LM Studio caching question hits LocalLLaMA

The hot take is that this is less a mysterious Gemma bug than a collision between stateless agent orchestration, limited hardware bandwidth, and incomplete user expectations around KV cache reuse.

• –LM Studio positions itself as a local LLM runner with OpenAI-compatible APIs and stateful chat options, but repeated external chat-completions calls can still force prompt processing if the harness is not reusing a persistent conversation state.
• –LM Studio’s own docs and blog show KV caching support in parts of its stack, especially around its MLX engine, so the bigger question is whether this specific Gemma GGUF runtime path and API workflow actually preserve cache across turns.
• –The user’s hardware setup is doing them no favors: once a 26B model spills beyond 8GB VRAM into DDR4, prompt ingestion becomes memory-bandwidth-bound, so even “only” a few thousand mostly unchanged tokens can stay painfully expensive.
• –This is exactly the sort of workload where smaller models, shorter tool schemas, or explicit stateful chat/session handling can matter more than raw quant size.
• –For developers building local agents, the post is a useful reminder that “context window” and “prompt caching” are not the same thing, and that local OpenAI-compatible servers do not automatically guarantee cloud-style prefix reuse semantics.