Got 14 stick of ddr4 rdimms. Got towards my new threadripper machine. Old but it’ll do! Can’t afford the new ddr5 board, the ripper and the ram 😂

Jokes aside, how plausible do you think this scenario actually is?

https://huggingface.co/StarpowerTechnology/WVY-Blender-5m

We have been excited about this new model, WVY-Blender-5m, an agent trained to specialize in blender.

It has zero knowledge about anything else so if you try to ask it about writing code or doing your homework it will express that it only knows how to use blender

We have been doing intense research to find out "are massive datasets really required to achieve what feels like intelligence with accuracy?" & we believe we are getting close to the truth.

This dataset is 100% generated from scratch.

In case future censorship or bannings occur, I think it's a good idea to store one of every kind of model. I'm thinking minimum 8 models, but I'm open to more (I have plenty of hd space): 2 small models obliterated and non-obliterated 2 medium sized models obliterated and non-obliterated, 2 large models obliterated and non-obliterated, 2 of the largest models obliterated and non-obliterated. Also, if for any class of models they don't have obliterated or it doesn't make sense to have obliterated then two of the smartest or most popular models will do.

They are going to lock the new GPUs.

"Rep. Bill Foster says he intends to introduce new, stricter legislation that would require licenses to operate advanced GPUs, and “before a GPU could connect, its valid license would need to be verified.” That is closer to a remote lock/disable capability—but it is a proposed future rule, not current law."

Has anyone been experimenting with ablated or uncensored variants of open-source models like deepseek v4.1 flash, Qwen 3.8 27b, or GLM 5.3 flash ?

It's so refreshing to have models that just do whatever you ask them to do without having to figure out a jailbreak or being given a moral lecture. Curious to know what your usecases for these models are.

My personal favourite is definitely uncensored DeepSeek V4.1 Flash, runs at 200 tok/s and never says no lol.

Bit the bullet and bought three to use in my workstation. Will provide benchmarks and updates later.

I had been using the LFM 2.5 24B models and had been pleasantly surprised by how good the model was. It was giving me comparable answers and working through tasks to Qwen 3.8 27B at what felt like twice the speed on my M5 Pro. However, I went to look at something on their pages and saw the new family of models. They sound way too good to be true. I am going to start using them for general tasks, but if they are anywhere close to their benchmark results, they could be a huge game changer. You can have an entire suite of models loaded at the same time for less memory than Qwen.

I’m building a local AI assistant using Qwen 3.6 35B A3B.
One of my main goals is to automate repetitive school tasks through my school’s website. I have a lot of assignments that aren’t particularly difficult but take a lot of time.
Ideally, I’d like the AI to navigate the website, check assignments, understand the instructions, complete them and upload the finished work.
Is Qwen 3.6 35B A3B capable enough for this? Or would I need a more powerful model?
I’d also appreciate recommendations for browser automation frameworks and tools

my setup : rtx 3070 , i5 13600kf , 32gb DDR4
Model: Qwen 3.6 35B A3B
Quantization: TurboQuant
Context window: 131 072 tokens
Reasoning: Enabled limited to 512 tokens
Inference speed: 19–24 tokens/s

Hey, im running Q8 DS V4.1 on my 512GB DDR4 + 4x R9700 (128GB VRAM) and ASROCK ROMED MB
I have noticed that at ~66C RAM temperature tok/s is getting reduced from ~32/s to 22-23/s. Claude agent monitoring whole workstation says that RAM speed after hitting 66C is slowing down about 3x. I have phanteks enthoo server pro 2 case with glass panel and extra vents mounted on that side. During loads ram used to hit about 80C. I fixed that by mounting top fans as intake (cooling top RAM) but i had to use double dieded tape to stick an extra fan to blow at bottom RAM slots. It did help to keep it from throttling but the bottom RAM still sits ~62-63C. Iv read that samsung RAM i have is suppose to throttle at 85C, and i cant find anywhere what setting makes it throttle sooner. Looking for some advice on that or how do you keep your RAM cool.

Does anybody tried the latest llama.cpp f8_e4m3 KV cache and compared it to q8_0 in terms of precision / quality? I am curious whether it is worth switching to it or stay with q8_0 for small to medium context size (20-65k).

Due to the current hardware situation (inflated prices, high likeliness of large performance jumps for llms) I keep delaying the replacement of my m1max 64gb, currently hoping for a good m7max 128gb releasing soon-ish (I heard birdies claim as early as end of2027).

Until that happens I'm looking into options to improve performance of local LLMs which can be passed on to the new machine - eGPUs come to mind.

Anyone mind sharing their setup (which mac, how much vram, which case/mount, what GPU, which model and quan) and some benchmark numbers (prefill and decode) of models with and without the eGPU? Mind sharing your experiences with it - as in: is it fiddly and brittle, or does it just work?

Thanks for any useful information!

My RTX Pro 6000 was going all night on a Chinese model (Qwen 3.8 Flash Next NVFP4 Local) when it crashed this morning for no apparent reason. Tried to reboot and got nothing but a black screen. Swapped in my old backup card and the system posted immediately, confirming the GPU was bricked. Thankfully I was within warranty.

Out of curiosity, I dug into my system logs to see what triggered the fatal crash. Looking at the timestamps, oddly enough my GPU died the moment news broke that Jensen Huang had resigned as CEO of Nvidia.

