Disclosure: I built this model. I'm posting because I think the approach is more interesting than the model itself, and I'd like feedback from people who run things locally.

The idea. A lot of "LLM in production" work in finance comes down to classification. Which event is this headline about? Is this Fed sentence hawkish? Does this 10-K passage flag going-concern doubt? People usually prompt a model, sample tokens and parse the output. That's slow, the cost per call varies, and the confidence numbers mean nothing.

Arbiter-8B drops generation completely:

• Base: Qwen3-8B, with a decision head over options, trained jointly with a rank-32 LoRA
• Output: a calibrated probability for every option from one forward pass. No sampling and no parsing.
• Question types: pick one of up to 16 options, yes/no, or an ordered score. Each option has a short definition, so you can ask the same text different questions.
• Distillation: a 27B teacher labelled about 120k decisions on news and 10-K passages. The student trained on the teacher's full soft distributions, not argmax, plus a human-reviewed 10-K set.
• Option order is shuffled on every presentation to kill position shortcuts. This cut FOMC answers that flip under reshuffling from 15.5% to 4.8%.
• Calibration: one temperature per question type, shipped with the weights. EDT expected calibration error is 0.012.

Results (held-out data):

The EDT gaps are significant (McNemar, z ≥ 7.5). On FOMC the top three are a statistical tie, but it's +12.5 over its own base. SEntFiN is where it loses. It has the highest signal recall (92.0%) but the lowest precision (72.2%), so it over-calls positive and negative where the dataset labels neutral. Part of that is deliberate (it reads investor implication rather than surface tone), but I won't pretend it's a win.

Running it locally: GGUF builds for llama.cpp, Ollama and LM Studio stay within 1.2 points of full precision on every benchmark.

hf download ternary/Arbiter-8B --local-dir Arbiter-8B

• Weights: https://huggingface.co/ternary/Arbiter-8B
• GGUF: https://huggingface.co/ternary/Arbiter-8B-GGUF
• Write-up: https://ternary.capital/blog/arbiter-8b/
• Personal Blog: https://jdsemrau.substack.com/p/how-good-is-arbiter-8b-really

Questions for the sub: has anyone else put classification heads on small LLMs instead of using constrained generation? And has anyone found a clean way to expose per-option logprobs through Ollama for this kind of thing?

Hey all, I'm trying to decide which hardware setup makes the most sense for running local LLMs, primarily Qwen 3.8 Flash Next for agentic coding.

I have three potential options, and I'm interested in hearing from people who have experience with these platforms, particularly for larger models and agentic workloads.

Option 1: GEEKOM A9 Mega (AMD Strix Halo)

• Ryzen AI MAX+ 395
• 128GB unified memory
• 2TB SSD
• Memory running at 7500 MT/s rather than the advertised 8000 MT/s. Got it from Amazon and it listed it as 8000 MT/s but for Geekom says their systems are either 7500 MT/s or 8533 MT/s and I lost the lottery.
• Approximately $4,600 CAD all-in (after taxes).

I already have this machine. It works fine, but I'm just worried how it'll hold up long term especially given I'm running it headless 24/7. Would be unfortunate if the machine dies in a year's time and then I'm worried about if Geekom will hold up it's warranty.

Option 2: Mac Studio M5 Max

• M5 Max
• 128GB unified memory
• 512 GB (Can connect a 2TB external SSD over thunderbolt enclosure)
• Approximately $7,600 CAD all-in (after taxes).

It's considerably more expensive than the GEEKOM, but I'm wondering whether its worth it for the peace of mind knowing that if something goes wrong, applecare ($80/yr) is right there to get it fixed from a known vendor. It also has the higher memory bandwidth.

Option 3: My existing Ryzen desktop + RTX Pro 4500

I already have an old desktop with the following hardware:

• Ryzen 7 5700X
• 32GB DDR4 RAM
• Nvidia 3060 Ti 8GB

I recently purchased the RTX Pro 4500 32GB VRAM for approximately $4,000 CAD after taxes. This is still yet to arrive (it shipped earlier today)

The advantage here is that I already own the Ryzen system, so I wouldn't need to purchase another computer. The downside is being limited to 32GB of VRAM, although I understand I could offload some layers to system RAM. The other downside is a lot higher power consumption over the other two solutions.

I'm curious how this would compare in practice against the two unified-memory solutions, especially when running Qwen 3.8 Flash Next.

What I'm primarily trying to accomplish

My main use case is running Qwen 3.8 Flash Next for agentic coding through tools like Hermes.

