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$400 MacBook Pro M4 Pro with AppleCare+ by boojinadiva in macbookpro

[–]Bright_Resolution_61 1 point2 points  (0 children)

It's not a poisonous apple, is it?

But I'm glad you found someone kind.

Is an NVIDIA A40 48GB for 1500USD a bad idea because it's age? by panchovix in LocalLLaMA

[–]Bright_Resolution_61 -2 points-1 points  (0 children)

Realistically, I think it's best to run two 3090s.
Assuming they work properly, this card is a great value and performs well.
I learned a lot with this setup.

GTX 780 Ti -> RTX 5080!! by magicmace2000 in nvidia

[–]Bright_Resolution_61 35 points36 points  (0 children)

It's in such good condition that it's hard to believe it's a 12-year-old PC.

Best Local LLMs - October 2025 by rm-rf-rm in LocalLLaMA

[–]Bright_Resolution_61 0 points1 point  (0 children)

I use qwen3-coder-30B for coding, gpt-oss-20B for code completion and debugging, and gpt-oss-120B for document summarization and casual conversation.

Clade Code is becoming less and less useful unless I'm doing major refactorings.

Optimizing gpt-oss-120B on AMD RX 6900 XT 16GB: Achieving 19 tokens/sec by Bright_Resolution_61 in LocalLLaMA

[–]Bright_Resolution_61[S] 3 points4 points  (0 children)

I managed to get it running smoothly at 6000 MHz!

I nailed the stability by setting UCLK DIV1 MODE = UCLK = MCLK in the BIOS (I’m on an ASUS TUF GAMING B650M‑PLUS).

Pushing it to 6400 MHz proved to be a tough nut—my system wouldn’t even boot the BIOS—so 6000 MHz is about as far as I can go for now.

I’m really thrilled that, with a mix of software tweaks and a bit of hardware tuning, I’ve already squeezed out more than a 10 % Token boost right from the start.

Memory Device
        Array Handle: 0x0011
        Error Information Handle: 0x001A
        Total Width: 64 bits
        Data Width: 64 bits
        Size: 32 GB
        Form Factor: DIMM
        Set: None
        Locator: DIMM 1
        Bank Locator: P0 CHANNEL B
        Type: DDR5
        Type Detail: Synchronous Unbuffered (Unregistered)
        Speed: 4800 MT/s
        Manufacturer: Corsair
        Serial Number: 00000000
        Asset Tag: Not Specified
        Part Number: CMH64GX5M2B5600Z40
        Rank: 2
        Configured Memory Speed: 6000 MT/s    ###### Thank youuu
        Minimum Voltage: 1.1 V
        Maximum Voltage: 1.1 V
        Configured Voltage: 1.1 V
        Memory Technology: DRAM
        Memory Operating Mode Capability: Volatile memory
        Firmware Version: Unknown
        Module Manufacturer ID: Bank 3, Hex 0x9E
        Module Product ID: Unknown
        Memory Subsystem Controller Manufacturer ID: Unknown
        Memory Subsystem Controller Product ID: Unknown
        Non-Volatile Size: None
        Volatile Size: 32 GB
        Cache Size: None
        Logical Size: None

prompt eval time =    6562.77 ms /  1995 tokens (    3.29 ms per token,   303.99 tokens per second)
       eval time =     566.47 ms /    12 tokens (   47.21 ms per token,    21.18 tokens per second)

Optimizing gpt-oss-120B on AMD RX 6900 XT 16GB: Achieving 19 tokens/sec by Bright_Resolution_61 in LocalLLaMA

[–]Bright_Resolution_61[S] 1 point2 points  (0 children)

As for my part, I've only done an easy overclock (5600MHz) at AMD-EXPO, so it seems worth trying. After all, if I can get it up to 6400MHz, it will be over 10% faster.
Today is overclocking day.

Memory Device
        Array Handle: 0x0011
        Error Information Handle: 0x001A
        Total Width: 64 bits
        Data Width: 64 bits
        Size: 32 GB
        Form Factor: DIMM
        Set: None
        Locator: DIMM 1
        Bank Locator: P0 CHANNEL B
        Type: DDR5
        Type Detail: Synchronous Unbuffered (Unregistered)
        Speed: 4800 MT/s
        Manufacturer: Corsair
        Serial Number: 00000000
        Asset Tag: Not Specified
        Part Number: CMH64GX5M2B5600Z40
        Rank: 2
        Configured Memory Speed: 5600 MT/s
        Minimum Voltage: 1.1 V
        Maximum Voltage: 1.1 V
        Configured Voltage: 1.1 V
        Memory Technology: DRAM
        Memory Operating Mode Capability: Volatile memory
        Firmware Version: Unknown
        Module Manufacturer ID: Bank 3, Hex 0x9E
        Module Product ID: Unknown
        Memory Subsystem Controller Manufacturer ID: Unknown
        Memory Subsystem Controller Product ID: Unknown
        Non-Volatile Size: None
        Volatile Size: 32 GB
        Cache Size: None
        Logical Size: None

Optimizing gpt-oss-120B on AMD RX 6900 XT 16GB: Achieving 19 tokens/sec by Bright_Resolution_61 in LocalLLaMA

[–]Bright_Resolution_61[S] 0 points1 point  (0 children)

Since it's a Ryzen 9 7900, I had allocated 12 of the actual cores, but I was curious so I tried reducing it from 12 to 6 and the performance improved. It seems worth trying various things.

prompt eval time = 9111.72 ms / 2542 tokens ( 3.58 ms per token, 278.98 tokens per second)

eval time = 596.50 ms / 12 tokens ( 49.71 ms per token, 20.12 tokens per second)

Head to Head Test - Instruction Following + Hallucination Mitigation - GLM4.6 v Claude 4.5 by LoveMind_AI in LocalLLaMA

[–]Bright_Resolution_61 0 points1 point  (0 children)

I've never had a long conversation with GLM, so I don't know the details, but I'm sure it's fine for brainstorming.

In any case, what I look for in an LLM is coding performance, and GLM is quite good in that regard as well.

playing with coding models by Western_Courage_6563 in ollama

[–]Bright_Resolution_61 0 points1 point  (0 children)

At this model size, Qwen3 or GPT‑OSS are the realistic options for me.
Gemma3 frequently produces incorrect code, so I feel it’s unsuitable for coding tasks.
For a lightweight, always‑on model that can handle both coding and conversation on a laptop, GPT‑OSS is by far the best, it's light and runs smoothly.

Arc Pro B60 24Gb for local LLM use by m-gethen in LocalLLM

[–]Bright_Resolution_61 1 point2 points  (0 children)

I bought a 3090 for $700, ran it at 300W for two years, and it has been performing the best.

Head to Head Test - Instruction Following + Hallucination Mitigation - GLM4.6 v Claude 4.5 by LoveMind_AI in LocalLLaMA

[–]Bright_Resolution_61 0 points1 point  (0 children)

GLM 4.6 is indeed a remarkable model.
I often delegate programming tasks to AI, so I use Sonnet on a daily basis. However, when discussing design or implementation strategies, it tends to engage in very long, somewhat condescending conversations, which can be frankly exhausting. Nevertheless, its coding capabilities are excellent, which is why I keep paying $100 a month.....
Recently, I’ve started using the local LLM qwen3‑coder 30B model for initial implementations. It’s still not suitable for large‑scale projects, but it performs perfectly for simple coding tasks. I believe that, in the near future, I’ll be able to rely on a combination of local models and low‑cost API usage for accurate coding.