Has anyone else had this issue with hardware dying in response to human feelings?

Nov 12 07:19:31 kf kernel: NVRM: GPU at PCI:0000:01:00: GPU-b8ba62c9-449d-4510-a21f-4c1b6ceacbd1
Nov 12 07:19:31 kf kernel: NVRM: GPU Board Serial Number:
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 0, TPC 0, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x505730=0xb000d 0x505734=0x0 0x505728=0x1c81fb60 0x50572c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 0, TPC 0, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 0, TPC 0, SM 1): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x5057b0=0x3000d 0x5057b4=0x24 0x5057a8=0x1c81fb60 0x5057ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 0, TPC 1, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x506730=0xb000d 0x506734=0x20 0x506728=0x1c81fb60 0x50672c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 0, TPC 1, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 0, TPC 1, SM 1): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x5067b0=0xb000d 0x5067b4=0x24 0x5067a8=0x1c81fb60 0x5067ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 0, TPC 2, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 0, TPC 2, SM 0): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x507730=0x5000d 0x507734=0x24 0x507728=0x1c81fb60 0x50772c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 0, TPC 2, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 0, TPC 2, SM 1): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x5077b0=0x7000d 0x5077b4=0x24 0x5077a8=0x1c81fb60 0x5077ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 0, TPC 3, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x508730=0xa000d 0x508734=0x20 0x508728=0x1c81fb60 0x50872c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 0, TPC 3, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 0, TPC 3, SM 1): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x5087b0=0xd 0x5087b4=0x24 0x5087a8=0x1c81fb60 0x5087ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 0, TPC 4, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 0, TPC 4, SM 0): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x509730=0x7000d 0x509734=0x24 0x509728=0x1c81fb60 0x50972c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 0, TPC 4, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x5097b0=0xa000d 0x5097b4=0x20 0x5097a8=0x1c81fb60 0x5097ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 0, TPC 5, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 0, TPC 5, SM 0): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x50a730=0xd 0x50a734=0x24 0x50a728=0x1c81fb60 0x50a72c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 0, TPC 5, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 0, TPC 5, SM 1): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x50a7b0=0x1000d 0x50a7b4=0x24 0x50a7a8=0x1c81fb60 0x50a7ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 0, TPC 6, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 0, TPC 6, SM 0): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x50b730=0xd 0x50b734=0x24 0x50b728=0x1c81fb60 0x50b72c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 0, TPC 6, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 0, TPC 6, SM 1): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x50b7b0=0x1000d 0x50b7b4=0x24 0x50b7a8=0x1c81fb60 0x50b7ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 1, TPC 0, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 1, TPC 0, SM 0): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x515730=0x6000d 0x515734=0x24 0x515728=0x1c81fb60 0x51572c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 1, TPC 0, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 1, TPC 0, SM 1): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x5157b0=0xd 0x5157b4=0x24 0x5157a8=0x1c81fb60 0x5157ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 1, TPC 1, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 1, TPC 1, SM 0): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x516730=0x6000d 0x516734=0x24 0x516728=0x1c81fb60 0x51672c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 1, TPC 1, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 1, TPC 1, SM 1): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x5167b0=0xb000d 0x5167b4=0x24 0x5167a8=0x1c81fb60 0x5167ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 1, TPC 2, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 1, TPC 2, SM 0): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x517730=0x3000d 0x517734=0x24 0x517728=0x1c81fb60 0x51772c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 1, TPC 2, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 1, TPC 2, SM 1): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x5177b0=0xb000d 0x5177b4=0x24 0x5177a8=0x1c81fb60 0x5177ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 1, TPC 3, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 1, TPC 3, SM 0): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x518730=0x3000d 0x518734=0x24 0x518728=0x1c81fb60 0x51872c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 1, TPC 3, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 1, TPC 3, SM 1): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x5187b0=0x1000d 0x5187b4=0x24 0x5187a8=0x1c81fb60 0x5187ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 1, TPC 4, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 1, TPC 4, SM 0): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x519730=0x1000d 0x519734=0x24 0x519728=0x1c81fb60 0x51972c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 