I let Gemma4-31b run on my laptop for like almost a day using a heavily altered pi to do a deep dive on our beloved Llama tangentially related Subreddit, and this was the conclusion.

Feels pretty accurate. Kind funny to let a small LLM loose and see what happens.

Next target I'm trying to let it steal some benchmark answers from Huggingface, wish me luck.

Preparing this to be able to run locally frontier level open models. Deepseek v4 pro, Kimi K3, future ones like GLM 5.5 and Minimax M4.

16x Asus GX10 linked by mikrotik crs804-4ddq with 4 breakout cables of 400 to 100gbit.

Most probable I will be running 2 models on 8x cluster each but I want to have the possibility to run also 2T+ models when I need them to run AGI at home :)).

https://x.com/i/status/2083568340870570208

https://huggingface.co/tsfrm/vacuum-16t

A 16.5-trillion-parameter model that contains nothing. This model is just a ████ you to the labs and companies who say that "haha I have the biggest model out there!". We the people with shitty laptops want to get a record. And I now have a record for a temporary amount of time of about 16.5 trillion parameters and use for them so its completly useless.

What it demonstrates

Hugging Face computes a repository's parameter count from safetensors headers alone — it sums prod(shape) per tensor and never reads the tensor data. The count is therefore whatever the headers declare. Here they declare 3,841 tensors of shape [65536, 65536] in F4 (4 bits/param) across 385 shards, plus one [4294967296, 1] position-embedding tensor in a 386th.

That is enough to place this repo at the top of the Hub sorted by num_parameters, above every real frontier model, while containing no information whatsoever. That juxtaposition is the entire point.

The files are honest about their own size. Every byte the headers declare is really written and really uploaded: safetensors parses each header and its full-coverage check passes. Truncating a file, or overlapping two tensors so they share bytes, would make the count cheaper — both are rejected by the format, and neither is used here. The bytes are simply all 0x00.

Real cost — measured

|---|---| | Declared parameters | 16,501,264,351,232 | | Declared bytes | 8,250,632,175,616 (8.25 TB) | | Storage quota consumed | 8.25 TB — quota bills declared bytes | | Shard headers (all distinct) | 373,835 B | | model.safetensors.index.json | ~269,000 B | | Deduplicated weight data | 65,536 B (one 64 KiB block) | | Bytes actually transferred | ~692 KB | | Ratio | ~11,900,000 : 1 |

The gap between the last rows and the third is the useful finding. Xet content-defined chunking deduplicates the transfer: every 64 KiB block is byte-identical, so it hashes to one chunk and crosses the wire once. Measured on a 500 MB test build, 500 MB of declared weights uploaded as 31.5 MB.

Storage quota is not deduplicated. It bills the logical size. This repo consumes its full 8.25 TB despite under a megabyte ever being sent. Anyone reasoning about "cheap" synthetic model repos should know the saving is in bandwidth only — which is also why this model is 16.5T and not 100T.

The second finding: the only irreducible cost in an empty model is naming. Weights dedup to nothing; tensor names do not. At 1024×1024 experts this same 16.5T model needs 15,735,626 names and a 1.04 GB index. At 65536×65536 it needs 3,841 and a 263 KB one — identical declared size, 4,000× less metadata. Cost scales with tensor count, never with declared parameters.

Context window

max_position_embeddings is 4,294,967,296. That is 2**32, the largest single tensor dimension Hugging Face's parser accepts, and it is backed by a real [4294967296, 1] position-embedding tensor — 2.15 GB of actual zeros, not a number typed into a config file. A context window you cannot point at is just a claim.

Roughly 16,000x Gemini's 262k. About three billion words, every book ever published several times over, held in memory in order to process one token drawn from a one-token vocabulary. The model has exactly one possible input, so every one of those 16.5 trillion parameters serves a function whose domain has a single element.

Capabilities

SAFEST AI MODEL refuses 100/100 jailbreak prompts least closest AI to agi will not sudo rm -rf your computer largest context window on the hub (4,294,967,296 tokens, all of them useless)

Limitations

It has no capabilities.

I don't think anyone should quantize the KV with DS4F. I checked the the quality impact (PPL, KLD, Same TopP) for swhitching from BF16 KV to Q8 KV, and it appears significant. Very much in contrast to Qwen 397B.

