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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchGroq’s Language Processing Unit (LPU) is a processor designed primarily to run trained AI models, not to train them. Through GroqCloud, developers can call hosted LPUs through an API instead of buying and operating Groq hardware. The result is a specialized alternative for real-time language-model inference, alongside dedicated infrastructure and enterprise deployment options.
What is Groq’s AI chip?
Groq’s LPU is a purpose-built accelerator for inference: generating predictions and tokens from models that have already been trained. Groq’s architecture uses a compiler to schedule memory transfers, computations and network-packet transmission deterministically. A single-core design and on-chip SRAM are intended to make response timing more predictable than on systems whose work is distributed dynamically across many processors.
That specialization matters because serving an interactive chatbot, voice assistant or coding tool is different from training a model. Training and large batch jobs favor flexible, massively parallel systems. Inference services often care more about time to first token, steady token rate, tail latency and predictable behavior for each user.
What the LPU is—and is not
- It is: an inference accelerator and the foundation of Groq’s hosted and dedicated services.
- It is not: a general replacement for every GPU workload, especially model training, graphics or highly varied scientific computing.
- It requires: models and software that Groq’s compiler and runtime support; compatibility should be checked before migration.
Why Groq says its chip is fast
Groq attributes performance primarily to controlled scheduling and keeping frequently used data close to the compute units.
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- 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
Deterministic compilation
The compiler maps model operations, memory loads and communication to the LPU ahead of execution. This reduces run-time scheduling uncertainty and is intended to produce more consistent latency, particularly when many requests arrive at once.
On-chip SRAM
Groq emphasizes high-bandwidth on-chip static RAM rather than relying as heavily on off-chip memory movement. Less data movement can reduce bottlenecks for supported inference graphs, although actual results depend on model size, precision, sequence length, batching and serving configuration.
Single-core execution model
Groq describes the LPU as a single-core architecture with a compiler-controlled execution plan. The design trades some generality for a tightly coordinated path through the model.
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- 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 reported 300 tokens per second per user on Llama 2 70B in its April 2, 2024 announcement. That is a company-reported result, not an independent benchmark; it should not be treated as a universal speed for every model or workload.
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What “debut in the cloud” means
GroqCloud launched on March 1, 2024, according to Groq’s April 2, 2024 announcement. Instead of purchasing a server, a customer sends requests to Groq’s hosted inference endpoint and pays for access as a service. Groq described this as Tokens-as-a-Service for experimentation and production.
Available deployment layers
| Layer | What it provides | Best fit |
|---|---|---|
| GroqCloud | Hosted API access to Groq inference hardware | Developers who want to test or ship inference without operating servers |
| GroqMetal | Dedicated bare-metal Groq infrastructure | Organizations needing reserved capacity and infrastructure control |
| GroqCore | Production-ready inference stack | Teams building managed, repeatable serving deployments |
| GroqAssured | Enterprise governance, auditability and control | Regulated or security-sensitive production environments |
Groq also says Groq Systems can be purchased for on-premises deployment. Availability, regional placement and commercial terms can change, so buyers should verify the current offering directly with Groq.
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- ✅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
Current platform scale
Groq’s platform page lists the following rack-level specifications and operating footprint. They are vendor-published figures and are time-sensitive.
