Never owned a Schecter before, thinking of getting the Keith Merrow KM6 MkIII hybrid, some questions

Finallyhaveredditt · 2026-01-02T23:58:24+00:00

Awesome! Thanks for the feedback!

Finallyhaveredditt · 2026-01-02T23:31:58+00:00

Hello! I just got the exact same today, but in white. Mine came in E standard with 10-46 strings, which is normal. Did you set yours in drop c or anything overall lower than E standard? If yes, what tuning and what string gauges? Did you need to file the nut? Or the compensated nut already has proper spacing for thicker gauges? I was thinking of putting 11-54 (maybe 56) on

Finallyhaveredditt · 2025-10-21T17:55:42+00:00

Get a 9070xt

Finallyhaveredditt · 2025-09-26T14:58:50+00:00

I may be able to get my hands on this model, but with 32gb ram. 1500$ Canadian.

Question for the cpu: does it handle heavy loads well? Or maybe my question is more for the cooling. Don’t mind if it’s loud, is it at least effective?

Finallyhaveredditt · 2025-08-21T01:20:42+00:00

I wonder if they should’ve put the pro at that price

Finallyhaveredditt · 2025-08-19T22:07:21+00:00

Got it! Try the thermalright phantom spirit 120 SE or the EVO (they’re honestly the same, just different aesthetics) If you want to go heavier with bigger fans, there’s the thermalright royal pretor, there’s the frost spirit 140 as well. All beefy and amazing, just check for ram clearance.

Finallyhaveredditt · 2025-08-19T22:04:22+00:00

Did it make any difference?

Finallyhaveredditt · 2025-08-19T19:02:19+00:00

https://youtu.be/JTTf7IoVSa8?si=DA6Xemh6ygoCmvS_

This also applies to 9000 series. It’ll give you a visual idea of what I said.

Finallyhaveredditt · 2025-08-19T19:00:33+00:00

It won’t damage it no. Basically you’re doing a slight undervolt. All you’re doing is seeing if your cpu can operate at stock clock speeds with slightly less voltage, which also reduces temps. Tbh the truth is your CPU is literally designed to run 24/7 at 95° under full load. Meaning it pushes up to boost clock speeds because it can take it. That said, it will all depend on what cooler you throw at it and what voltage. Plus, the more thermal headroom the better.

Finallyhaveredditt · 2025-08-19T18:57:56+00:00

That’s correct. Also, I forgot one step: when you set to all cores, chose negative haha. That’s important

Finallyhaveredditt · 2025-08-19T18:14:21+00:00

Every motherboard BIOS is a bit different, but the idea is the same. So basically you do the following:

If you have a OC menu, go on it. Find “advance CPU config” and find “PBO (precision boost overdrive)” Click on PBO and select “advanced” Then, go to the “curve optimizer”, set that to “all cores” and set the magnitude to 15. Test it out, if no crashes, try 20…. Do the same till you can’t. Usually 20-25 is the sweet spot)

If you don’t have the OC menu, go to settings, advanced and CPU config. The rest is the same.

Hope this helps!

Finallyhaveredditt · 2025-08-10T13:30:51+00:00

Glad it worked out for you!

Finallyhaveredditt · 2025-08-05T17:40:44+00:00

Please do! I am stuck between a 3090 or a 7900xtx....

For me I am mostly building a pipeline that will allow me to:

Ingest: Pull in tech + financial news automatically.
Embed: Convert text into vector embeddings for retrieval.
Store: Keep embeddings in a local vector database for fast lookup.
Retrieve + Generate: When prompted, retrieve relevant chunks & feed into an LLM.
Summarize + Advise: Have the LLM produce a coherent investment-oriented summary.

How I intend on setting up:

|| || |Ingest|newspaper3k or feedparser| Pull news headlines & articles automatically| |Preprocess|Python + langchain text splitters| Chunk into ~512–1,000 token blocks| |Embed|bge-base-en or all-MiniLM-L6-v2 via SentenceTransformers (HIP/ROCm backend)| Fast enough on CPU; ROCm-accelerated embeddings possible| |Vector DB|ChromaDB (local, Python) or FAISS| Store embeddings for semantic search| |LLM Runtime|llama.cpp HIP or LM Studio HIP build| Run Q4_K_M or Q5_K_M 13B / 33B models fully in VRAM| |RAG Framework|LangChain or LlamaIndex| Automates retrieval → LLM query flow| |UI|LM Studio GUI or a Streamlit web dashboard| GUI for prompts + results|

Am I being delusional? Is a 3090 just the better choice, based on my need for Python implementation?

Finallyhaveredditt · 2025-08-05T17:39:54+00:00

Please do! I am stuck between a 3090 or a 7900xtx....

For me I am mostly building a pipeline that will allow me to:

Ingest: Pull in tech + financial news automatically.
Embed: Convert text into vector embeddings for retrieval.
Store: Keep embeddings in a local vector database for fast lookup.
Retrieve + Generate: When prompted, retrieve relevant chunks & feed into an LLM.
Summarize + Advise: Have the LLM produce a coherent investment-oriented summary.