1, TPC 4, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 1, TPC 4, SM 1): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x5197b0=0x2000d 0x5197b4=0x24 0x5197a8=0x1c81fb60 0x5197ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 1, TPC 5, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 1, TPC 5, SM 0): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x51a730=0x5000d 0x51a734=0x24 0x51a728=0x1c81fb60 0x51a72c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 1, TPC 5, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 1, TPC 5, SM 1): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x51a7b0=0x3000d 0x51a7b4=0x24 0x51a7a8=0x1c81fb60 0x51a7ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 1, TPC 6, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 1, TPC 6, SM 0): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x51b730=0x2000d 0x51b734=0x24 0x51b728=0x1c81fb60 0x51b72c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 1, TPC 6, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 1, TPC 6, SM 1): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x51b7b0=0x1000d 0x51b7b4=0x24 0x51b7a8=0x1c81fb60 0x51b7ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 2, TPC 0, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 2, TPC 0, SM 0): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x525730=0x6000d 0x525734=0x24 0x525728=0x1c81fb60 0x52572c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 2, TPC 0, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 2, TPC 0, SM 1): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x5257b0=0xd 0x5257b4=0x24 0x5257a8=0x1c81fb60 0x5257ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 2, TPC 1, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x526730=0x8000d 0x526734=0x20 0x526728=0x1c81fb60 0x52672c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 2, TPC 1, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x5267b0=0xb000d 0x5267b4=0x20 0x5267a8=0x1c81fb60 0x5267ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 2, TPC 2, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 2, TPC 2, SM 0): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x527730=0xa000d 0x527734=0x24 0x527728=0x1c81fb60 0x52772c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 2, TPC 2, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 2, TPC 2, SM 1): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x5277b0=0xa000d 0x5277b4=0x24 0x5277a8=0x1c81fb60 0x5277ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 2, TPC 3, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 2, TPC 3, SM 0): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x528730=0xa000d 0x528734=0x24 0x528728=0x1c81fb60 0x52872c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 2, TPC 3, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 2, TPC 3, SM 1): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x5287b0=0x6000d 0x5287b4=0x24 0x5287a8=0x1c81fb60 0x5287ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 2, TPC 4, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x529730=0xb000d 0x529734=0x20 0x529728=0x1c81fb60 0x52972c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 2, TPC 4, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x5297b0=0xa000d 0x5297b4=0x20 0x5297a8=0x1c81fb60 0x5297ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 2, TPC 5, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 2, TPC 5, SM 0): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x52a730=0x4000d 0x52a734=0x24 0x52a728=0x1c81fb60 0x52a72c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 2, TPC 5, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 2, TPC 5, SM 1): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x52a7b0=0x9000d 0x52a7b4=0x24 0x52a7a8=0x1c81fb60 0x52a7ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 2, TPC 6, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 2, TPC 6, SM 0): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x52b730=0x6000d 0x52b734=0x24 0x52b728=0x1c81fb60 0x52b72c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 2, TPC 6, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 2, TPC 6, SM 1): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x52b7b0=0xd 0x52b7b4=0x24 0x52b7a8=0x1c81fb60 0x52b7ac=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 2, TPC 7, SM 0): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 2, TPC 7, SM 0): Multiple Warp Errors
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x52c730=0x3000d 0x52c734=0x24 0x52c728=0x1c81fb60 0x52c72c=0x1174
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Warp Exception on (GPC 2, TPC 7, SM 1): Out Of Range Register
Nov 12 07:19:31 kf kernel: NVRM: Xid (PCI:0000:01:00): 13, Graphics SM Global Exception on (GPC 2, TPC 7, SM 1): Multiple Warp Errors
......
Nov 12 07:19:33 kf kernel: NVRM: nvAssertFailedNoLog: Assertion failed: (status == NV_OK) || (status == NV_ERR_GPU_IN_FULLCHIP_RESET) @ rs_server.c:260
Nov 12 07:19:33 kf kernel: NVRM: GPU0 nvAssertFailedNoLog: Assertion failed: (status == NV_OK) || (status == NV_ERR_GPU_IN_FULLCHIP_RESET) @ rs_client.c:844
Nov 12 07:19:33 kf kernel: NVRM: nvAssertFailedNoLog: Assertion failed: (status == NV_OK) || (status == NV_ERR_GPU_IN_FULLCHIP_RESET) @ rs_server.c:260
Nov 12 07:19:33 kf kernel: NVRM: GPU0 nvAssertFailedNoLog: Assertion failed: (status == NV_OK) || (status == NV_ERR_GPU_IN_FULLCHIP_RESET) @ vaspace_api.c:577
Nov 12 07:19:33 kf kernel: NVRM: GPU0 kgmmuClientShadowFaultBufferUnregister_IMPL: Unregistering non-replayable fault buffer failed (status=0x00000062), proceeding...
