DeepSeek with Ollama and LangChain produces slow responses and garbled output

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1

I’m having trouble with my setup using LangChain together with DeepSeek model through Ollama. When I try to run queries, the response time is extremely slow and sometimes I get completely random text output that doesn’t make sense.

Has anyone experienced similar issues with this combination? The model seems to work fine when I use it directly with Ollama, but adding LangChain into the mix creates these problems. I’m wondering if there are specific configuration settings I need to adjust or if this is a known compatibility issue.

Any suggestions on how to optimize the performance or fix the garbled responses would be really helpful. Thanks in advance!

2

Memory allocation’s probably your bottleneck. I hit the same performance issues when my system tried loading the entire model into RAM while LangChain managed its own memory overhead. Lower your num_gpu parameter in Ollama and reduce LangChain’s batch size - that should help. For the garbled output, check if you’re using streaming responses. I disabled streaming and switched to regular completion calls, which fixed my text corruption. Also check your temperature and top_p settings - LangChain sometimes overrides these with defaults that mess up output quality.

3

i had the same problem too! downgrading langchain helped fix that garbled stuff for me. also, make sure your ollama context window ain’t set too high, that can slow things down a lot.

4

check your model quantization - q4_0 or q8_0 can mess up deepseek’s outputs. switch to fp16 if you’ve got the vram. fixed the same issue for me when i was running it through langchain.

5

Been there with local model setups. You’re dealing with multiple moving parts that never want to cooperate.

I ditched the Ollama + LangChain compatibility nightmare and moved my DeepSeek stuff to a proper automation platform. No more version conflicts or memory issues.

Now I just use API endpoints that handle everything. Response times stay consistent, no more garbled outputs, and I can chain model calls without worrying about context windows or memory leaks.

You can build the same reliable setup without the local infrastructure headaches. Check out Latenode for proper model integrations: https://latenode.com