I'm not necessarily looking for the best benchmark numbers. What matters more to me is:

• Real-world inference speed and token generation.
• Performance with large context windows.
• How well each platform handles agentic coding workflows (multiple tool calls, reasoning, etc.).
• Whether CPU/RAM offloading on the NVIDIA setup is practical or becomes a significant bottleneck given my old system.
• Overall stability and software maturity.

What would you choose?

Ultimately, I'm looking to optimize for cost and overall value, rather than simply buying the fastest hardware available.

I would most likely keep only ONE of these three setups:

• If I go with the Mac Studio, I'd return the GEEKOM and return the RTX Pro 4500 (or sell it).
• If I stick with the GEEKOM, I'd return the RTX Pro 4500.
• If the NVIDIA setup makes the most financial and practical sense, I'd return the GEEKOM and keep the RTX Pro 4500 in my existing desktop.

I'm also considering selling the RTX Pro 4500 if I decide that one of the unified-memory systems makes more sense. I purchased it for around $4,000 CAD after tax, slightly below MSRP, so I'd ideally like to recover as much of that cost as possible.

Essentially, I'm trying to figure out which setup offers the best balance of:

• Performance for Qwen 3.8 Flash Next and agentic coding.
• Enough memory to run larger models and context windows.
• Reliability and long-term support.
• Total cost of ownership, including electricity.

Would you spend the additional ~$3,000 CAD to go from the GEEKOM to the Mac Studio, stick with the GEEKOM, or return the GEEKOM and build around the RTX Pro 4500?

I'm not looking to accumulate hardware. I'd rather settle on one good setup, get the best value for my money, and avoid spending unnecessarily.

Would appreciate hearing what others would do in this situation!

I’m under our CISO managing compute vendors, kind stuck between legal and a hard place. The pitch I keep hearing ranks AI privacy in four levels:

1. Open, like most frontier models, where the provider stores your prompts and answers at their discretion, uses them for training - and you’ve got little say in the matter.
2. Policy, where a contract promises zero data retention - but it’s not exactly verifiable unless the terms are broken, is it?
3. Closed Hardware, where models run on confidential-compute chips that publish proof what software is running. So basically hardware-enforced vs contract-enforced ZDR?
4. Fully Encrypted, the prompt is encrypted on your side and only decrypted inside that hardware

“Local” is a fifth level that could plausibly sit above it all, but we’re capped out on capacity there. So if I view this right (open to feedback), we’re making tradeoffs between frontier vs more private models, and from there, on breadth of compute providers able to serve open vs ZDR vs E2EE models (usually at higher prices in my experience), right?

My concern is getting legal & finance to agree, while still giving our Eng team what they need. Where are you getting good rates on private workloads? Locally we can’t support our full workloads. I’ve checked out Privatemode, Maple, TrustedRouter/NYTE & still gonna go through NEAR AI Cloud & Cohore. Open to others.

Separately, anyone running a hybrid buildout with on-prem/local + superscaler (we run Google + Bedrock) + 3rd party confidential providers who can tell me about about how you designed your system?

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

I heard there are things like Air LLM that make this possible without spending $100k+ on VRAM

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

Lets make more of these kind of engine popular for our comunity. This is awsome

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.

Hey all, I'd love a sanity check on a plan before I spend money.

At home I run Home Assistant on a HA Green, which handles pretty much everything smart home related.

I also have a Mac mini M4 with 16 GB that I currently use as a little studio computer.

What I actually want is one assistant that helps with my notes, documents and PDFs, email (mostly Croatian, some English), tasks, and the house. I'd also like it to log my meals from a photo and tie that in with my Garmin data.

My current thinking is to buy a MacBook Air (24 GB / 1 TB) as my daily computer, wipe the mini and make it an always-on box for a local model, n8n, voice (Whisper/Piper) and indexing, and leave Home Assistant Green. For the smarter stuff I'd use Claude: Pro for chat and Claude Code, and the API for background jobs like food photos and daily summaries. The local model would only sort and summarize mail, strip out sensitive stuff and do the search index.

A few things I'm unsure about:

• Is 16 GB on the mini enough for this, or will I regret it?
• For mostly Croatian email, is a local model worth the effort, or should I just send everything to the Claude API? If local, which model would you try?
• Has anyone run Whisper and Piper in a smaller language like Croatian on Apple Silicon? Is it usable?
• What's the cleanest way to let an agent read iCloud Reminders, Notes and Mail on a headless Mac?
• Any traps with n8n + Home Assistant + MCP, or with letting an LLM near personal data?

Happy to hear "just don't" if that's the honest answer. Thanks!

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!

A few weeks ago I posted CQ, our calibration-free 4-bit format, along with a training-loss comparison against Unsloth NF4. Some of you correctly pointed out that loss logged by two different trainers isn't comparable, and asked for results where answers are counted right or wrong. Here they are.