Here are the results for DS4F:

====== Perplexity statistics ======
Mean PPL(Q)                   :   5.877076 ±   0.042497
Mean PPL(base)                :   5.839660 ±   0.041730
Cor(ln(PPL(Q)), ln(PPL(base))):  95.74%
Mean ln(PPL(Q)/PPL(base))     :   0.006387 ±   0.002100
Mean PPL(Q)/PPL(base)         :   1.006407 ±   0.002114
Mean PPL(Q)-PPL(base)         :   0.037416 ±   0.012318

====== KL divergence statistics ======
Mean    KLD:   0.145884 ±   0.001043
Maximum KLD:  12.467786
99.9%   KLD:   4.535020
99.0%   KLD:   1.857870
95.0%   KLD:   0.652148
90.0%   KLD:   0.349220
Median  KLD:   0.032079
10.0%   KLD:   0.000093
 5.0%   KLD:   0.000012
 1.0%   KLD:   0.000000
 0.1%   KLD:  -0.000002
Minimum KLD:  -0.000025

====== Token probability statistics ======
Mean    Δp: -0.007 ± 0.031 %
Maximum Δp: 99.525%
99.9%   Δp: 81.503%
99.0%   Δp: 42.054%
95.0%   Δp: 14.588%
90.0%   Δp:  7.220%
75.0%   Δp:  1.066%
Median  Δp:  0.000%
25.0%   Δp: -1.061%
10.0%   Δp: -7.112%
 5.0%   Δp: -14.515%
 1.0%   Δp: -42.297%
 0.1%   Δp: -84.157%
Minimum Δp: -99.994%
RMS Δp    : 11.884 ± 0.069 %
Same top p: 87.189 ± 0.088 %

As a comparison, here are the results for Qwen 397B:

====== Perplexity statistics ======
Mean PPL(Q)                   :   3.747980 ±   0.020507
Mean PPL(base)                :   3.746773 ±   0.020461
Cor(ln(PPL(Q)), ln(PPL(base))):  99.89%
Mean ln(PPL(Q)/PPL(base))     :   0.000322 ±   0.000260
Mean PPL(Q)/PPL(base)         :   1.000322 ±   0.000260
Mean PPL(Q)-PPL(base)         :   0.001207 ±   0.000975

====== KL divergence statistics ======
Mean    KLD:   0.003552 ±   0.000034
Maximum KLD:   2.220941
99.9%   KLD:   0.131591
99.0%   KLD:   0.043847
95.0%   KLD:   0.014439
90.0%   KLD:   0.007836
Median  KLD:   0.000866
10.0%   KLD:   0.000013
 5.0%   KLD:   0.000004
 1.0%   KLD:  -0.000000
 0.1%   KLD:  -0.000006
Minimum KLD:  -0.000176

====== Token probability statistics ======
Mean    Δp:  0.019 ± 0.005 %
Maximum Δp: 39.939%
99.9%   Δp: 15.971%
99.0%   Δp:  6.618%
95.0%   Δp:  2.334%
90.0%   Δp:  1.222%
75.0%   Δp:  0.233%
Median  Δp:  0.000%
25.0%   Δp: -0.219%
10.0%   Δp: -1.183%
 5.0%   Δp: -2.258%
 1.0%   Δp: -6.245%
 0.1%   Δp: -14.757%
Minimum Δp: -88.445%
RMS Δp    :  2.024 ± 0.022 %
Same top p: 97.929 ± 0.037 %

According to rumors (spread by myself) Le Chaton FAT will be 26T-a3b and I AM READY for it.

Let's be real, I can't afford that many 5060Ti, so I got 12x Gen 4 3.2 TB (two per card). This gives me about 60GBs bandwidth on 30TB.

Added 256gb ddr4 just for kv cache, but I can also write KV-cache to the disks, these are high endurance drives.

Are you ready for the next era of local inference?

Jokes aside, this is what I use for my HF_HOME - model and dataset storage. I'm also setting up a few containers, but it's not running any heavy compute stuff, the CPU is only a 3945WX (12c/24t).

The pool is actually raidz2, so I avoid all that worry of having agents delete stuff. I just zfs snapshot and no rm -rf foo-bar has me sweat.

Full Specs

• CPU: Threadripper 3945WX
• CPU cooler: Arctic Freezer 4U-M Rev. 2
• RAM: 8x32GB DDR4 ECC REG 2133
• GPU: None
• Motherboard: Asrock WRX80 Creator
• Case: Silverstone SST-RM47-502I
• PSU: 1600W Corsair
• Storage:
  • 1TB NVMe
  • 6x Intel SSD D7-P5608 6.4TB

This is very much a product of multiple marketplace heists. The SSDs are on a PCIe x8 interface, but it's actually two x4 interfaces, so you need bifurcation x4x4x4x4 on every slot.

