10 open-source repos to cut GPT-6 Astra compute costs
A list of ten open-source tools covering routing, caching, inference acceleration, quantization and usage tracing to lower the cost of running GPT-6 Astra.
AI agents are getting f...cking illegal 10 open-source GitHub repos for making GPT 6 Astra spend less compute without making the system dumber 01 LiteLLM ▸ https://github.com/BerriAI/litellm → route requests across models through one gateway ROUTE THE EASY WORK 02 RouteLLM ▸ https://github.com/lm-sys/RouteLLM → send simple prompts to cheaper models 03 Martian ▸ https://github.com/withmartian/routerbench → benchmark which routing strategies actually work CACHE WHAT YOU ALREADY PAID FOR 04 GPTCache ▸ https://github.com/zilliztech/GPTCache → reuse previous LLM responses 05 LMCache ▸ https://github.com/LMCache/LMCache → reuse KV cache across inference requests MAKE INFERENCE FASTER 06 SGLang ▸ https://github.com/sgl-project/sglang → high performance serving for LLM workloads 07 vLLM ▸ https://github.com/vllm-project/vllm → high throughput model serving RUN SMALLER WHEN YOU CAN 08 llama.cpp ▸ https://github.com/ggml-org/llama.cpp → run quantized models locally 09 bitsandbytes ▸ https://github.com/bitsandbytes-foundation/bitsandbytes → lower precision inference and training WATCH THE BILL 10 OpenLLMetry ▸ https://github.com/traceloop/openllmetry → trace LLM calls, latency, and usage the efficiency loop: request → classify → route → cache → infer → measure → optimize the expensive architecture sends every problem to the smartest model the better one knows when GPT 6 Astra is actually needed more intelligence ⭣
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