be declared the faster choice from the published figures alone. Cerebras emphasizes wafer-scale processors and on-chip memory; Groq offers an LPU-based inference cloud with multiple service tiers. Their speed claims refer to different models and dates, so the useful comparison is the exact model, workload, latency, cost, capacity, and reliability your application needs.
How do Cerebras and Groq differ architecturally?
Cerebras: wafer-scale processing and on-chip memory
Cerebras describes its WSE-3 processor, used in the CS-3 system, as combining 900,000 AI-optimized cores with 44 GB of on-chip SRAM and 21 petabytes per second of memory bandwidth. The vendor’s stated rationale is that keeping substantial memory on the processor can reduce data movement and interconnect bottlenecks during autoregressive decoding. These are Cerebras-published specifications, not independent measurements. Cerebras’s architecture and AWS integration article gives its account of the approach.
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Groq: an LPU-based inference service
Groq describes its hosted inference offering in terms of its LPU platform and service tiers. The Groq materials cited here provide model listings, API guidance, and tier details, but not hardware specifications at the same level of detail as the cited Cerebras materials. That means a precise chip-to-chip comparison is not established by these sources; the defensible distinction is Cerebras’s emphasis on wafer-scale integration and on-chip memory versus Groq’s LPU-based inference cloud. See Groq’s model catalog and service-tier documentation.
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In an August 2026 discussion of CS-4 and its Nexus rack-scale platform, Cerebras reported 53.5 petabytes per second of aggregate on-wafer fabric bandwidth for WSE-3T. This is a distinct fabric-bandwidth figure: it should not be treated as another way of stating the WSE-3 memory-bandwidth figure or as an independently verified comparison with Groq. Cerebras’s Hot Chips 2026 article describes the claim.
#1 Best Overall
- High-Performance AI Processing: The MX3 is designed to handle the most demanding AI computer vision workloads, delivering exceptional performance and efficiency.
- Flexible Integration: The MX3 can be easily integrated into your existing systems via its M.2 M-key form factor and support for Linux operating systems.
- Energy Efficient: The MX3 is designed to provide high performance while minimizing power consumption.
- Comprehensive Software Development Kit (SDK): The MX3 is supported by a comprehensive SDK that simplifies development and deployment.
- Hardware compatability: The MX3 is compatible with the PCI-SIG M.2 M-key 2280 Specification. It can be used with the Raspberry Pi 5 with a M-key 2280 HAT.
What do the published speed figures show?
The figures below are useful context, not a head-to-head test. They involve different model generations and dates, and provider-published rates do not establish how either service will perform on your prompts, account tier, or network.
| Provider and source date | Model and published figure | How to interpret it |
|---|---|---|
| Cerebras, August 27, 2024 | Llama 3.1 8B: 1,800 tokens per second | Historical provider-reported launch figure; not a current cross-provider result. |
| Cerebras, August 27, 2024 | Llama 3.1 70B: 450 tokens per second | Historical provider-reported launch figure; not directly comparable to Groq’s listed Llama 3.3 70B rate. |
| Groq model documentation, accessed October 2026 | Llama 3.1 8B Instant: 560 tokens per second | A rate listed by Groq, not a neutral benchmark. Check whether this model is active for your account tier. |
| Groq model documentation, accessed October 2026 | Llama 3.3 70B Versatile: 280 tokens per second | A rate listed for a different model generation from Cerebras’s cited 70B figure. Check current model and tier availability. |
Cerebras’s August 2024 announcement compared its 8B result with Groq, but that dated vendor comparison is not evidence of a present-day winner. Groq’s deprecation documentation says its Llama 3 8B and 70B models were shut down for free and developer tiers in August 2026 and names recommended replacements. A model appearing in a catalog or in a past speed claim does not by itself establish that it is available to your tier today. Verify the live catalog and deprecation schedule before building around a model. The figures are in Cerebras’s launch announcement and Groq’s model catalog.
Rank #2
- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
Why tokens per second are not enough
Generation speed is only one part of an interactive response. Time to first token affects how quickly a user sees a reply begin; inter-token latency affects its perceived pace; total response time depends on prompt processing and output length. Under concurrent traffic, throughput, tail latency, and errors can matter more than a single request’s peak rate. Network time between your users, application, and provider also affects experienced latency; Groq’s latency guide distinguishes server-side latency from network time.
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How do access, service tiers, and reliability compare?
Cerebras access
Cerebras announced self-serve pay-per-token access on October 13, 2025, and said developers could start with a $10 deposit. That is an announcement detail, not a complete current price list or a guarantee about onboarding, model availability, or capacity. The same announcement describes Cerebras Code Pro and Max subscriptions and says production subscriptions and enterprise tiers offer higher capacity, priority routing, and dedicated support. Confirm current terms in the provider’s live service information. Cerebras’s pay-per-token announcement sets out those access options.
Rank #3
- Professional AI & Creator Workstation: AMD Radeon AI PRO R9700 GPU with 32GB GDDR6 is engineered for AI development, professional content creation, and compute-intensive workloads.