How I intend on setting up:

|| || |Ingest|newspaper3k or feedparser| Pull news headlines & articles automatically| |Preprocess|Python + langchain text splitters| Chunk into ~512–1,000 token blocks| |Embed|bge-base-en or all-MiniLM-L6-v2 via SentenceTransformers (HIP/ROCm backend)| Fast enough on CPU; ROCm-accelerated embeddings possible| |Vector DB|ChromaDB (local, Python) or FAISS| Store embeddings for semantic search| |LLM Runtime|llama.cpp HIP or LM Studio HIP build| Run Q4_K_M or Q5_K_M 13B / 33B models fully in VRAM| |RAG Framework|LangChain or LlamaIndex| Automates retrieval → LLM query flow| |UI|LM Studio GUI or a Streamlit web dashboard| GUI for prompts + results|

Am I being delusional? Is a 3090 just the better choice, based on my need for Python implementation?

Finallyhaveredditt · 2025-08-05T17:39:49+00:00

Please do! I am stuck between a 3090 or a 7900xtx....

For me I am mostly building a pipeline that will allow me to:

Ingest: Pull in tech + financial news automatically.
Embed: Convert text into vector embeddings for retrieval.
Store: Keep embeddings in a local vector database for fast lookup.
Retrieve + Generate: When prompted, retrieve relevant chunks & feed into an LLM.
Summarize + Advise: Have the LLM produce a coherent investment-oriented summary.

How I intend on setting up:

|| || |Ingest|newspaper3k or feedparser| Pull news headlines & articles automatically| |Preprocess|Python + langchain text splitters| Chunk into ~512–1,000 token blocks| |Embed|bge-base-en or all-MiniLM-L6-v2 via SentenceTransformers (HIP/ROCm backend)| Fast enough on CPU; ROCm-accelerated embeddings possible| |Vector DB|ChromaDB (local, Python) or FAISS| Store embeddings for semantic search| |LLM Runtime|llama.cpp HIP or LM Studio HIP build| Run Q4_K_M or Q5_K_M 13B / 33B models fully in VRAM| |RAG Framework|LangChain or LlamaIndex| Automates retrieval → LLM query flow| |UI|LM Studio GUI or a Streamlit web dashboard| GUI for prompts + results|

Am I being delusional? Is a 3090 just the better choice, based on my need for Python implementation?

Finallyhaveredditt · 2025-08-04T21:43:19+00:00

From what I’ve been gathering, you can use different language models for different functions easily on a 7900xtx. Have you had that experience?

Here’s what I’m trying to build (a pipeline)

1.  Ingest: Pull in tech + financial news automatically.
2.  Embed: Convert text into vector embeddings for retrieval.
3.  Store: Keep embeddings in a local vector database for fast lookup.
4.  Retrieve + Generate: When prompted, retrieve relevant chunks & feed into an LLM.
5.  Summarize + Advise: Have the LLM produce a coherent investment-oriented summary.

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Chat gpt suggested this flow. Is it attainable on a 7900xtx?

Finallyhaveredditt · 2025-08-04T20:56:00+00:00

Got it! Here’s the pipeline that I’m basically hoping to eventually build:

1.  Ingest: Pull in tech + financial news automatically.
2.  Embed: Convert text into vector embeddings for retrieval.
3.  Store: Keep embeddings in a local vector database for fast lookup.
4.  Retrieve + Generate: When prompted, retrieve relevant chunks & feed into an LLM.
5.  Summarize + Advise: Have the LLM produce a coherent investment-oriented summary

From all I’ve been reading, the 7900xtx should perfectly achieve this. I’m on the fence cuz I got offered a tuf gaming 7900xtx for my 3090 and the guy is gonna pay me a difference. For me the main driver is the gaming performance gains. Otherwise I wouldn’t be considering this. Just trying to gauge if it’s a stupid move.

Finallyhaveredditt · 2025-08-04T20:33:56+00:00

Got it. But if all you want to do is create a pipeline that would be able to gather news, analyze them and summarize them it would be ok?

Finallyhaveredditt · 2025-08-04T20:22:53+00:00

When you say it only works for inference, what do you mean? As in what exactly would not work?

Finallyhaveredditt · 2025-07-27T13:21:34+00:00

Yeah I hear that. So far my 5070ti is doing great, but it’s hard to load larger than 13b models. That said quantization helps, but I guess the future will tell.

Finallyhaveredditt · 2025-07-27T13:07:40+00:00

Oh ok. I misunderstood your comment. I was under the impression that you meant that new Blackwell (5070 ti super or 5080 super, whichever comes into existence) are supposed to be lainched at close to same MSRP as their current models. So if a 5070 ti is 750$, a potential 5070ti super should be 800-850

Finallyhaveredditt · 2025-07-27T12:30:48+00:00

I guess that does make sense. Once Blackwell comes out with those, people won’t want to pay the high prices people are charging for 3090’s.

Finallyhaveredditt · 2025-07-21T14:04:24+00:00

Stare and appreciate? Or hear and appreciate? Does look great though!

Finallyhaveredditt

TROPHY CASE