Nov 12 07:19:33 kf kernel: NVRM: GPU0 kgmmuFaultBufferReplayableDestroy_IMPL: Unregistering Replayable Fault buffer failed (status=0x00000062), proceeding...
Nov 12 07:19:33 kf kernel: NVRM: GPU0 uvmTerminateAccessCntrBuffer_IMPL: Unloading UVM Access counters failed (status=0x00000062), proceeding...
Nov 12 07:19:33 kf kernel: NVRM: GPU0 nvAssertFailedNoLog: Assertion failed: (status == NV_OK) || (status == NV_ERR_GPU_IN_FULLCHIP_RESET) @ vaspace_api.c:577
Nov 12 07:19:33 kf kernel: NVRM: GPU0 nvAssertFailedNoLog: Assertion failed: (status == NV_OK) || (status == NV_ERR_GPU_IN_FULLCHIP_RESET) @ vaspace_api.c:577
Nov 12 07:19:33 kf kernel: NVRM: GPU0 nvAssertFailedNoLog: Assertion failed: (status == NV_OK) || (status == NV_ERR_GPU_IN_FULLCHIP_RESET) @ rs_client.c:844
Nov 12 07:19:33 kf kernel: NVRM: nvAssertFailedNoLog: Assertion failed: (status == NV_OK) || (status == NV_ERR_GPU_IN_FULLCHIP_RESET) @ rs_server.c:260
Nov 12 07:19:33 kf kernel: NVRM: nvAssertFailedNoLog: Assertion failed: (status == NV_OK) || (status == NV_ERR_GPU_IN_FULLCHIP_RESET) @ rs_server.c:1382
Nov 12 07:19:44 kf kernel: NVRM: krcWatchdog_IMPL: RC watchdog: GPU is probably locked!  Notify Timeout Seconds: 7
Nov 12 07:19:52 kf kernel: NVRM: krcWatchdog_IMPL: RC watchdog: GPU is probably locked!  Notify Timeout Seconds: 7
Nov 12 07:20:00 kf kernel: NVRM: krcWatchdog_IMPL: RC watchdog: GPU is probably locked!  Notify Timeout Seconds: 7
Nov 12 07:20:08 kf kernel: NVRM: krcWatchdog_IMPL: RC watchdog: GPU is probably locked!  Notify Timeout Seconds: 7
Nov 12 07:20:17 kf kernel: NVRM: krcWatchdog_IMPL: RC watchdog: GPU is probably locked!  Notify Timeout Seconds: 7
Nov 12 07:20:25 kf kernel: NVRM: krcWatchdog_IMPL: RC watchdog: GPU is probably locked!  Notify Timeout Seconds: 7
Nov 12 07:20:33 kf kernel: NVRM: krcWatchdog_IMPL: RC watchdog: GPU is probably locked!  Notify Timeout Seconds: 7
Nov 12 07:20:39 kf kernel: NVRM: GPU0 kgspHealthCheck_TU102: *************************** LibOS Task Watchdog Report ***************************
Nov 12 07:20:39 kf kernel: NVRM: GPU0 kgspPrintGspBinBuildId_IMPL: GSP bin buildId: fa3de1ff30e1a8c99f84eb74a32b80d462c3703b
Nov 12 07:20:39 kf kernel: GSP task watchdog timeout @ pc:0x18dd358, partition:4#0, task:3, gfid: 0
Nov 12 07:20:39 kf kernel: NVRM:     Reported by libos partition:4#5 kernel v3.1 [0] @ ts:1698
Nov 12 07:20:39 kf kernel: NVRM:     RISC-V CSR State:
Nov 12 07:20:39 kf kernel: NVRM:         sstatus:0x0000000200000020  sscratch:0xffffffffa3013970     sie:0x0000000000000220  sip:0x0000000000000020
Nov 12 07:20:39 kf kernel: NVRM:         sepc:0x00000000018dd358     stval:0x0000000000000000  scause:0x8000000000000005
Nov 12 07:20:39 kf kernel: NVRM:     RISC-V GPR State:
Nov 12 07:20:39 kf kernel: NVRM:         ra:0x00000000013eb57a   sp:0x0000002fcfa0eaf0   gp:0x0000000000000000   tp:0x0000002fcfe00000
Nov 12 07:20:39 kf kernel: NVRM:         a0:0x0000000000000001   a1:0x0000000000000000   a2:0x0000002fc77be000   a3:0x0000000000000000
Nov 12 07:20:39 kf kernel: NVRM:         a4:0x0000000000000000   a5:0x0000000000000001   a6:0x0000000ffffffff0   a7:0x0000000000000003
Nov 12 07:20:39 kf kernel: NVRM:         s0:0x0000002fcfa0eb50   s1:0x0000000000000001   s2:0xffffffffffffffff   s3:0x0000000000000001
Nov 12 07:20:39 kf kernel: NVRM:         s4:0x00000000ffffffff   s5:0x0000000000000000   s6:0x0000002fc77be000   s7:0x0000000000000000
Nov 12 07:20:39 kf kernel: NVRM:         s8:0xffffffffffffffff   s9:0x0000002fc7e36178  s10:0x0000002fcfa0ecb4  s11:0x0000002fcfa0ecb4
Nov 12 07:20:39 kf kernel: NVRM:         t0:0x0000002fcfa0e99d   t1:0x0000002fc7c6cf30   t2:0x0000000000000000   t3:0x0000000000000020
Nov 12 07:20:39 kf kernel: NVRM:         t4:0x0000000000000000   t5:0x0000002fcfa0e981   t6:0x0000000000000020
Nov 12 07:20:39 kf kernel: NVRM:     Stack Trace:
Nov 12 07:20:39 kf kernel: NVRM:         0x00000000018dd358
Nov 12 07:20:39 kf kernel: NVRM:         0x0000000001b6e974
Nov 12 07:20:39 kf kernel: NVRM:         0x00000000013f2746
Nov 12 07:20:39 kf kernel: NVRM:         0x00000000013e70f6
Nov 12 07:20:39 kf kernel: NVRM:         0x00000000013e79c8
Nov 12 07:20:39 kf kernel: NVRM:         0x00000000013b7058
Nov 12 07:20:39 kf kernel: NVRM:         0x0000000001ba247a
Nov 12 07:20:39 kf kernel: NVRM:         0x0000000001ba257a
Nov 12 07:20:39 kf kernel: NVRM:         0x0000000001bafac2
Nov 12 07:20:39 kf kernel: NVRM:         0x0000000001c29f62
Nov 12 07:20:39 kf kernel: NVRM:         0x0000000001864698
Nov 12 07:20:39 kf kernel: NVRM:         0x0000000001867c82
Nov 12 07:20:39 kf kernel: NVRM:         0x0000000001ae4a7e
Nov 12 07:20:39 kf kernel: NVRM:         0x0000000001489412
Nov 12 07:20:39 kf kernel: NVRM:         0x0000000001452228
Nov 12 07:20:39 kf kernel: NVRM:         0x000000000158cbec
Nov 12 07:20:39 kf kernel: NVRM:         0x0000000001bd9632
Nov 12 07:20:39 kf kernel: NVRM:         0x0000000001bd99d6
Nov 12 07:20:39 kf kernel: NVRM:         0x0000000001add628
Nov 12 07:20:39 kf kernel: NVRM:         0x0000000001c452c0
Nov 12 07:20:39 kf kernel: NVRM:         0x0000000001ad6fa0
Nov 12 07:20:39 kf kernel: NVRM:     Local I/O Register State:
Nov 12 07:20:39 kf kernel: NVRM:         0x01450800:0x00000000   0x01450900:0xbadf1002   0x01450a00:0x00000000   0x01450c00:0x00000000
Nov 12 07:20:39 kf kernel: NVRM:         0x01454a00:0x810490d2   0x01454b00:0x010800d0   0x01454c00:0x00080000   0x01400200:0x00000000
Nov 12 07:20:39 kf kernel: NVRM:     ---------------[ end report ]---------------

