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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesNeither Cerebras nor Groq can be called the faster inference provider on the published figures alone. Their speed claims refer to different models and dates, and they are not a controlled head-to-head test. The useful distinction is that Cerebras emphasizes wafer-scale processors and on-chip memory, while Groq offers an LPU-based inference cloud with distinct service tiers. For a real buying decision, compare the same model and workload, then weigh response latency, quality, cost, capacity, and contractual guarantees.
How Cerebras and Groq differ
Both companies offer hosted APIs for running AI models, but their public descriptions emphasize different hardware and service choices. Cerebras describes a wafer-scale system designed to keep substantial memory and processing resources on a single processor. Groq presents its LPU-based inference cloud as a service with selectable tiers. The available public materials do not support a detailed chip-to-chip comparison, so the architectural descriptions should not be read as proof that one service is faster for every workload.
Cerebras: wafer-scale processing and on-chip memory
Cerebras says its WSE-3 processor, used in the CS-3 system, has 900,000 AI-optimized cores, 44 GB of on-chip SRAM, and 21 petabytes per second of memory bandwidth. These are Cerebras-published hardware specifications, not independently verified performance results. The company’s architectural rationale is that keeping memory on the wafer can reduce the movement of model data that can constrain autoregressive text generation. Cerebras’s explanation of its architecture and AWS integration provides the company’s description.
In an August 2026 discussion of its CS-4 and Nexus rack-scale platform, Cerebras also reported 53.5 petabytes per second of aggregate on-wafer fabric bandwidth for WSE-3T. That is a separate fabric-bandwidth figure, not another way of expressing the WSE-3 memory-bandwidth number. Cerebras’s Hot Chips 2026 article describes the CS-4 discussion and claim.
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#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.
Groq: an LPU-based hosted service
Groq describes its inference cloud as LPU-based and publishes a catalog of supported models alongside API documentation, rate limits, and service tiers. The reviewed public materials do not offer architecture specifications at the same level of detail as Cerebras’s materials, so a precise hardware comparison would go beyond what those sources establish. For a buyer, Groq’s published tier and model information is more directly useful for understanding how access and capacity may differ by account. See the Groq model catalog and Groq service-tier documentation.
Do published speed figures show which is faster?
No. The available numbers are provider-published figures for different models, from different dates, and do not establish a current winner in a like-for-like test.
| Provider and published figure | What the figure refers to | How to interpret it |
|---|---|---|
| Cerebras: 1,800 tokens per second | Llama 3.1 8B, announced August 27, 2024 | A dated, provider-reported result—not a current comparison against Groq under matched conditions. Cerebras launch announcement. |
| Cerebras: 450 tokens per second | Llama 3.1 70B, announced August 27, 2024 | A dated, provider-reported result. It is not directly comparable to Groq’s listed Llama 3.3 70B figure because the model versions differ. Cerebras launch announcement. |
| Groq: 560 tokens per second listed | Llama 3.1 8B Instant in Groq’s model documentation, accessed October 7, 2026 | A rate listed by the provider, not a neutral benchmark. Groq’s deprecation documentation says its Llama 3 8B models were shut down for free and developer tiers in August 2026; confirm that the model is active for your account tier. Model catalog; deprecation schedule. |
| Groq: 280 tokens per second listed | Llama 3.3 70B Versatile in Groq’s model documentation, accessed October 7, 2026 | A provider-listed rate, not a matched test against Cerebras’s Llama 3.1 70B figure. Confirm current model and tier availability in the catalog and deprecation schedule linked above. |
Tokens per second alone do not describe the speed a user experiences. A service can generate tokens quickly after generation begins yet take longer to return the first token, or respond differently when context length, concurrency, or service tier changes. Network time between a client and hosted service also contributes to perceived latency; Groq’s latency guide distinguishes server-side latency from network time.
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
How to benchmark them fairly
Run a small workload that reflects your application rather than comparing headline rates. Match the exact model ID and revision, precision, prompt and context lengths, output length, concurrency, streaming setting, and service tier. Measure:
- Time to first token and token-to-token latency, as well as total response time.
