Groq provides industry-leading inference speed through its self-developed language processor (LPU). In the field, it can run mainstream open source models such as Llama 4 and Qwen3 at the fastest inference speed of 1000 tokens/second. It provides developers with free API Keys and pays as low as $0.075/million tokens. It is the preferred inference platform for building low-latency AI applications.

Groq — Self-developed LPU-driven ultra-high-speed AI inference platform

Groq’s core parameters and statistics

Parameters	Current public information
Product positioning	Self-developed LPU hardware-driven AI inference API service platform
Place of Residence	United States (US)
Supported Platforms	Web (GroqCloud Console), API
Core hardware	LPU (Language Processing Unit, language processor)
Peak inference speed	Up to 1,000+ tokens/second (Llama 3 70B level model)
First Token Output Time (TTFT)	Usually < 100ms
API Compatibility	OpenAI API Compatible Interface (Chat Completions Format)
Free quota	Free API Key, with rate limit (RPM + TPM dual control)
Pay-as-you-go billing range	$0.075–$0.60/million tokens (floating according to model specifications)
Number of supported models	20+ open source models (including Llama, Qwen, DeepSeek, Mixtral series)
Founder	Jonathan Ross (former Google TPU core designer)
Financing stage	Multiple rounds of top VC support, undisclosed valuation

The most identifiable metric among Groq's core parameters is inference speed. On the Llama 3 70B level model, its LPU inference speed can reach 1,000+ tokens/second, and the time to first token output (TTFT) is usually less than 100ms. This performance translates directly into a "conversation-free" experience in conversational and streaming applications - users barely feel that the AI ​​is "thinking". Comparing the performance of the NVIDIA H100 GPU solution on similar models at about 100–200 tokens/second, the gap can reach 5–10 times. It should be pointed out that Groq’s free quota has dual limits of RPM (requests per minute) and TPM (tokens per day). The specific thresholds are not disclosed. High-frequency production use needs to be bound to a payment method upgrade.

Groq’s users and market recognition

Benchmark popularity in the developer community: Since the public beta was launched in February 2024, the Groq API has attracted phenomenal attention in the developer circles of Hacker News, X/Twitter and Reddit. The Groq speed comparison videos posted by many developers have received millions of views on social platforms, and the perception that "Groq is as fast as lightning" has spread widely among the AI ​​developer community. There were queues for API Key applications in the early stages of the public beta, which is rare among API products and reflects the market's strong demand for the combination of "low cost + high-speed reasoning".

Long-term dominance in third-party evaluations: In the AI ​​inference speed rankings of the independent benchmark platform Artificial Analysis, the models supported by Groq have long occupied the first place in each specification segment. Its Llama 3 70B inference speed (output tokens/second) is more than 2 times higher than the second place in multiple tests. It is the first cloud inference service to achieve "instant response" on consumer-level AI tasks.

Financing and Business Progress: The company was founded by Jonathan Ross (former Google TPU core designer) and has completed several rounds of financing, with investors including top Silicon Valley VCs. Although the specific valuation has not been made public, its financing pace and the participation of well-known investment institutions indicate the capital market’s recognition of LPU’s technology route. On the production side, several well-known AI applications and SaaS products already use Groq as the primary inference backend for real-time conversation and code assistant scenarios.

Community Activity: Groq's official sample repository and SDK project on GitHub are continuously updated, and together with the active Discord community, a developer ecosystem centered around "rapid reasoning" has been formed. However, compared with more general platforms such as Hugging Face and LangChain, Groq's ecological scale is still small, and the richness of third-party tool chains and tutorials needs to be improved.

Groq’s cost advantage: the three-tier structure comprehensively lowers the entrance threshold for reasoning

Groq's cost advantages extend from three levels, covering individual developers to large enterprises, and each level provides support for the cost-effectiveness of "open source model + high-speed inference".

Personal/Developer Level

• Free API Key: You can get it by registering, no need to bind a credit card. The free RPM and TPM limits are enough to support daily development testing, personal projects, and small- and medium-scale prototype verification. For independent developers and students, zero-cost access to high-speed inference is an extremely low starting threshold.
• Hidden Cost: The free quota may be insufficient for production-level applications that require continuous operation, and you need to switch to the paid tier. Alternatively, the free tier may have the same model selections as the paid tier, but with stricter rate limits.