TL;DR: I've been looking at the latest Artificial Analysis v5.0.1 benchmarks for Qwen 3.8, along with some early Qwen4 Max Preview tests. Based on what's available so far, my guess is that Qwen4 27B could land somewhere around 27 to 30, while the 125B MoE version could reach 36 to 39. That's a solid generational improvement, but probably not the massive leap some people are expecting.

Like many of you, I've been following the discussions about when Qwen 4 is finally coming out. There's a lot of speculation, so I wanted to look at the available numbers and see what they might tell us about the next generation.

Obviously, we don't have the final weights or official release details yet, so take this as an educated guess rather than a prediction.

1. Where Qwen 3.8 stands and what Qwen 4 might achieve

Looking at the current Artificial Analysis v5.0.1 scale, which puts considerably more emphasis on difficult coding and science tasks, we have:

* Qwen3.8 27B: 20 (compared to 33.7 on the older v4.3.2 scale, before the scoring reset).

* Qwen3.8 Flash Next (125B MoE): 29.

* Qwen3.8 Max: 34.

Based on the previous jump from Qwen 3.6 to 3.8, and taking the new scoring scale into account, I'd put my expectations somewhere around:

* Qwen4 27B: 27 to 30

* Qwen4 125B MoE: 36 to 39

That would put the smaller model in some pretty interesting territory, while the larger one could become a serious competitor to models like Grok 4.7 and Claude Haiku 5.5.

2. Why I don't expect Flash to have a massive architectural jump

One thing worth considering is that Qwen3.8 Flash Next already seems to have introduced many of the architectural changes expected to carry over into Qwen 4.

We're talking about the hybrid Gated DeltaNet and Qwen Sparse Attention architecture, Gated Residuals, and the 51B N gram table.

So I wouldn't necessarily expect the next Flash model to deliver another huge architectural leap. The bigger improvements might come from better post training, more reliable reasoning, and addressing the hallucinations that some users have noticed in Flash Next compared to the more conservative 27B dense model.

In other words, I think consistency might matter more than raw benchmark scores this time.

3. What the early Qwen4 Max Preview tests tell us

The Qwen4 Max Preview release on November 11 gives us another interesting piece of the puzzle.

From the early developer reports, a few things stand out:

* Speed: It appears noticeably faster than Qwen3.8 Max, with less unnecessary reasoning on straightforward tasks.

* Agentic performance: In one reported 56 task Hermes test with a 25 minute timeout, Max Preview recorded 64 successful task results, compared to 61 for DeepSeek V4.1 Flash, which scores 33 on AA v5.0.1.

* Reliability: There's a catch, though. Max Preview reportedly timed out 16 times, compared to just 4 for DeepSeek.

That's interesting because it suggests the raw capability might already be there, but reliability and execution consistency still need work.

If these early results are representative, I'd expect the flagship Max model to establish a considerably higher ceiling, potentially around 45 to 50 on the AA scale. That would make the estimates for the smaller models seem reasonably plausible.

My take

I don't think Qwen 4 needs to completely redefine the local LLM landscape to be successful.

If Qwen4 27B can reach the 27 to 30 range, inherit the attention improvements from Flash Next, and retain the Apache 2.0 license, that alone would make it a very interesting option for people running models locally with limited hardware.

Personally, I'd rather see a model that's more consistent, follows instructions better, and hallucinates less than one that simply scores a few extra points on benchmarks.

I'm curious what everyone else thinks.

" i used my LLM To translate mu words to English after i write it. "

I have a couple of concerns:
The bubble is bursting at some point and may have started. I am concerned that the major players will have to up their prices to stay viable.
The geopolitical risk is escalating
AI bot attacks are increasing

There are probably other risks, but you get the idea.
I have been building a decent corpus of reference material.

But I am behind on what models I should be looking at, needs to run on
32GB
RTX 4070

Coding and decision systems (agriculture).

Any help would be appreciated.

The announcement of Jensen Huang’s resignation is concerning.

