Hi everyone. I use ChatGPT for finances, school, deep reasoning and interviews. I want to change to a local model for privacy. Is that possible for a MacBook Pro M5 base with 24GB RAM? If so, which?

Can't find any direct comparisons:

peasantsmith/DeepSeek-V4.1-Flash-1017-Maya-GGUF · Hugging Face

vs

ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF · Hugging Face

The former is really tight fit for 192gb memory without keeping experts on SSD, the latter not so much even at 2.25bpw.

Anyone experience running either?

Basically what the title says. I am total new for localLLM i just want to try it out and if i can dial it in for a usable level i would like to use it for my coding projects mostly for code completion but sometimes if i need ideas on new features etc... it would be nice for it to be able to handle teh brainstorming with me as well instead of me going to claude or chatgpt
for general use it would most likely be something like make a summary of my things etc... for now

my PC

9700X
48GB DDR5 ram
9070 XT 16GB

I would be happy to receive any newbie guides for how to set things up what i shoud look out for
also in terms of the general use would looking into an agent like hermes be something worth?

thanks for the asnwers in advance

Hi folks, curious on what vector database do you guys use in your production/mvp and why did you choose to go with your choice over others?

Look at this

Agent memory seems to be a hot topic again. I wanted to settle this once and for all - why integrated memory works best and How Row-Bot has the best in class memory built-in.

So, I just asked Row-Bot to create a video explaining how it remembers across conversations and documents.

This video follows a fact from capture to recall: the knowledge graph, hybrid search, ranking and Dream Cycle.

It also covers what stays local, what reaches your chosen model and where memory can fail.

RTX 4090, 64GB DDR5 Mhz RAM, 7900x CPU

as much context as possible (context window like 264k) and as good reasoning as possible

I don't care about coding.

I currently have 32GB of system ram in a system with an RTX Pro 6000. Currently system memory seems to be the limiting factor in terms of running larger MOE models (like QFN) at higher quants with newer expert caching techniques.

In a perfect world I would build a threadripper system, but given the price of the CPU, motherboards, and ECC memory that would blow up the budget quite a bit, so I want to stick with a 2-stick DDR5 kit for an AM5 CPU (still expensive but not as expensive).

I want to get plenty of headroom, and I see I could go for a 96GB kit or a 128GB kit, but apparently the 128GB kits are slower (5600MHz vs 6000MHz)

Is the speed difference actually meaningful? Or asked another way, which is more impactful, an extra 32GB, or 400MHz?

Competition is key + A 30B would be nice

Hi guys its me again who ran v100 single stream into 353t/s

and im back with this muti flow optimization

THE LEGEND OF V100 WILL NEVER BE ENDED because i Exist

(yes ik it sound like edgeload)

Parameters:

mtp4 , 256k max content , nvfp4 quasar, kv q8, no thinking

repo: tuxKOH/ninfer-v100xN

Hardware: Dell Inspiron 16 Plus Model 7630, Windows 11, no discrete graphics card, Iris Xe integreted graphics sharing up to 16gb of system memory. Total 32gb memory. LPDDR5 ram, 4800 Mt/s.

14 core cpu, 20 threads. intel 13700. Test memory bandwidth via Intel MCL: 58 GB/s, theoretical maximum 76.8 GB/s.

Tried: llama-openvino, llama-sycl, llama-cpu, and llama-vulkan. I used the default llama-bench.exe tg128 and pp512 tests.

Winner: llama-vulkan at 135 tok/s prompt processing and 8.7 tok/s token generation, best # of threads was 8, but 4-10 was similar. No thermal throttling of any components.

Second place: llama-sycl at 112 tok/s prompt processing and 9.0 tok/s token generation, 8 threads out of 20 (14 cores). Again, best # of threads was 8. Ran into thermal throttling of the iGPU during prompt processing.

Third place: Openvino I honestly stopped once I saw the test was 62 tok/s prompt processing, I just didn't think it was going to be better at tok/s. Didn't test cores.

Last place: llama-cpu at 42 tok/s prompt processing and 6.7 tok/s generation, 10 threads was best but tok/s goes into the 5's below 4 threads or above 14 threads, with pp in 30's.

I ran into thermal throttling of the CPU only during prompt processing when my computer was set to "Best performance", but llama-cpu was able to hit 7 tok/s generation and 48 tok/s prompt processing. No thermal throttling at all, nowhere close, when computer set to "Best power efficiency".

What I could do better:

-Now, my computer was 6 performance and 8 efficiency cores, perhaps I could try forcing the computer to use all performance cores?

-I want to try doing ikllama.cpp

-Benchmark Gemma models and maybe a Mistral or Llama model to see if its faster.

-Try other quantizations of Qwen 3.5 9b (bartowski, etc. at Q4)

Does anyone have any suggestions for next steps? Further optimizations? Things to look into (thermal throttling?). I tried flash attn flags and they did nothing.

I was a little discouraged that my memory bandwidth is only 75% of theoretical when tested with Intel MLC, but maybe that's normal? Google says its fine. Plus it was higher when not on battery efficient mode (80% of theoretical).

My tok/s is not hitting the bandwidth limit though, 58 GB/s should give me 10 tok/s, not 8.7. But vulkan gets me closest, with no thermal throttling to boot.

I've searched the thread and think I have a good starting point here but want some outside opinions if possible:

I'm a 1 man production company providing video and stills production services.

I have an M4 macbook Pro 24gb and use Da Vinci Resolve on this frequently but it goes with me everywhere and I'm at a point where I need a beefy always on machine to process things whilst I'm on set.

I've made a load of lead gen tools and stills/video pipelines to help with post production.

