u/ankijain21

I’m working on a solution for a couple of clients. The goal is to provide a hybrid infrastructure for dev teams (5-7 devs) that eliminates 'token anxiety'.

The Tech Stack:

• Hardware: NVIDIA DGX Spark (or equivalent GB10 Grace Blackwell).
• Local LLM: Qwen 3.6-27B (as it is hitting ~77.2% on SWE-bench, parity with Sonnet for coding tasks).
• The Router: A LiteLLM layer serving an OpenAI-compatible endpoint.
• The Logic: IDE plugins (Claude Code/VS Code) point to the local LiteLLM endpoint. The router decides: if the task is routine coding or document analysis, it stays on-prem. If it’s a high-complexity agentic task, it overflows to the Claude API automaticall

We’re aiming for ~80% of queries to be served locally at zero token cost.

The questions I have -

1. How much overhead does LiteLLM add when deciding between local vs. API? Is there a better lightweight orchestrator for this?
2. In a production environment, how often does Qwen 27B actually fail where Claude 4.6 succeeds for routine refactoring?
3. When overflowing to Claude, how do you efficiently pass the context that was already partially processed locally without doubling the latency?

I am pricing this as an all-inclusive $10,000 one-time cost to replace recurring cloud bills. Is the hardware-software-support bundle actually viable with a 6-month support window?

I’m working on a solution for a couple of clients. The goal is to provide a hybrid infrastructure for dev teams (5-7 devs) that eliminates 'token anxiety'.

The Tech Stack:

• Hardware: NVIDIA DGX Spark (or equivalent GB10 Grace Blackwell).
• Local LLM: Qwen 3.6-27B (as it is hitting ~77.2% on SWE-bench, parity with Sonnet for coding tasks).
• The Router: A LiteLLM layer serving an OpenAI-compatible endpoint.
• The Logic: IDE plugins (Claude Code/VS Code) point to the local LiteLLM endpoint. The router decides: if the task is routine coding or document analysis, it stays on-prem. If it’s a high-complexity agentic task, it overflows to the Claude API automaticall

We’re aiming for ~80% of queries to be served locally at zero token cost.

The questions I have -

1. How much overhead does LiteLLM add when deciding between local vs. API? Is there a better lightweight orchestrator for this?
2. In a production environment, how often does Qwen 27B actually fail where Claude 4.6 succeeds for routine refactoring?
3. When overflowing to Claude, how do you efficiently pass the context that was already partially processed locally without doubling the latency?

I am pricing this as an all-inclusive $10,000 one-time cost to replace recurring cloud bills. Is the hardware-software-support bundle actually viable with a 6-month support window?

I had been trying hard to set up openclaw using my local LLM but it was constantly giving errors.

Even it cooked my hard drive completely.

So I ended up installing a new hard drive, reinstalled ubuntu and installed qwen2.5-7b and connected hrmes instead. Seems to be working fine for now.