Developer/API layer

Groq's pay-as-you-go pricing is among the lowest among similar services. The following is a price comparison between Groq’s main model and its main competitors:

Service provider	Model specifications	Input price ($/million tokens)	Output price ($/million tokens)	Remarks
Groq	Llama 3.1 8B	$0.05	$0.08	Lightweight inference at extremely low cost
Groq	Llama 3.3 70B	$0.59	$0.79	Main model, balanced cost performance
Groq	Llama 4 Scout	$0.11	$0.34	A new generation of efficient MoE models
Groq	Llama 4 Maverick	$0.50	$0.77	High Power MoE Model
Groq	Qwen3-32B	$0.29	$0.39	An open source model with outstanding Chinese capabilities
Together AI	Llama 3.3 70B	$0.88	$0.88	Similar open source model hosting
Replicate	Llama 3.3 70B	$0.65	$0.65	Billed by the second, about the same price
OpenAI	GPT-4o	$2.50	$10.00	Closed source business model, 10–50 times more expensive
Anthropic	Claude 3.5 Sonnet	$3.00	$15.00	Closed source business model, 10–50 times more expensive

As can be seen from the table above, the price of Groq's open source model with the same specifications is significantly lower than the closed source commercial models of OpenAI and Anthropic (10–50 times cheaper). It is also in a more competitive range compared with direct competitors such as Together AI and Replicate. For production-grade applications with high token consumption, this price gap directly translates into calculable operating cost savings.

Enterprise layer

• Enterprise Exclusive Agreement: For enterprise customers with high concurrency and high SLA requirements, Groq provides exclusive pricing and service level agreements, including higher rate limits, priority technical support, and custom model deployment options. The price needs to be confirmed by contacting the business team, and the standardized quotation has not been published.
• Hidden Cost: An important dimension that enterprise customers need to evaluate is that Groq currently only provides cloud inference services, and there is no public privatized deployment plan. For enterprises with data sovereignty requirements or compliance restrictions, this may mean having to supplement with an on-premises GPU solution, creating a double spend. In addition, the LPU hardware ecosystem is single and there is no multi-vendor competition like the GPU market, so long-term bargaining space may be limited.

Main functions of Groq

• LPU ultra-high-speed inference engine: The self-developed language processor (LPU) is specially designed for the sequential token generation task of the Transformer model, and the inference speed is 5–10 times faster than GPU solutions at the same price. Applicable tasks: All latency-sensitive NLP tasks - real-time conversations, stream generation, code completion, voice interaction. Batch tasks that require high throughput also benefit from short single inference times.
• OpenAI compatible API: The Groq API interface and parameter format are highly consistent with the OpenAI Chat Completions API. Existing code using OpenAI SDK only needs to modify base_url and API Key to switch to Groq without refactoring the calling logic. This compatibility strategy greatly reduces the migration friction for developers and is a key factor in Groq’s rapid accumulation of users in the early stages.
• Streaming: Supports SSE (Server-Sent Events) streaming output, with extremely low first token delay. At the UI level, the "typewriter" effect of token-by-token rendering can be achieved, and the user-perceived delay is much lower than waiting for a complete response and then outputting it all at once. This is an important reason why Groq has a better experience than GPU inference services in real-time conversation and AI writing assistant scenarios.
• Structured Output (JSON Mode): The response_format parameter is used to constrain the model output to legal JSON, and the format can be verified according to the predefined JSON Schema. Applicable tasks: Data extraction API Response generation, structured reports - ensure that downstream parsing is not broken due to format abnormalities, and reduce repeated requests caused by output format errors.
• Function Calling: Supports Function Calling in OpenAI format, allowing the model to select and call external tools (database queries, calculators, search engines, etc.) during the inference process. This is the core capability for building AI Agents. Developers can build autonomous Agents with multi-step reasoning and tool usage based on Groq.
• Multiple models on-demand switching: GroqCloud console and API support instant switching between 20+ open source models, different models are suitable for different tasks——

Lightweight models (such as Llama 3.1 8B) are suitable for simple question answering and classification, medium-sized models (such as Llama 3.3 70B) are suitable for complex reasoning, and very large models (such as Llama 3.1 405B) are suitable for high-precision long text generation.