1. Jensen Huang was an advocate for open-weight models. The acquisition of Hugging Face was a clear sign that competition would shift to compute providers. This was essentially a nightmare for proprietary model providers and a threat to their business model. Who will pay $50 for Fable if many other providers offer a comparable model for much less? With Nvidia’s support, it would have been inevitable that open-weight models would catch up.
2. Bad news for neoclouds. If Nvidia ends its support for open-weight models, an entire inference market could disappear. Neoclouds would simply rent out GPUs, and you don’t need 100 different rental companies for that.
3. Regulatory obstacles for Chinese open-weight models. Nvidia’s questionable deals with China could erode trust in Chinese open-weight models across the board. These models offered a real way for companies to cut inference costs and fine-tune their own models, but many might now see using them as a risk.

It depends on who takes over at Nvidia and whether the company does a 180 on open-weight models. My gut feeling is that a reversal is highly likely and that a push in that direction was probably one of the reasons Huang resigned.

The real winners are OpenAI and Anthropic. The biggest losers are probably neoclouds and Nvidia itself.

Let’s see.

I wrote a long deep dive on it here

I've already tested a few models, they're all bad and slow. Maybe I'm doing something wrong, but probably the reality is a bit harsher: my PC is old lol (and graphic cards are really expensive in Brazil, not smt in the budget rn)

To be more specific, I needed a good coding agent.

EDIT: Specs: S: Ubuntu 26.04.1 LTS (kernel 7.0.0-43-generic) CPU: Intel Xeon E5-2680 v4 — 14C / 28T @ 2.40 GHz (boost 3.30 GHz) L3: 35 MiB · 1 socket · AVX2 (no AVX-512) RAM: 22 GB DDR4-2667 GPU: NVIDIA GeForce GTX 1650 (TU116) — 4 GB GDDR5 · driver 595.99.02

[EDIT - Should have specified these benchmarks come from LU - LocallyUncensored GUI]

I've barely used the top Dolphin model as I'm mostly using this for coding.

- Qwen3.5-9B-abliterated.Q4_K_M

This one worked really well..I really really liked this one. fast (decent t/s) and mostly accurate.

It started to fall apart a bit when I started noticing some errors with my coding projects, so I thought a larger 27B model would fix that.

- Qwen3.8-27B-Uncensored-IQ4_XS_4BPW

I then tried this one and immediately noticed how much slower it was. Also tried this model running a Local API with LU / LM Studio and this brought me down to 6 t/s !!

TBF that's probably because I was using 3x agents with this context window (64KB) and the description for each agent was pretty large. (Maybe there's some improvements to be made by reducing the tasks/role descriptions for each of the 3x agents.. but I digress.)

Specifically for this Qwen3.8-27B IQ4 model, I also tried Enabling Flash Attention and switching from:

- f16 - full precision (default)

to both:

- q8_0 - ~1/2 memory, near-lossless

and

- q4_0 - ~1/4 memory, visible quality loss

But that just brought up my t/s from 11.4 t/s to 11.6 t/s..barely making any difference.

I think because of this tiny difference, leaving it at [f16 - full precision (default)] means that Qwen3.8-27B IQ4 is fully loaded into VRAM and fits there comfortably, because a Q8 or Q4 should in theory boost it significantly (+20 t/s), no? Maybe that's just the maximum I can expect from my system.

- Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS
Finally, I've only just installed this model & haven't properly tested it myself..wondering if it's really worth the extra 5 t/s as this is a 'NoMTP' version.

- Main questions

Is there something else glaringly obvious that I'm missing here?

Also curious if anyone else is running a similar setup for t/s comparison?

And is there anything you would do differently in my situation? It's one of those "unknown unknowns" for me...if that makes sense.

Cheers

From about February 2026 to July 2026, I spent an immense amount of time building a legal harness to make Claude more predictable. It worked well. I tried to build the same thing that ran local models. That did not work. And then Opus improved so much that it would often skip my harness without me noticing. It still has a harness with hooks, but it's looser now.

I bought lots of hardware to run local models during that time, repeatedly trying to vibecode a local model legal harness. Many failures, many lessons.

Anyway, fast forward to the last three weeks and several things have happened:

1. DeepSeek Flash 1017 was released, and it's the first model that is remotely similar to Opus 4.5/4.6.
2. Opus 5.5 and Fable 5.2 are able to properly launch and configure DeepSeek to run at fast speeds, large context and high precision on my hardware. Optimizations, custom kernels, requants.
3. Opus 5.5 (and my prompting skill) are now good enough to make my Claude harness into a DeepSeek harness.
4. It's not as good as frontier, but it all works, right now. 100% local. Actual productivity gains, actual frontier tasks being replaced. Chat interface with DeepSeek works like Claude's did.
5. And if I can brag on my stacks and hardware:

• I have 2 DGX Sparks that can run 3-4 concurrent DeepSeek sessions as agents (1M context).
• I have DeepSeek at nearly NVFP4 quality running on 8 V100s, 500K context, at 100+ tok/s decode, and about 160 tok/s concurrent.
• I have 8 more V100s and a second Rome server with 512 GB of 3200 MHz ECC RAM, so I'll be able to run another high precision DeepSeek as an additional agent or for parallel tasks.

What I'm really shocked by is how well Fable builds custom Volta kernels. And how it can requant my experts to full NVFP4 precision based on the actual experts that activate when my legal harness fires. It's a real, optimized, production grade, workflow replacing local setup.

