PsychoticSheepMy OpenCode experience: From local LLM to free cloud
I tried OpenCode because I wanted an AI agent for coding.
One of the cool things about an agent is that it automatically checks if all dependencies are installed. No more "module not found" stress.
My journey:
- I started with Qwen2.5 Coder 14B. Tool calling does not work, so it is useless for an agent framework.
- Then I attempted local Qwen3:14B. The problem here is the context window. Tool calling requires num_ctx to be increased to 16384. At that size, the model no longer fits into VRAM and gets partially offloaded to the CPU, which kills performance.
- Next I tried local Qwen3:8b. It works fine for simple Nginx configs and static HTML/CSS. But with JavaScript, it struggles without very precise instructions.
- I also looked at Qwen3 Coder. Even the smallest model has 30B parameters, which is too heavy for my PC.
Then DeepSeek V4 Flash Free changed everything
This is a full cloud model, completely free. No API key, no credit card, no subscription.
About the limits: According to my research, the community proxy services behind OpenCode allow about 50 to 200 million tokens per day, which is more than enough. The counter resets daily. The context is around 256k instead of 1M, but that is still plenty for coding.
Local models are nice, but let's be honest, running 30B+ models on normal hardware is still a pain. Until that changes, DeepSeek V4 Flash Free in OpenCode is the perfect bridge solution. It just works. It is fast, simple, and costs nothing. Highly recommended!
#OpenCode #DeepSeekV4 #FreeAI #AIAgent #Coding #LocalLLM #VRAM #Qwen3 #LLM #DevTools #OpenSourceAI #AICoding #Fediverse #Programming
I tried OpenCode because I wanted an AI agent for coding.
One of the cool things about an agent is that it automatically checks if all dependencies are installed. No more "module not found" stress.
My journey:
- I started with Qwen2.5 Coder 14B. Tool calling does not work, so it is useless for an agent framework.
- Then I attempted local Qwen3:14B. The problem here is the context window. Tool calling requires num_ctx to be increased to 16384. At that size, the model no longer fits into VRAM and gets partially offloaded to the CPU, which kills performance.
- Next I tried local Qwen3:8b. It works fine for simple Nginx configs and static HTML/CSS. But with JavaScript, it struggles without very precise instructions.
- I also looked at Qwen3 Coder. Even the smallest model has 30B parameters, which is too heavy for my PC.
Then DeepSeek V4 Flash Free changed everything
This is a full cloud model, completely free. No API key, no credit card, no subscription.
About the limits: According to my research, the community proxy services behind OpenCode allow about 50 to 200 million tokens per day, which is more than enough. The counter resets daily. The context is around 256k instead of 1M, but that is still plenty for coding.
Local models are nice, but let's be honest, running 30B+ models on normal hardware is still a pain. Until that changes, DeepSeek V4 Flash Free in OpenCode is the perfect bridge solution. It just works. It is fast, simple, and costs nothing. Highly recommended!
#OpenCode #DeepSeekV4 #FreeAI #AIAgent #Coding #LocalLLM #VRAM #Qwen3 #LLM #DevTools #OpenSourceAI #AICoding #Fediverse #Programming
Dr. Bruce Simpson
Zachary Cutlip
Sean 