• GroqCloud Console: The web-side management platform provides model playground (code-free testing), API Key management, usage monitoring, bill viewing, and model performance indicator visualization. Developers can evaluate each model's performance and speed on specific tasks without writing any code.
• Rate Limit and Usage Management: The console provides real-time rate consumption charts and usage warning settings to help developers avoid service interruptions due to exceeding free quota or rate boundaries. Paid users can adjust the rate limit cap through the console.

Groq’s model and version evolution

Groq's core positioning is reasoning infrastructure, so its "version evolution" is mainly reflected in two aspects: the iteration of the LPU chip itself and the expansion of the scope of the API platform docking model.

Hardware and Platform Milestones

Time	Event	Meaning
2016	Groq company was established and started LPU chip research and development	The founding team designed a dedicated chip for AI reasoning from scratch
2020	The first-generation LPU chip was successfully taped out and verified	Confirming the speed advantage of the SRAM architecture in Transformer inference
2023	GroqCloud provides inference services to enterprise customers	The first batch of commercial customers are connected to verify product market fit
2024-02	Groq API public beta is open, supporting Llama 2 + Mixtral	Open to global developers, API Key application queue
2024-04	Llama 3 was launched simultaneously with online support on the day of release	Demonstrated the ability to quickly follow up on mainstream open source models, and the reputation exploded
2024-07	Access to Llama 3.1 405B (405 billion parameters)	Prove the LPU architecture’s ability to support very large parameter models
2025-01	Access to DeepSeek-R1-Distill series	Expand inference model support to cover emerging popular open source models
2025-04	Connect to Meta Llama 4 Scout/Maverick	Follow up on Llama 4 architecture (MoE) and maintain the competitiveness of the model library
2025-05	Access to Qwen3 series (multiple specifications)	Enhance Chinese capability coverage to attract developers in the Asia-Pacific region
2025–2026	Continue to follow up on the latest open source model releases	The model library is expanded to 20+ models, covering the mainstream open source ecosystem

Model library features

• Fast Track Strategy: Groq usually completes integration within 3–7 days after the release of mainstream open source models. This speed is among the fastest in the current inference API market and directly benefits from LPU’s unified inference architecture and the team’s streamlined model adaptation process.
• Main model cluster: The current core model pool includes Llama 4 Scout/Maverick, Llama 3.3 70B, Llama 3.1 405B, Qwen3-8B/14B/32B/72B/235B, DeepSeek-R1-Distill-Llama-70B, Mixtral 8x7B, etc., covering multiple gears from lightweight to ultra-large parameters.
• Time difference in model access: Although Groq follows up quickly, due to the uniqueness of hardware adaptation, the launch time of new models is still later than that of competing products that directly use NVIDIA GPUs (such as Together AI and Fireworks AI). The latter only need to do software-level adaptation. This is the inherent cost of Groq's dedicated hardware route in terms of model timeliness.

Groq’s technical advantages

The fundamental difference between LPU architecture vs GPU architecture: The root of Groq’s speed leadership lies not in software optimization, but in the generational differences in hardware architecture. NVIDIA GPU was originally designed for matrix multiplication in graphics rendering, and was later reused for AI training and reasoning through the CUDA ecosystem; its memory uses HBM (high-bandwidth memory), and the performance bottleneck lies in the bandwidth from HBM to the computing unit. The LPU is designed from scratch for Transformer inference, using SRAM (static random access memory) as the main storage - SRAM's read and write speeds are several times faster than HBM, and its power consumption is lower. This means that LPU eliminates "moving data", the biggest bottleneck in GPU reasoning, thereby achieving order-of-magnitude latency compression in the scenario of sequential token generation.

Deterministic scheduling eliminates delay jitter: GPU operation relies on dynamic memory management and thread scheduling, and there is unpredictable jitter (variance) in reasoning delay. LPU adopts deterministic computing scheduling. The memory access and computing unit allocation of each computing step are determined at the compilation stage, and no dynamic scheduling overhead is incurred at runtime. This makes Groq's inference latency not only fast, but also stable - for production systems that require strict SLAs (such as financial trading AI, real-time voice conversations), low jitter is equally important as low latency.

