Update providers/groq.mdx

Co-Authored-By: mintlify[bot] <109931778+mintlify[bot]@users.noreply.github.com>

Author: Gerome-Elassaad

providers/groq.mdx

@@ -1,80 +1,45 @@
 ---
 title: "Groq"
-description: "Learn how to configure and use Groq's lightning-fast inference to access models from OpenAI, Meta, DeepSeek, and more with Groq."
+description: "Configure Groq's ultra-fast LPU inference for models from OpenAI, Meta, and DeepSeek."
 ---
 
-Groq provides ultra-fast AI inference through their custom LPU™ (Language Processing Unit) architecture, purpose-built for inference rather than adapted from training hardware. Groq hosts open-source models from various providers including OpenAI, Meta, DeepSeek, Moonshot AI, and others.
+Groq provides ultra-fast AI inference through custom LPU™ (Language Processing Unit) architecture. Hosts open-source models from OpenAI, Meta, DeepSeek, and others.
 
 **Website:** [https://groq.com/](https://groq.com/)
 
-### Getting an API Key
+## Getting an API Key
 
-1.  **Sign Up/Sign In:** Go to [Groq](https://groq.com/) and create an account or sign in.
-2.  **Navigate to Console:** Go to the [Groq Console](https://console.groq.com/) to access your dashboard.
-3.  **Create a Key:** Navigate to the API Keys section and create a new API key. Give your key a descriptive name (e.g., "CodinIT").
-4.  **Copy the Key:** Copy the API key immediately. You will not be able to see it again. Store it securely.
+1. Go to [Groq Console](https://console.groq.com/) and sign in
+2. Navigate to API Keys section
+3. Create a new API key and name it (e.g., "CodinIT")
+4. Copy the key immediately - you won't see it again
 
-### Supported Models
+## Configuration
 
-CodinIT supports the following Groq models:
+1. Click the settings icon (⚙️) in CodinIT
+2. Select "Groq" as the API Provider
+3. Paste your API key
+4. Choose your model
 
--   `llama-3.3-70b-versatile` (Meta) - Balanced performance with 131K context
--   `llama-3.1-8b-instant` (Meta) - Fast inference with 131K context  
--   `openai/gpt-oss-120b` (OpenAI) - Featured flagship model with 131K context
--   `openai/gpt-oss-20b` (OpenAI) - Featured compact model with 131K context
--   `moonshotai/kimi-k2-instruct` (Moonshot AI) - 1 trillion parameter model with prompt caching
--   `deepseek-r1-distill-llama-70b` (DeepSeek/Meta) - Reasoning-optimized model
--   `qwen/qwen3-32b` (Alibaba Cloud) - Enhanced for Q&A tasks
--   `meta-llama/llama-4-maverick-17b-128e-instruct` (Meta) - Latest Llama 4 variant
--   `meta-llama/llama-4-scout-17b-16e-instruct` (Meta) - Latest Llama 4 variant
+## Supported Models
 
-### Configuration in CodinIT
+- `llama-3.3-70b-versatile` (Meta) - 131K context
+- `openai/gpt-oss-120b` (OpenAI) - 131K context
+- `moonshotai/kimi-k2-instruct` - 1T parameters with caching
+- `deepseek-r1-distill-llama-70b` - Reasoning optimized
+- `qwen/qwen3-32b` (Alibaba) - Q&A enhanced
+- `meta-llama/llama-4-maverick-17b-128e-instruct`
 
-1.  **Open CodinIT Settings:** Click the settings icon (⚙️) in the CodinIT panel.
-2.  **Select Provider:** Choose "Groq" from the "API Provider" dropdown.
-3.  **Enter API Key:** Paste your Groq API key into the "Groq API Key" field.
-4.  **Select Model:** Choose your desired model from the "Model" dropdown.
+## Key Features
 
-### Groq's Speed Revolution
+- **Ultra-fast inference:** Sub-millisecond latency with LPU architecture
+- **Large context:** Up to 131K tokens
+- **Prompt caching:** Available on select models
+- **Vision support:** Available on select models
 
-Groq's LPU architecture delivers several key advantages over traditional GPU-based inference:
+Learn more about [LPU architecture](https://groq.com/blog/inside-the-lpu-deconstructing-groq-speed).
 
-#### LPU Architecture
-Unlike GPUs that are adapted from training workloads, Groq's LPU is purpose-built for inference. This eliminates architectural bottlenecks that create latency in traditional systems.
+## Notes
 
-#### Unmatched Speed
-- **Sub-millisecond latency** that stays consistent across traffic, regions, and workloads
-- **Static scheduling** with pre-computed execution graphs eliminates runtime coordination delays
-- **Tensor parallelism** optimized for low-latency single responses rather than high-throughput batching
-
-#### Quality Without Tradeoffs
-- **TruePoint numerics** reduce precision only in areas that don't affect accuracy
-- **100-bit intermediate accumulation** ensures lossless computation
-- **Strategic precision control** maintains quality while achieving 2-4× speedup over BF16
-
-#### Memory Architecture
-- **SRAM as primary storage** (not cache) with hundreds of megabytes on-chip
-- **Eliminates DRAM/HBM latency** that plagues traditional accelerators
-- **Enables true tensor parallelism** by splitting layers across multiple chips
-
-Learn more about Groq's technology in their [LPU architecture blog post](https://groq.com/blog/inside-the-lpu-deconstructing-groq-speed).
-
-### Special Features
-
-#### Prompt Caching
-The Kimi K2 model supports prompt caching, which can significantly reduce costs and latency for repeated prompts.
-
-#### Vision Support
-Select models support image inputs and vision capabilities. Check the model details in the Groq Console for specific capabilities.
-
-#### Reasoning Models
-Some models like DeepSeek variants offer enhanced reasoning capabilities with step-by-step thought processes.
-
-### Tips and Notes
-
--   **Model Selection:** Choose models based on your specific use case and performance requirements.
--   **Speed Advantage:** Groq excels at single-request latency rather than high-throughput batch processing.
--   **OSS Model Provider:** Groq hosts open-source models from multiple providers (OpenAI, Meta, DeepSeek, etc.) on their fast infrastructure.
--   **Context Windows:** Most models offer large context windows (up to 131K tokens) for including substantial code and context.
--   **Pricing:** Groq offers competitive pricing with their speed advantages. Check the [Groq Pricing](https://groq.com/pricing) page for current rates.
--   **Rate Limits:** Groq has generous rate limits, but check their documentation for current limits based on your usage tier.
+- **Speed:** Optimized for single-request latency
+- **Pricing:** See [Groq Pricing](https://groq.com/pricing)