Performance: High latency and slow response times make the tool difficult to use

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. Summary Llama Coder is experiencing significant performance issues, resulting in very slow code generation that disrupts the development workflow. Tasks that should take seconds are often taking several minutes, making interactive use of the tool impractical.

2. Steps to Reproduce Launch or connect to the Llama Coder instance.

Provide a moderately complex prompt for code generation. For example:

[Paste the prompt you used here. For example: "Create a Python script using the Django Ninja framework for a simple API with endpoints for creating and listing products. The Product model should have 'name', 'description', 'price', and 'created_at' fields."] Initiate the code generation process.

Observe the time taken for the complete response to be generated.

3. Expected Behavior For a prompt of this complexity, I would expect the code to be generated within a reasonable timeframe, perhaps 30-90 seconds at most, allowing for a fluid, iterative coding process.

4. Actual Behavior The code generation process takes an unexpectedly long time, often [mention how long it took, e.g., 5-10 minutes]. During this period, there is little to no feedback on the progress, and the tool can appear to be frozen. This long delay makes it much less efficient than manual coding or using alternative tools.

5. Environment Details To help diagnose the issue, here is my setup:

Tool Version: [e.g., Llama Coder v0.8, or specify if you're using it via an API]

Operating System: [e.g., Windows 11, macOS Sonoma, Ubuntu 22.04]

Hardware:

CPU: [e.g., Apple M3 Pro, Intel Core i9-13900K]

RAM: [e.g., 16 GB, 32 GB]

GPU (if applicable): [e.g., NVIDIA RTX 4080, or N/A]

Connection: [e.g., Running locally, or specify internet speed if using a web service, e.g., 200 Mbps Fibre]

6. Additional Context This performance issue seems to be consistent across different types of prompts, although it gets noticeably worse with more complex requests. The core problem is that the latency is high enough to break concentration and flow, which is the primary benefit a tool like this should provide.

Thank you for looking into this!

[meet306]

Hi team,

I’ve noticed the high latency and slow response times in Llama Coder make interactive use difficult. Here’s a structured proposal to address this:

1. Streaming Output

Stream generated code tokens/results to the user as they are produced.

Reduces perceived latency and allows users to start reviewing/editing immediately.

Implementation: Use WebSockets or Server-Sent Events (SSE) for real-time updates.

2. Model Optimization

Use smaller, task-specific models for common frameworks.

Quantize the model (FP16 or INT8) to speed up inference.

Preload frequently used modules/templates in memory.

3. Hardware Scaling

Allocate sufficient CPU/GPU resources for heavy generation tasks.

Consider cloud autoscaling for multiple users.

4. Performance Profiling

Measure time for token generation, preprocessing, and I/O.

Optimize the slowest pipeline steps (caching, parallelization, etc.).

5. Caching Frequent Prompts

Cache outputs for repetitive prompts to reduce recomputation.

Immediate response for previously executed tasks.

Expected Outcome:

Moderate prompts should generate code in 30–90 seconds.

Users can see code progressively while it’s being generated.

Reduced frustration and improved workflow efficiency.

I can help with implementation details for any of these if needed.

Thank you, Meet Patel