Benefits and costs of the software stack: LPU’s software ecosystem is currently much smaller than the NVIDIA CUDA ecosystem. This means that Groq has a shortcoming in model coverage - not all open source models can run on the LPU, and the adaptation of some models requires additional engineering investment. To compensate, Groq chose OpenAI API compatibility as the software layer standard. Developers do not have to learn new API formats, and the amount of modification of existing code is close to zero. This strategy has achieved significant returns in terms of developer experience, but whether the combination of "hardware differentiation + API standardization" can maintain competitiveness in the long term depends on whether the iteration speed of LPU can keep up with the expansion of the GPU ecosystem.

Energy-saving advantages: Since the power consumption of SRAM is much lower than that of HBM, and the deterministic calculation of LPU does not require complex dynamic power management, Groq also has advantages in energy consumption per token. Although Groq does not disclose specific energy efficiency data, it can be inferred from architectural principles that with the same amount of inference, the total energy consumption of the LPU solution is lower than that of a GPU cluster with the same throughput. This is potentially attractive to businesses considering "green AI" and long-term operating costs.

How to use Groq

Entrance	Applicable objects	Main purposes
GroqCloud console (https://console.groq.com)	All users	Register to obtain API Key, Playground test model, usage monitoring, bill management
Python SDK (groq package or openai package)	Python developers	Integrating Groq inference capabilities in Python applications
REST API	Any language	Call Groq inference endpoints directly over HTTP
Curl / Command Line	All developers	Quickly test and debug API responses

Typical Python call example (including key parameters):

from groq import Groq

client = Groq(api_key="<YOUR_API_KEY>")

response = client.chat.completions.create(
    model="llama-3.3-70b-versatile", # model identification
    messages=[
        {"role": "system", "content": "You are a technical documentation assistant who is proficient in Chinese."},
        {"role": "user", "content": "Please explain the working principle of Groq LPU in Chinese, within 200 words."}
    ],
    temperature=0.7, # Control generation randomness, range 0–2
    max_tokens=500, # Maximum output tokens
    stream=True, # Enable streaming output
    response_format={ # JSON schema (optional)
        "type": "json_object"
    }
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

Curl call example (non-streaming):

curl -X POST "https://api.groq.com/openai/v1/chat/completions" \
  -H "Authorization: Bearer <YOUR_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-3.3-70b-versatile",
    "messages": [{"role": "user", "content": "Hello"}],
    "temperature": 0.7,
    "max_tokens": 100
  }'

Usage steps:

1. Visit https://console.groq.com and register with a GitHub or Google account (free, no credit card required).
2. Click "Create API Key" on the "API Keys" page on the left side of the console and copy the generated key.
3. Select the access method according to the development language: Python recommends pip install groq; other languages ​​use the standard REST API.
4. Check the currently supported model list and its specification identification (model ID) on the "Models" page of the console.
5. Monitor real-time consumption and set usage warnings through the "Usage" page of the console.

Groq Product Pricing

The pricing model is subject to the official real-time page. Usually a freemium or subscription system is adopted, basic functions can be used for free, and advanced functions or high-frequency use require payment.

Application scenarios of Groq

Scenario 1: Real-time dialogue AI and voice assistant When building customer service robots, voice assistants and real-time question and answer systems, the time to first token output (TTFT) directly determines the user experience. Groq's LPU inference can achieve TTFT of <100ms on the Llama 3 70B model. With streaming output, users can hardly feel that the AI ​​is "thinking". For voice AI scenarios (such as voice awakening + large model understanding), low latency is particularly important - the experience of waiting for 2 seconds for a reply after the user finishes speaking is unacceptable in high-frequency interaction scenarios. In such scenarios, Groq can replace solutions that require local deployment of high-performance GPUs and achieve near-local real-time performance in the form of cloud services.