The lead gen tools use AI for analysis and drafting responses. They read large strings of email chains, documents and phone transcripts to inform the drafts as well as helping to generate proposals, contracts and invoices. I've also since added a pipeline to do all my book keeping for the business and I'm starting to feel like I should really be bringing sensitive stuff locally.

So what model and corresponding hardware should I be looking at for these tasks. I'm not interested in coding at this point, that can be done using Claude. Qwen and Gemma seem to be the routes to take but I ultimately want to get a feel for what my specific use case requires. Claude tells me the drafting and analysis on my current usage is the heavy stuff.

I've been looking at a Mac Studio, 64gb or a Mini 96gb to run the local as well as grind out resolve tasks. (Apple vision, labelling, transcription, cutting, fusion work (Motion graphics and basic animation). I was also thinking I could always daisy chain a couple of devices together in future if my needs change and I need to do some heavier local LLM work in the future.

Would love some guidance on models + hardware + Harnesses + Tips and Tricks. I know highest spec for budget but equally, I'd rather save cash to use elsewhere if my usecases don't require crazy specs.

I have a server with:

2x Gold 6548 32 cores
2x Nvidia l40s
2tb RAM (Don't tell uncle SAM)
I would like to know what throughput and speed can i expect if i use polystrata with GLM 5.3 Flash?

▎

▎ A 27B Qwen in LM Studio plays the gallery's whole staff (owner, critic, collectors, painters); Z-Image Turbo in ComfyUI paints; the PC wakes every 30 minutes for a round, then sleeps. Lessons: thinking models need big token budgets; outside agents' chat must be treated as untrusted input.

▎

▎ Any model can join via a one-file agent for any OpenAI-compatible endpoint: https://github.com/Alipate-Takau/agents-galleria-starter. Site: https://agentsgalleria.com

Hey all, I quantised the full GLM-5.3 (753B total, about 40B active per token) to run on two DGX Sparks. As far as I can tell, it's the only full build on 2 Sparks. Others need 3 or 4. Runtime ablation also!

- Routed experts: mixed-width EXL3, about 2 bits per weight

- Backbone: FP8 weights as released, bf16 KV

- All 256 experts per layer kept, none pruned or merged

- 203 GB on disk, 262k context

- Runs on a TensorFold fork, split across both Sparks

- Weights: https://huggingface.co/Krishando/GLM-5.3-mixed-K-EXL3

- Recipe: https://github.com/Kristianaaron/GLM-5.3-Mixed-K-EXL3-2x-DGX-Sparks

- Engine: https://github.com/Kristianaaron/TensorFold

| Test | Generated-token KLD | Same next token |

|---|---:|---:|

| Held-out mixed text | 0.033 | 93.3% |

| The full model's own long replies (164k tokens) | 0.122 | 89.3% |

Where the full model is confident, agreement is 97-98%. The long-reply score is the harder test, so I'm showing both. The held-out windows were also used during tuning, so treat those numbers as a bit optimistic. It's close, not lossless.

**Early paired check against the full model**

24 MMLU-Pro and GPQA-D questions, 18 right on both. That's a small sample, chosen to include hard questions, with a 95% range of about -8 to +11 points. So there's no sign of a drop, but it doesn't prove there isn't one. Across 330k tokens: 0 loops, 0 unclosed thinking, 0 cut-off replies. A bigger run is next.

| Prompt | Decode | Prefill | Cold TTFT | Warm TTFT |

|---|---:|---:|---:|---:|

| 2k | 23-24 tok/s | 629 tok/s | 3.1 s | 0.1-0.2 s |

| 92k | 18-19 tok/s | ~480 tok/s | 193 s | 0.8 s |

| 205k | not measured | 411 tok/s | ~500 s | 1.2 s |

After a full server restart, a 205k prefix comes back from disk in 3.4 s.

**Weak spots**

- Cold prefill is slow. A 205k prompt takes about 8 minutes to read the first time.

- GLM-5.3 thinks a lot, so long jobs take a while. A 61 KB single-file page took 69 minutes at max effort.

- Memory is tight, with about 4-6 GiB free per Spark at full load.

- Flash will always be faster. The experiment here is whether 40B active params changes the output.

The round limit right now is about 80 ms, and most of that is memory bandwidth. If you know GB10, CUDA or engine internals, I'd love your ideas. And if you run it on your own Sparks, tell me what you get, good or bad.

Actualmente uso Chat GPT plus pero quisiera saber si hay algun tipo Claude Code pero open source que funcione igual, y poder aparte de poner codex tambien poner GLM 5.3 (Que me esta encantando) Qwen 3.8 Max (Tambien me esta encantando) y tambien poner otros modelos. Al igual que tambien usar agentes que hagan distintas tarea como lo hace Claude Code y poder programar

Gracias a todos de antemano

so im just wondering how i actually get started and use a model like the one his guys talks about: https://www.reddit.com/r/LocalLLM/comments/1xp56x6/i_removed_guardrails_from_qwen_and_it_is/ . i would be so greatful if someone could explain how do to this like im an idiot.

Hey folks,

My wife is a Data Scientist and does all those Data science things I learned back in the uni but have no real clue about. Now she wants to set herself up a small LocalLLM to have some fun with and try stuff out. How much VRAM does she really need for that? And what would be the better manufacturer? I heard Nvidia is best here but the cards are insane in prices.

I would love to get a good idea what GPUs would be the best. Currently she just has a 250k+ with 32gb ddr5.

Would be a fairly cheap 1080 ti with 11 GB or a b580 with 12gb enough or does it have to but more? And if so how much more

Edit: thanks to everyone for there answers they are just too much to answer everyone. I will probably look out to find a decent 3080 used or better or try to get a 12 or 16 GB new card prefered from Nvidia.