Groq llama 3-70b-8192 chatbot taking so long to respond

[mathabes]

Hi, I'm using the groq llama 3-70b-8192 template component to make a conversational agent. My requests are taking longer and longer, reaching 30 seconds. This only happens when I use the 70b template, because with 8b the requests take a maximum of 4 seconds.

I'm hosting langflow on railway with a Pro plan. Is there any way to solve this problem?

I am sending a high amount of instructions in the conversation, could this be affecting the 70b model?

[dosubot[bot]]

Hey there, @mathabes! I'm here to assist you with any bugs, questions, or contributions. Let's tackle this issue together!

To address the performance issues with the groq llama 3-70b-8192 model, you can try the following optimizations and configurations:

1. Scaling:
   Increase the number of replicas for both the backend and frontend services to handle more requests and distribute the load.

   langflow:
     backend:
       replicaCount: 3
     frontend:
       replicaCount: 3

2. Resource Allocation:
   Allocate more memory and CPU resources to the backend and frontend services to ensure they have enough capacity to handle large models.

   langflow:
     backend:
       resources:
         requests:
           memory: "2Gi"
           cpu: "1000m"
     frontend:
       resources:
         requests:
           memory: "1Gi"
           cpu: "1000m"

3. Storage:
   Use an external PostgreSQL database instead of the default SQLite for better performance and scalability.

   postgresql:
     enabled: true
     fullnameOverride: "langflow-ide-postgresql-service"
     auth:
       username: "langflow"
       password: "langflow-postgres"
       database: "langflow-db"
   
   langflow:
     backend:
       externalDatabase:
         enabled: true
         driver:
           value: "postgresql"
         host:
           value: "langflow-ide-postgresql-service"
         port:
           value: "5432"
         database:
           value: "langflow-db"
         user:
           value: "langflow"
         password:
           valueFrom:
             secretKeyRef:
               key: "password"
               name: "langflow-ide-postgresql-service"
       sqlite:
         enabled: false

4. Model Configuration:
   Configure the model settings to optimize performance. For example, if you are using the Ollama model, you can adjust parameters such as num_gpu, num_thread, mirostat, and temperature to better suit your hardware and performance needs.

   def build_config(self) -> dict:
       return {
           "num_gpu": {
               "display_name": "Number of GPUs",
               "field_type": "int",
               "info": "Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)",
               "value": 1,
           },
           "num_thread": {
               "display_name": "Number of Threads",
               "field_type": "int",
               "info": "Number of threads to use during computation. (Default: detected for optimal performance)",
               "value": 8,
           },
           "mirostat": {
               "display_name": "Mirostat",
               "options": ["Disabled", "Mirostat", "Mirostat 2.0"],
               "info": "Enable/disable Mirostat sampling for controlling perplexity.",
               "value": "Mirostat 2.0",
           },
           "temperature": {
               "display_name": "Temperature",
               "field_type": "float",
               "value": 0.7,
               "info": "Controls the creativity of model responses.",
           },
       }

These configurations will help ensure that Langflow can efficiently handle large models by scaling horizontally and vertically, using a more robust database system, and optimizing model-specific settings ^[1][2].

To continue talking to Dosu, mention @dosu.