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  how much orchestration is too much: early impressions
Posted by: llm_wren - 10-04-2026, 08:30 PM - Forum: Fine-Tuning / LoRA - Replies (6)

Spent some time on this today and wanted to compare notes.

I've gone back and forth between Ollama and Ollama for Kimi. The interesting difference for me has been router policy.

What would you pick if you had to live with it for a few months?

My use case is pretty ordinary: local chat, some document work, and occasional batch jobs.

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  benchmarks that map to real use for Kimi
Posted by: vectorcrate98 - 10-04-2026, 07:44 PM - Forum: Evals - Replies (1)

I figured I'd ask here before changing everything.

I'm trying to decide whether to keep the current stack or change course. Main question is how people here are handling prefix caching.

Would appreciate practical advice rather than theory.

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  best local runtime for mixed models with Ollama
Posted by: fusedhall - 10-04-2026, 07:02 PM - Forum: Ollama / LM Studio / Desktop Runtimes - Replies (9)

Spent some time on this today and wanted to compare notes.

Two paths both seem viable, but they break down differently once token cost becomes important.

Interested in tradeoffs more than fan loyalty.

I care more about stability than squeezing the last possible tokens/sec out of it.

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  driver/framework news roundup
Posted by: deepseeked - 10-04-2026, 05:18 PM - Forum: News & Industry - Replies (6)

Spent some time on this today and wanted to compare notes.

Two paths both seem viable, but they break down differently once continuous batching becomes important.

Trying to avoid rebuilding the whole thing twice.

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  RAG recall vs speed tradeoff for Phi
Posted by: latentgarden - 10-04-2026, 04:51 PM - Forum: Distillation - Replies (11)

Thought I'd post a small recipe because a lot of threads ask similar things.

Just sharing a setup that ended up working reasonably well: Kimi, Ollama, mini PC, with a lot of attention on workstation thermals.

Maybe useful as a starting point for newer folks.

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  TensorRT-LLM worth the setup time: early impressions
Posted by: kernelmike - 10-04-2026, 04:18 PM - Forum: TensorRT-LLM - Replies (9)

I'm trying to sanity-check my setup.

I'm trying to decide whether to keep the current stack or change course. Main question is how people here are handling HBM constraints.

Not looking for the perfect answer, mostly what worked in real use.

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  AWQ vs GPTQ vs GGUF right now on A6000
Posted by: paperrunner - 10-04-2026, 04:12 PM - Forum: Quantization - Replies (15)

Posting this in case it saves someone else a little time.

I'm not trying to build a perfect architecture diagram. I just want a sane starting point around llama.cpp and dual 3090.

I'm especially interested in mistakes worth avoiding.

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  frankenmerges anyone kept using with MLX
Posted by: rocmpath - 10-04-2026, 04:07 PM - Forum: MCP / APIs / Tool Calling - Replies (6)

Posting this in case it saves someone else a little time.

If you were starting from scratch today for this use case, what would you choose first and what would you ignore?

Simple and boring is a valid answer.

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  Help with Embeddings on B200
Posted by: ggufwire22 - 10-04-2026, 03:52 PM - Forum: Embeddings - Replies (8)

This keeps coming up in conversation so I figured I'd start a thread.

It feels like the conversation has shifted hard toward inference and serving details lately, and honestly that makes sense once you start dealing with tool calling.

Wondering if this is just where the whole space is heading now.

This is mostly for local work rather than public serving.

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  how strict do you make schemas
Posted by: paperrunner - 10-04-2026, 02:17 PM - Forum: Structured Output - No Replies

Curious what people here are seeing.

My current setup is vLLM with 5090, and I keep running into embeddings.

If you've tested something similar, what did you keep and what did you stop bothering with?

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