- Throughput and p95/p99 latency at realistic concurrency—not just a single request.
- Error and retry rates, including what happens when capacity is unavailable.
- Task-specific output quality, using the same prompts and evaluation criteria.
- Total cost for the same input/output mix, including any provisioned capacity, minimum commitment, retries, or idle capacity.
Confirm that the exact model is currently offered to your account before running the test. Model names and tier access can change; the Groq deprecation schedule is particularly relevant if a test or application relies on its Llama 3 8B or 70B models.
Availability, service tiers, and reliability
“Available” can mean that a model appears in a catalog, that an account can call it, or that capacity is dependable enough for a production service. Check all three, and distinguish a best-effort tier from an enterprise commitment.
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.
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 and said production subscriptions and enterprise tiers offer higher capacity, priority routing, and dedicated support. Those are the access details in that announcement, not a guarantee that every model or region is continuously available. Confirm current onboarding, model access, and prices in the live service. Cerebras’s 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; and Performance is an enterprise tier. Groq’s Performance documentation states a 99.9% availability SLA and a 99% low-latency guarantee, with the details set out in the customer’s offline agreement. The documentation describes Performance as sold through provisioned-throughput bundles rather than ordinary per-token pricing. Do not treat those guarantees as applying to free, developer, on-demand, or Flex accounts. Review the service-tier overview and Performance tier terms for the scope relevant to your account.
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Which service is cheaper?
The published material here does not establish a current, comparable price for a specific workload, so it cannot support a claim that one provider is cheaper. Cerebras’s $10 figure was a starting deposit described in its October 2025 announcement, not a per-token price or an estimate of ongoing spend. Groq’s Performance tier uses provisioned-throughput bundles, which are not directly comparable with ordinary per-token billing without knowing the workload and contract.
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 finance or procurement decision, calculate cost using the model and traffic you actually intend to run. Compare input and output charges separately where applicable; account for peaks, retries, provisioned capacity, minimums, and idle time; and include the cost of a slower response or lower-quality output if it changes the application’s economics. Verify current prices and terms directly with each provider rather than relying on an old announcement or a speed figure as a proxy for value.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can you switch between their APIs?
Both providers describe compatibility with familiar OpenAI-style client patterns, which can reduce the work of changing endpoints. Groq says its API is mostly compatible with OpenAI client libraries: developers can configure the base URL and API key, but some OpenAI features are unsupported. Cerebras has described its inference API as using the OpenAI Chat Completions format. Neither statement means full feature or behavioral parity. Check current model IDs, request parameters, streaming behavior, limits, tool use, structured output, and other required features before sending production traffic. See Groq’s OpenAI compatibility documentation and Cerebras’s API announcement.
Before routing live requests to a replacement provider, test a representative sample, validate output quality and error handling, and use a gradual rollout with a fallback plan. The provider’s current model catalog and deprecation information matter as much as endpoint compatibility.
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Compare the services against your requirements in this order:
- Model and quality: Verify the exact supported model ID, version, precision, and task performance. Hardware speed cannot make up for a model that misses your quality target or lacks a required capability.
- Latency profile: Measure first-token delay, inter-token speed, total time, and tail latency under your expected concurrency. Include client network conditions when judging user experience.
- Context and API features: Check context and completion limits, structured output, tools, multimodal input, and streaming for the exact model and account tier.
- Capacity and reliability: Inspect default limits, burst behavior, capacity errors, support arrangements, and the scope of any SLA. Treat best-effort access and an agreement-backed enterprise guarantee as different products.
- Cost for your traffic: Model input and output volume, peak usage, retries, commitments, and idle provisioned capacity using current prices and contract terms.
- Geography and data terms: Confirm service regions, residency, retention, and contractual requirements with the provider for your account. The published materials cited here do not establish a complete region-by-region comparison.
Choose Cerebras if its currently available models, access terms, and measured results suit the workload; choose Groq if its model catalog and tier options do. If the service is business-critical, make the decision from a workload-matched benchmark and the actual agreement—not from the providers’ unlike headline speed claims.
Quick Recap
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