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consumer accelerators wor...
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structured output failure...
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CUDA update broke my thro...
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70B still worth the pain
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context size vs speed in ...
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do small edge boxes make ...
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8B vs 14B sweet spot: any...
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| when a plain Python stack is enough |
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Posted by: routerunner - 10-11-2026, 01:35 AM - Forum: PyTorch / JAX / Other Frameworks
- Replies (14)
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Spent some time on this today and wanted to compare notes.
I've gone back and forth between vLLM and TensorRT-LLM for Gemma. The interesting difference for me has been batch size.
What would you pick if you had to live with it for a few months?
For context, I'm not chasing leaderboard numbers. I mostly want the system to feel consistent day to day. At this point the real bottleneck is my patience.
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| Help with Embeddings for Phi |
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Posted by: vectorshed43 - 10-11-2026, 01:32 AM - Forum: Embeddings
- Replies (9)
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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 LM Studio and 7900 XTX.
Would rather learn from your scars than collect my own all at once.
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| draft model choices for Kimi |
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Posted by: cacheline - 10-11-2026, 01:29 AM - Forum: Context / KV Cache
- Replies (1)
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Spent some time on this today and wanted to compare notes.
Two paths both seem viable, but they break down differently once tool calling becomes important.
Trying to avoid rebuilding the whole thing twice.
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| serving on Blackwell on dual 3090 |
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Posted by: kernelgrid - 10-11-2026, 12:09 AM - Forum: TensorRT-LLM
- Replies (2)
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Wanted to compare notes with people doing similar things.
I'm trying to decide whether to keep the current stack or change course. Main question is how people here are handling quant drift.
If you've tested something similar, what did you keep and what did you stop bothering with?
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| how strict do you make schemas |
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Posted by: plainworks - 10-11-2026, 12:09 AM - Forum: Structured Output
- Replies (10)
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Thought I'd post a small recipe because a lot of threads ask similar things.
This isn't revolutionary, but the combination of MLX and Threadripper box felt surprisingly usable once I tuned around MoE serving.
Maybe useful as a starting point for newer folks. The logs were somehow calmer than I was.
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| small speech stack for local use |
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Posted by: kernelpeak - 10-11-2026, 12:06 AM - Forum: Image
- Replies (2)
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Did a bit of testing and thought I'd post the result here.
I tried this with DeepSeek using Ollama on M3 Max. The headline result was better than I expected, but long context still ended up being the limiting factor.
Happy to post more details if anyone wants them.
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| tool calling reliability: setup advice |
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Posted by: turbosupply83 - 10-11-2026, 12:05 AM - Forum: Embedding / Reranking Models
- Replies (2)
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Spent some time on this today and wanted to compare notes.
Two paths both seem viable, but they break down differently once quant drift becomes important.
Trying to avoid rebuilding the whole thing twice.
My use case is pretty ordinary: local chat, some document work, and occasional batch jobs.
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| PyTorch compile got faster? |
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Posted by: cacheworks - 10-10-2026, 11:58 PM - Forum: PyTorch / JAX / Other Frameworks
- No Replies
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This keeps coming up in conversation so I figured I'd start a thread.
One thing I keep noticing is that people underestimate MoE serving until they actually try to run this stuff for more than one afternoon.
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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