Scenario 2: AI code assistant and IDE integration Code completion and code explanation are among the most latency-sensitive AI tasks—if developers wait more than a second to see suggestions after entering code, it interrupts the programming flow. Groq's extremely low output latency allows cloud-based AI code assistants to approach the responsiveness of local models such as CodeLLaMA, while calling larger 70B-level models for higher quality recommendations. A number of Groq-based IDE plug-ins and coding tools have been put into production, mainly for the VSCode and JetBrains ecosystem.

Scenario 3: High-throughput content processing pipeline In the fields of content platforms, media publishing and data processing, tasks such as document summarization, batch classification, tag generation, SEO Meta description generation and other tasks usually require processing hundreds of thousands of pieces of content. Use the Groq API to compress processing time from hours to minutes. For example, for a content platform that processes 500,000 articles per day, the median latency of using Groq to process document summaries is 5–10 times lower than the GPU solution, directly reducing the waiting time and computing cost of the job scheduling system. The hidden benefits in this scenario are also reflected in that since single inference is faster, the time cost of retrying even if the task fails is much lower than that of traditional solutions.

Scenario 4: AI Agent prototype and production deployment Groq's native support for Function Calling and tool invocation makes it suitable for building AI agents. Developers can quickly build Agents with search and database query API calling capabilities on Groq, and use its low-latency features to achieve rapid feedback for multi-step reasoning. It is suitable for customer service automation IT work order processing, data query assistant and other scenarios. It should be noted that multiple reasoning steps in the Agent scenario are executed serially, and Groq's low latency at each step will form a cumulative advantage. However, the total response time of the Agent is still constrained by the delay of external tool calls - Groq can solve the "fast model thinking" part, not the "fast tool execution" part.

Scenario 5: Education and Research Verification Students and researchers at universities and research institutions can take advantage of Groq's free API credits to quickly validate experimental hypotheses based on Llama or Qwen models without requiring budget or GPU quotas. In scenarios such as paper reproduction prompt engineering experiments and model behavior testing, Groq's free quota can cover most medium-scale experimental needs.

Applicable groups of Groq

• AI application developers and independent entrepreneurial teams: core adaptable group. Benefit from the extremely low migration cost of starting with a zero-cost free quota, OpenAI-compatible APIs, and the improved product experience brought by low-latency inference. It’s important to note that if a product relies on closed-source models (GPT-4o, Claude, Gemini), Groq won’t be able to accommodate the demand – it currently only hosts open-source models.
• Backend Engineers and DevOps Teams: Technical teams that need to integrate AI capabilities into existing systems. Groq's standard REST API and OpenAI-compatible design make the back-end integration path clear, and there is no need to learn additional dedicated SDKs. Points that need to be paid attention to during the evaluation include: the capability gap between the open source model and the closed source model in certain business scenarios, as well as the availability SLA of the Groq API (the enterprise version can obtain a higher reliability guarantee).
• AI researchers and students: Free API quota provides high-speed access to 20+ mainstream open source models, suitable for experimental exploration, model comparison, and paper verification. Unsuitable scenarios: Research tasks that require access to model weights or model fine-tuning (Groq only provides inference services, not training or fine-tuning functions).
• Content Platform & Media Technology Team: Groq's combination of speed and cost offers significant advantages in high-throughput batch processing tasks (article classification, summarization, tag generation, content moderation). Not suitable for scenarios: tasks that require direct understanding of multi-modal content such as images, videos, and audios (Groq’s current API is mainly used for text reasoning and has limited multi-modal capabilities).
• High-frequency trading and financial AI scenarios: The low latency and low jitter characteristics of LPU are theoretically suitable for real-time data analysis and decision-making assistance in the financial field. However, it should be noted that Groq currently has no public on-premises deployment solutions, and data sovereignty and compliance requirements commonly held by the financial industry may pose obstacles. Adoption by this group depends on whether Groq launches a privatized unit in the future

deployment or dedicated regional deployment options.

• Not suitable for people: Enterprises that require local/private deployment (Groq is a pure cloud service, no public privatization plan); users who require multi-modal reasoning (image generation/understanding, video analysis); development teams that rely on closed-source business models; teams that require model fine-tuning or training services.