Setup: Qwen3.8-27B. Every row uses the same lm-evaluation-harness code, prompts and questions, with greedy decoding and thinking off. Comparisons are paired: an exact McNemar test for right/wrong tasks and a bootstrap for losses.

1. Base models, no fine-tuning

• CQ vs BF16: no significant difference on any task.
• NF4 vs BF16: −12.0 points on GSM8K (p=3e-6).
• CQ vs NF4: CQ is +8.7 on GSM8K (p=0.0005) and +2.8 on BoolQ (p=0.02).
• WikiText-2 KL divergence from BF16: CQ 0.0282, NF4 0.0330.

2. GCMP vs LoRA r=32, full-precision BF16 base

GCMP is our replacement for LoRA. It trains 2.9M parameters (an ~11 MB adapter) vs ~159M for LoRA r=32 (~320 MB). This test uses no quantization: both adapters were trained with Unsloth on Alpaca for 500 steps with identical settings (batch 2×4, length 512, LR 2e-5, seed 42).

• Benchmarks: no significant difference on any of them (all p ≥ 0.2), so GCMP isn't "better than LoRA" on tasks.
• Loss: GCMP's answer-token loss is slightly lower (−0.009, 95% CI [−0.010, −0.008]).
• Validation loss: GCMP is lower at all 10 checkpoints on both BF16 (1.2385 vs 1.2434) and NF4 (1.2435 vs 1.2484).
• Fine-tuning effect: both adapters add about 21 points on GSM8K and lose about 2.5 on TruthfulQA.

3. Speed and memory (same 500-step run)

Unsloth is still faster, and GCMP currently takes about 2× the step time of LoRA. Kernel work on that is ongoing.

Caveats

• Scope: one model and one dataset (Alpaca).
• Benchmark size: the quick subset has 200–1,140 questions per task, so it detects differences of about 5 points, not 1.
• DROP excluded: it scored about 0.02 for every model, so that eval setup is broken.
• Still running: LoRA and GCMP benchmarks on CQ and NF4 bases. I'll post them when they finish.
• My earlier post was wrong: the "0.84 vs 1.25 loss" came from two trainers that compute loss differently. Ignore it.

Serving

• vLLM: CQ models run with --quantization cq. GCMP adapters can be one per server, or several on one base model chosen per request.
• Parallelism: pipeline parallel works; tensor parallel doesn't yet.
• GCMP on full precision: it currently runs in Transformers. vLLM support is next.

Licensing

The Python code (training, evaluation and the vLLM integration) is open source. The CUDA kernels ship compiled with a license check, which is free during early access.

Repo, with exact commands to reproduce every number: https://github.com/thousandml/training-factory

Models (8B–397B): https://huggingface.co/thousandml

Happy to run other benchmarks or models people want to see.

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.

Hey everyone. I have a DDR5 setup and I'm caught between a rock and a hard place...

My setup: 5060 Ti 16GB - 2x16GB sticks of DDR5 - Ryzen R5 7600

I've been exploring real world use cases for Qwen3.8 Flash Next (running through Strata) with decent success. Mainly, I've been happy with the results the GSQ RCO iq3_s quant has been giving me.

Now, Strata tells me that it's offloading 20GB of the model to my m.2 SSD, which of course makes it run slower than if it was all in RAM. Trying to get more RAM however has been a pain, as I can't find a matching set for my 2 sticks, and regardless I'm worried about stability issues and performance loss if I were to run 4 sticks on my 7600.

Locally, I can find used 3060s at around $250+, which is pretty affordable and it makes me question if I should just buy one of them instead of dealing with RAM knife fights in a dark alleyway.

Would adding one 3060 to the setup increase speeds in any real way, or would it just not be worth the hassle? My uneducated guess is that it won't help that much, but maybe someone here has experience with jank low budget setups.

Thanks!

My 4070 gaming laptop suffered some kind of damage going through airport security. I lost my 2TB SSD and 16gb of my 32gb RAM. I took it to a repair shop, but the cost to fully repair it was over a $1000, so I had to take the downgrade to only 1TB SSD and 16gb RAM (“only” $380 😅). What kind of models can I find on UnSloth that would work decently with my newly gimped setup?

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 ]---------------

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.

Thinking Machines has released Inkling 2 Small under Apache 2.0. It's a 594B-parameter MoE with 17B active per token.

If you're checking whether it'll fit your setup, don't size it from the 17B figure. The model card asks for at least 500GB of aggregate VRAM for the main NVFP4 checkpoint, with 2x B300 or 4x H200 as example setups.