| Specification | Published value | Qualification |
|---|---|---|
| LPUs per rack | 256 | Groq platform-page figure |
| SRAM bandwidth | 40 PB/s | Groq platform-page figure |
| On-chip SRAM per rack | 128 GB | Groq platform-page figure |
| FP8 inference compute | 315 PFLOPS | Groq platform-page figure |
| Listed throughput | 1,000 tokens/sec/user | Groq platform-page figure; workload and model conditions are not stated |
| Data centers | 13 across four continents | Groq platform-page statement; location count can change |
Groq LPU versus Nvidia GPUs
The useful comparison is workload-specific rather than a simple “chip A is faster” ranking. Nvidia GPUs are broad, established accelerators used for training, inference, batch processing and visualization-heavy workloads. Groq targets a narrower but important case: interactive inference where consistent response timing matters.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →| Comparison axis | Groq LPU | Nvidia GPU systems |
|---|---|---|
| Primary strength | Real-time, supported-model inference | General-purpose acceleration, including training and inference |
| Latency approach | Compiler-determined scheduling intended to reduce variability | Highly capable, but timing depends on GPU architecture, software, batching and serving stack |
| Memory strategy | Emphasis on on-chip SRAM and planned data movement | Typically relies on high-bandwidth memory and a broader memory hierarchy |
| Software path | Groq compiler maps operations directly to the LPU; CUDA kernels are not required | Extensive CUDA and framework ecosystem |
| Cloud access | GroqCloud, dedicated GroqMetal and enterprise layers | Available from many public-cloud and on-premises providers |
| Best initial question | Can this supported model meet my latency and throughput target? | Do I need flexibility across training, inference and other accelerated workloads? |
No single published speed number settles procurement. A fair evaluation must hold model, precision, prompt and output lengths, batch size, concurrency, software version, price and measurement date constant. Groq’s published figures are vendor claims; independent matched testing is needed for a purchasing decision.
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- 48GB AI graphics accelerator
How to use GroqCloud
- Choose a supported model and region. Confirm that the model, context window, tool-calling features and data-handling requirements fit your application.
- Create GroqCloud access. Obtain the API credentials and usage terms for the account or organization.
- Send inference requests. Use Groq’s API-compatible client path or the integration documented for your framework, keeping the model identifier and token limits explicit.
- Measure your real workload. Record time to first token, tokens per second, p50 and p95 latency, error rates and cost under expected concurrency.
- Harden production operation. Add authentication protection, rate-limit handling, retries that do not duplicate unsafe actions, observability and a fallback provider where uptime requirements justify it.
Groq’s 2025 Meta announcement described a three-line migration from OpenAI as a starting point for developers. Treat that as an integration shortcut, not a guarantee that every OpenAI feature, model behavior or tool API is identical.
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Meta’s official Llama API
On April 29, 2025, Groq and Meta announced a partnership for an official Llama API. The announcement reported throughput of up to 625 tokens per second and said more than 1.4 million developers were using Groq at that time. Both figures were company statements tied to that announcement and are not independent benchmarks.
Aramco Digital and Saudi Arabia
On September 12, 2024, Groq and Aramco Digital announced a Saudi inferencing data center. They said the service would be offered through Aramco Digital’s nawat marketplace in an as-a-Service model and projected billions of tokens per day by the end of 2024. Those were announced plans, not verified outcomes.
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Growth capital and NVIDIA-related technology
In a June 22, 2026 announcement, Groq said it had raised $650 million in growth capital, operated 13 data centers, served more than five million developers, processed trillions of AI tokens each week and planned to scale toward 200 MW by the end of 2027. The same announcement said NVIDIA’s LPX platform incorporates Groq inference technology. These are current company statements and should be rechecked because capacity, customer counts and plans can change.
Who should consider Groq?
- Good candidates: chat, voice, coding and agent applications where interactive latency and predictable service are more important than running training workloads on the same hardware.
- Potentially poor fit: teams training large models, running unsupported architectures, needing broad CUDA-specific software or optimizing for large offline batches rather than user-facing response time.
- Enterprise candidates: organizations that need a hosted API first but may later require dedicated bare metal, governance controls or on-premises deployment.
How to evaluate it responsibly
- Define service-level targets for first-token latency, sustained generation rate and tail latency.
- Replay representative prompts with the same model, precision, context lengths, concurrency and output limits on each provider.
- Measure quality as well as speed: compare refusals, tool calls, structured-output validity and application-level success.
- Include total cost, quotas, egress, observability, support, regional availability and migration effort.
- Run a time-bounded production pilot and repeat measurements after material model or platform changes.
The Bottom Line
Groq’s cloud debut makes its specialized LPU available as an API rather than only as hardware. It is most compelling when a supported model must answer users quickly and consistently; GPUs remain the more versatile choice for training and mixed workloads. Groq’s performance and adoption numbers are promising but company-reported, so matched independent testing should determine whether GroqCloud is the right production platform for a specific application.
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