Summary and Outlook

With self-developed LPU hardware as the core of differentiation, Groq has established a leading position that is difficult to match in the current market in the single dimension of AI inference speed. It encapsulates this hardware advantage into an OpenAI-compatible API service, and builds clear competitive barriers in the open source model inference market with a combination of "extremely low price + extremely fast speed". Judging from market feedback, Groq is highly recognized by the developer community, and many well-known AI products have been integrated into production environments; from a technical perspective, the advantages of LPU's SRAM architecture and deterministic scheduling in reasoning scenarios are supported by solid hardware principles and are not a flash in the pan marketing rhetoric.

Core Competencies: The speed advantage formed by LPU hardware cannot be equaled by pure software optimization; the OpenAI compatible API maximizes developer migration efficiency; the three-tier pricing structure (free → pay-as-you-go → enterprise) covers the entire funnel from individuals to large enterprises; the ability to quickly follow mainstream open source models ensures that the model library continues to be attractive.

Current Limitations and Risks:

1. Model coverage is limited: Only open source models are supported and cannot compete with top closed source models such as GPT-5 and Claude 4 in terms of upper limit of capabilities; some newly developed models may take a long time to adapt to the LPU architecture.
2. No privatized deployment plan: For industries with mandatory data sovereignty requirements such as finance, medical care, and government affairs, Groq's pure cloud model poses a direct obstacle. Even if companies in these industries recognize Groq's speed advantages, they won't be able to adopt it.
3. Insufficient multi-modal capabilities: The current Groq API is mainly oriented to text reasoning. If the application scenario requires multi-modal capabilities such as image understanding, video analysis, and audio processing, developers need to combine multiple services.
4. Ecological dependence: The software ecosystem of LPU is much smaller than that of NVIDIA CUDA, which means that the initiative of model adaptation is not entirely on the side of Groq - if the model publisher uses CUDA-specific optimizations, Groq will require additional engineering investment to port to LPU.
5. Risk of competing products catching up: NVIDIA and cloud vendors (AWS, Google Cloud, Azure) are accelerating investment in inference optimization, and GPU inference efficiency is rapidly improving. While LPU currently maintains a significant lead, the gap may shrink over time.

Procurement/Adoption Risk Assessment: For development teams and small and medium-sized enterprises that require "fast evaluation + low latency + low cost", Groq is the most worthy pilot option in the current open source model inference market. Recommended path: First use the free API Key to complete the PoC and verify that the inference quality and latency indicators meet business needs; after confirming the binding, enter the pay-as-you-go stage and monitor the speed, cost and stability under the production environment; if entering large-scale production and have strict SLA requirements, contact Groq sales to obtain the terms of the enterprise agreement, and also pay attention to the terms in the contract regarding data use, availability compensation and IP ownership - these details are not fully disclosed in Groq's public documents and require business confirmation. For enterprises with data sovereignty requirements, it is recommended to continue to pay attention to Groq's potential privatized deployment or exclusive regional release plan. Before the solution is implemented, Groq may need to be positioned as a supplementary inference path instead of the only inference backend.

Related tools: , replicate

How to use Groq

• Web client: You can use it by visiting the official website and registering an account. Most functions do not require installation.
• API access: Provides RESTful API, developers can obtain the API Key and integrate it into their own applications.

Version Info

• Groq API current version ：It currently supports Llama 4 Scout/Maverick, Qwen3 (multiple specifications), Llama 3.3 70B, Llama 3.1 405B, DeepSeek-R1-Distill and other mainstream open source models. The inference speed continues to lead the industry, and the pay-as-you-go billing is $0.075–$0.60/million tokens (depending on the model). （2026-06-04）
• Groq API public beta launched ：The Groq inference API is open to developers for public testing and supports Llama 2 and Mixtral. The inference speed test reaches 500+ tokens/second, which has attracted widespread attention from the developer community, and API Key applications have been queued up in a short period of time. （2024-02-01）
• Llama 3 support online ：On the day Meta released Llama 3, Groq simultaneously launched Llama 3 inference support, demonstrating its extremely fast speed in following the latest open source models. The inference speed of Llama 3 70B set a historical record at the time. （2024-04-18）
• Llama 4 support online ：Supports Meta Llama 4 Scout and Maverick models, continuing the tradition of rapid follow-up of new models, and the inference speed is significantly ahead of other cloud inference services. （2025-04-01）