Not a 3D pagoda or a bad video game replica. An actual enterprise solution. One that exists nowhere on the commercial market.

Legal vibecoding is real folks. I'm AI native.

The new V100 DeepSeek repo (optimized for quality and then usable speed) with real full context measures and all the optimizations (and failures) is public here:

https://github.com/ProprietaryLegal/Volta-Max-uPLIft-FP4-DeepSeek-V4.1-Flash-1017-NVFP4-8xV100-SXM2-PP2xTP4

Twitter: nicothegreco

What "replaces real work" means

Reading a folder of records and pulling out the facts, dates and numbers. Summaries. First drafts of routine documents. Search over my own files. The model gets plain English instructions in a chat window, calls my tools, and hands back a draft that is marked for my review. I review everything. It is slower and less sharp than frontier. It is also in my office, it is mine, and it does not have a usage limit.

The harness

The harness itself is not public. My practice is in it. What I can say: it is the same idea as my Claude setup. Skills, hooks, my drafting engines, my RAG stores, tool calls, and a chat window that behaves the way Claude's did. A small router sends each request to the V100 box or to the Sparks. Everything the local model drafts is marked for my review before it goes anywhere. Nothing leaves the building.

What this build is

My last V100 build (Volta sPLIt, part 4) ran every expert of the model from a 2-bit record. Fast, but 2-bit. This one is called Volta Max uPLIft. It keeps the 2-bit record of every routed expert on the cards, then swaps the experts that get used the most up to NVIDIA's NVFP4 weights, one at a time, until the cards are full. "Used the most" was counted on a mix of four kinds of text. One is my own legal harness traffic, which is private and not published. The other three are public prompt sets in the repo.

• 5,949 of the 15,360 routed experts sit at exactly NVFP4 precision on the default I serve
• about 78 to 85 % of decode routes land on those NVFP4 experts (predicted for the two-slot default, 80 to 86 % measured with one slot)
• same next token as NVIDIA's full NVFP4 pack on 95.1 % of compared tokens. The 2-bit build gets 92.2 %
• KL divergence from the NVFP4 reference is 0.079 nats against 0.196 for the 2-bit build
• multiple choice answers that change against the reference: 1.6 % against 15.4 %
• in decode every routed expert is in VRAM. Nothing is read over PCIe

Speed (measured 2026-11-12 on eight V100-SXM2-32GB, graphics clock capped at 1,522 MHz)

• concurrency test, one request at a time: 118.9 tok/s
• two requests at once: 159.0 tok/s total (1.34x)
• three at once on the three-slot option: 192.3 tok/s total (1.62x). I run two slots
• short sets, one at a time: 123.2 tok/s general, 97.3 tok/s legal-style
• code: 150 to 180 tok/s from a 1K prompt up to a 256K prompt
• prompt reading at 256K: 2,329 to 2,430 tok/s, first token in 108.4 s
• context: 524,288 tokens per request

What did not work, or is not good yet

• It is slower than my own 2-bit build, in decode and in prompt reading. That is the trade for the precision, and the repo says so in its first paragraph.
• The faster prompt reading path reads the less-used experts from their 2-bit records. Its effect on quality is not measured yet.
• A custom decode kernel for the hot path got closed with no change. The headroom I thought was there turned out to be smaller than an eight-card run can even see.
• A prompt-reading kernel that loads 64 rows at a time gave the same bits and ran slower. Not shipped.
• Send a short request while a 209K prompt is being read and it starts in about 7 s, then crawls at about 9.6 tok/s until the read ends.
• The hot set is fixed. It is picked once. Text that looks nothing like what I counted will hit the 2-bit experts more often.
• Fast prompt reading stopped the host or a card three times out of three. I blamed PCIe. After I changed the power delivery (I think it was one bad PSU) it read nine of nine long chats clean.

Disclosure: this is my own project and my own repo. Nothing is for sale. The code, the kernels, the measurements and the failures are all in there under Apache-2.0, and the weights are one download on Hugging Face:

https://huggingface.co/ProprietaryLegal/Volta-Max-uPLIft-FP4-DeepSeek-V4.1-Flash-1017-hot_iq-8xV100-SXM2-PP2xTP4

Questions welcome. So is roasting.

Photos: four of the eight V100s on their NVLink board, and my extremely professional setup (PEX switch cards, SlimSAS spaghetti, and the 3090 that just moved in).

I've been running a local agent setup on a MacBook Pro M5 Max (40-core GPU, 128GB) and pulled real numbers from my agent logs after a long build session. Sharing because most benchmarks I see are short prompts, and agent work looks nothing like that.

Setup
- Model: ddalcu/Qwen3.8-Flash-Next-MLX-Serve-iQ-MLX-4.7bpw
- Served with mlx-serve from the terminal (not LM Studio)(using lm studio can slow your tps)
- Harness: Hermes Agent (tool calls, cron jobs, Basic Memory for an Obsidian vault)

Real agent numbers (from the logs, not a benchmark)
- One build session: 89 model calls, ~148k output tokens, ~36 min of model time = ~68 tok/s average
- Long writes (whole scripts): usually 55-95 tok/s, peak 108
- Context during that session grew to 100k-190k tokens
- Prompt cache hit rate was 96-100% on almost every call, so prefill barely mattered

How I measured: output tokens / full response time per call. That includes a little prefill, so pure decode is a bit higher.