- Massive 32GB Memory Capacity: 32GB of GDDR6 memory on a 256-bit bus provides ample bandwidth for large AI models, 8K video editing, and complex 3D rendering.
- Advanced RDNA 4 with AI Accelerators: 64 Compute Units with 3rd Gen Ray Tracing and dedicated 2nd Gen AI Accelerators for groundbreaking AI performance and visual computing.
- Professional Blower Cooling: Efficient single blower design exhausts heat directly out of the chassis, ideal for multi-GPU workstation and server configurations.
- Enterprise-Grade Thermal Solution: Vapor chamber heatsink with industrial Honeywell PTM7950 thermal interface material ensures reliable cooling under sustained professional loads.
Groq tiers
| Groq tier or option | Published description | Reliability or capacity detail |
|---|---|---|
| On-Demand | Default service tier | Consult current account documentation for limits and terms; no enterprise Performance guarantee should be assumed. |
| Flex | Higher-throughput, best-effort tier | May return capacity errors when capacity is unavailable. |
| Auto | Routing option | Specific behavior and availability should be checked in current documentation. |
| Performance | Enterprise tier sold through provisioned-throughput bundles rather than ordinary per-token pricing | Groq documents a 99.9% availability SLA and a 99% low-latency guarantee; details are in the customer’s offline agreement. |
The Performance figures apply to that enterprise tier and its agreement, not to Groq accounts generally. The service-tier descriptions are in Groq’s tier documentation and Performance tier documentation.
Which provider is cheaper?
The available figures do not establish which provider costs less for a given application. Cerebras’s cited $10 amount is a starting deposit announced in 2025, not a per-token rate. Groq’s Performance tier uses provisioned-throughput bundles, and the cited material does not provide a directly comparable price for the same model and traffic pattern.
Rank #4
- ✅Powered by 26 Tera-Operations Per Second (TOPS) Hailo-8 AI Processor. 2.5W typical power consumption
- ✅Scalable, enabling simultaneous processing of multi-streams & multi-models
- ✅Enabling real-time, low latency and high-efficiency AI inferencing on the edge devices
- ✅Supports TensorFlow, TensorFlow Lite, ONNX, Keras, Pytorch frameworks
- ✅Supports Linux and Windows. Supports the temperature range of -40°C to 85°C
For a meaningful cost comparison, use current quotes or account pricing for the same model and workload. Include input and output token charges where applicable, provisioned capacity, minimum commitments, retries, and idle capacity. A lower per-token rate can still produce a higher bill if responses are longer, retries are more frequent, or reserved capacity sits unused.
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Run a controlled test against the models and account tiers you can actually use. Match the task and prompt, not just the model’s parameter class, and collect enough requests to see ordinary and high-load behavior.
- Choose the exact active model IDs. Check each provider’s current catalog, version, and tier restrictions. Do not treat differently named or different-generation models as a like-for-like speed test.
- Hold the workload constant. Use identical prompts and context lengths, comparable output limits, the same streaming setting, and the same concurrency pattern.
- Measure the whole response. Record time to first token, inter-token latency, total response time, throughput under load, and error rate. Include client-side network time if user experience is the goal.
- Check quality and required features. Evaluate task-specific answer quality and confirm context limits, maximum completion, structured output, tools, multimodal inputs, and streaming support where needed.
- Compare the operating terms. Record prices for the traffic mix, rate limits, burst behavior, capacity failures, support, SLA scope, and any geography or data-handling requirements.
- Test failure and recovery paths. Observe what happens when capacity is constrained, requests time out, or a model is retired; measure the impact of retries and fallback routing rather than assuming either provider handles these cases identically.
Can you switch between the APIs?
Both providers describe ways to ease migration, but compatibility does not guarantee identical behavior. Groq says its API is mostly compatible with OpenAI client libraries: developers can configure the base URL and API key, while some OpenAI features remain unsupported. Cerebras has described its inference API as using the familiar OpenAI Chat Completions format. Treat these formats as a starting point, then verify the current endpoint, model IDs, supported parameters, tool behavior, streaming, limits, and error handling before moving production traffic. See Groq’s OpenAI compatibility documentation, Cerebras’s API announcement, and Groq’s deprecation schedule.
What should determine the choice?
- Model and quality: Choose from the models currently supported on your intended account tier and test their quality on representative tasks.
- Latency and throughput: Use measured first-token time, response duration, inter-token pacing, and loaded throughput rather than relying on a single advertised tokens-per-second figure.
- Features and context: Confirm that the required context size, output limits, tools, structured responses, streaming, or multimodal inputs are supported.
- Capacity and service commitment: Match your reliability needs to the actual tier, limits, support, and agreement. A best-effort option and an agreement-specific enterprise guarantee are not interchangeable.
- Total cost: Price the same workload and traffic mix, including reserved capacity and retries where relevant.
- Geography and data obligations: Confirm service regions, residency, retention, and contractual requirements for your own account. The cited materials do not establish a complete cross-provider region comparison.
For a prototype that values rapid token generation, test whichever current model and tier meet its feature and cost requirements. For a production system, decide from workload-specific measurements and written service terms, not from the providers’ unmatched speed figures.
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