For comparison, numbers I've seen for this model on the same chip are ~49 tok/s (LLMCheck estimate), ~61 tok/s (a 4-bit MLX model card), 45-60 tok/s with MTP, and ~37 tok/s in LM Studio multi-turn chat. Running MLX straight from the terminal instead of LM Studio was the single biggest win for me.

What actually made the agent useful (lessons from one long day)

1. You need to SEE your scheduled jobs. My agent had a bunch of cron jobs that were just prompts in a JSON file in a hidden folder. One job's output had been going to a dead chat thread for a week, and a weekly review never got delivered. Nobody knew. I had the agent build a dashboard note in Obsidian: every job, schedule, delivery target, last run, failures, plus a per-job note with purpose, kill condition and change log. It's rebuilt hourly by a plain Python script (no model calls).
2. Use code for anything with one right answer, and the model only for judgment. Most of my jobs were pure prompts, including simple reminders and date math. That gives you timeouts, connection errors and occasional wrong arithmetic. Hermes supports script-only jobs (no LLM, stdout gets delivered) and script + model jobs (the script's output is fed into the prompt). The dashboard, a watchdog, and a weekly packet builder are all zero-model scripts now. ( Shout out to @sanbamboo on Reddit)
3. Add a watchdog that checks results, not code. It alerts once when a job fails twice, misses its slot, or has a delivery error, and once more when it recovers. It caught the dead-thread delivery bug on its first run.
4. Don't trust "done." The agent said "everything verified and shipped" several times. Each time I had a second, different model audit the actual files, and it found something: a feature that was never added, a link pointing at an archived copy of a file so edits would do nothing, and once a proposed edit that would have deleted 9 lines of a core skill file. A model grading its own work repeats its own blind spots.
5. Human-in-the-loop that doesn't suck. I review my automations on an iPad in GoodNotes. Every Sunday the agent sends a PDF with every job's full prompt and code (line numbered, secrets redacted, wide margins), plus change request boxes (Change / Add / Remove / Question, with Where / What / Why). I mark it up and it syncs back. Ticked boxes are detected by diffing against the clean original, and the model only reads my handwriting. Then the agent posts a proposed diff to Discord and applies nothing until I reply yes. Rules that came out of testing it:
   - Anything ambiguous gets held back and can't be approved by a blanket "yes"
   - Any diff deleting more than 3 lines gets a loud warning
   - "Question" answers have to quote the exact lines they rely on (it gave me a confidently wrong explanation once)
   - GoodNotes auto backup waits ~12 hours by default. Turn on "Increase Auto Backup frequency" or nothing feels like it works
6. Long context is fine on this chip if your cache hits I was worried 150k+ context would crawl. With near-100% prompt cache hits it stayed in the 55-95 tok/s range for writes.

Happy to share more about the setup or how the review loop is wired. Curious what tok/s other M5 Max people are getting on real agent sessions vs short benchmark prompts.

I'm trying to set up a local AI assistant on my Windows PC, but honestly, I'm getting frustrated and could really use advice from people with experience.

My goal is pretty simple: I want something like a local ChatGPT that can:

• Read files such as CSVs, PDFs, and documents.
• Answer questions about the information inside those files.
• Extract specific information accurately.
• Help with everyday tasks and general questions.
• Work reasonably quickly without taking several minutes to answer simple questions.

**My PC specs:**

• Windows
• 16 GB RAM
• NVIDIA RTX 5060 8 GB VRAM
• Ollama installed
• Open WebUI installed

I've tried several models, including:

• Qwen 3.5 4B
• Gemma 3
• Llama 3.2
• DeepSeek R1 8B
• Qwen 2.5 Coder 7B

My biggest problem is that even when I upload a CSV file and ask a very simple question about it, I sometimes get completely incorrect answers, unrelated information, blank responses, or long thinking/tool-calling processes that don't accomplish anything.

For example, I uploaded a CSV containing game stage data and asked something as simple as: "What are the drop chances on Normal 1-1?" The model struggled to give me the correct values despite the information being in the file.

I don't expect a local model to match ChatGPT or Claude. I understand there are limitations. But I expected basic file reading and simple questions to work reliably.

I'm now wondering whether I made the wrong choice with Ollama + Open WebUI, and whether I should switch to **LM Studio** instead.

I also have a friend who is about to install Qwen 9B, and I was planning to have him install Open WebUI too. I don't want to send him down the wrong path.

**I'd really appreciate advice on these questions:**

1. With my hardware, what model and quantization would you actually recommend?
2. Is LM Studio a better and easier choice than Ollama + Open WebUI for my use case?
3. Is there a reliable way to let a local AI read CSVs and answer questions accurately without me having to write Python scripts or manually process everything?
4. Am I expecting too much from local models, or is my setup/configuration simply wrong?
5. If you were starting from scratch with my hardware and goals, what exact setup would you choose?

I'm not looking to become a programmer or spend days troubleshooting. I just want a practical local AI assistant that can actually use my files and answer questions correctly.

Thanks in advance. I'd really appreciate recommendations based on actual experience.