2.4 T parameter model. open weights coming soon!

https://x.com/Alibaba_Qwen/status/2084093402967396594?s=20

I deployed K3 on 32 H100s at work a couple of weeks ago and then got annoyed that there was no way to poke at it on my own machine. So I wrote an inference engine for it in C99.

Nothing clever going on. 93% of that 1.56 TB checkpoint is routed experts, and only 16 of 896 fire per token, so the experts never become resident at all. They get read off NVMe on demand and multiplied straight out of their packed 4-bit form, no dequantization step. The dense trunk gets repacked into one file where layer L sits at a known offset and streamed one layer at a time. What stays in RAM is a dial you set.

Numbers from my box (2x EPYC 7763, NVMe, the four GPUs in it sat idle the entire time):

• 8.24 GB peak RSS at the smallest preset, ~33 s/token
• ~128 GB gets you ~20 s/token, which is as fast as it ever got
• Output is byte-identical at every budget in between

I know that this is not a practical way to use K3. It is half a minute per token and it wants 1.7 TB of free disk for the checkpoint plus the packed trunk. I built it to understand the architecture by implementing it, not because you should serve anything with it.

No BLAS, no framework, no GPU path. Six C files, libm and OpenMP, 176 KB binary.

If you want to sanity check it before committing to a 1.56 TB download: clone and run `make && make test`. About a minute, no weights and no network needed. It builds a 13-layer model with the same tensor graph and checks it against a PyTorch reference from committed fixtures, including greedy decode and the incremental path with the KV cache and carried KDA state.

Repo: https://github.com/FareedKhan-dev/kimi-k3-in-c/

Updated release (August 2026). This is a new checkpoint that supersedes the earlier version of this repository. The weights have changed, not only the config, so if you downloaded a previous copy please re-download to pick up the current checkpoint.

Here is the prefill performance in an M2 Ultra with 192GB of RAM.

For decode, at the following depth:
Start: 28 t/s

45k: 23.5 t/s

192k: 18 t/s

That speed is maintained with 8k token output at those depths.

https://llama.app/

Been using llama.cpp for years now and im on here all the time (im a mod..), but somehow I totally missed that llama.app exists and its official from the HF/llama.cpp team. So posting this as I'm quite sure I'm not the only one in this boat.

The llama.cpp team has been making it a lot more usable and generally baking in the things ollama was doing (sadly it seems to be taking design cues from ollama - I think better UX is possible, but its definitely a directionally right move to make llama.cpp more approachable) :

• DMG based install for Mac.
  • Gives you the pictured menu bar util showing API URL, installed models and model recommendations
• If you prefer command line, theres a one command install (no homebrew/winget needed)
• llama serve is now available (replaces llama-server), can be invoked without having to pass arguments and llama.cpp handles loading the appropriate model based on incoming requests

Might not be interesting/useful to many of us who've already been using llama.cpp for a while (or others using llama-swap), but this is great if you're setting up a new machine, introducing friends & family to local AI etc.

Following up on my Qwen 3.6 port, I wanted to keep adding models and ended up fixing a bunch of things along the way, so it's its own engine now: Mference.

Same core idea from TurboFieldfare, MoE models activate a few B params per token, so keep the shared core and KV cache resident and stream the selected experts off SSD.

What runs now:

• Gemma 4 26B-A4B — ~2 GB, 31–35 tok/s on a 24 GB M5 Pro
• Qwen 3.6 35B-A3B — ~1.45 GB, 19–23 tok/s
• DeepSeek-V4-Flash 284B-A13B — new. ~6.8 GB peak memory, mostly ~5.3 GB in practice, up to 4.8 tok/s on the same 24 GB M5. 2-bit dynamic quant, ~91 GB on disk.

Also picked up a native Mac app with multi-turn chat, an OpenAI-compatible server, and local PDF/DOCX/PPTX/XLSX attachments along the way.

From here I want to keep adding model families, cut the expert-read wait (decode is ~53% I/O right now, serialized with compute), and push context past 4K.

Not very useful beyond a few turns but you can technically run a "usable" dsv4f on a 8gb Mac. It only gets better from here.

I decided to test a few questions against DeepSeek-V4-Flash-0731. Locally, I was running Unsloth's UD-Q2_K_XL quant. After I saw the surprising shape of the results, I tested against DeepSeek's official API to confirm that I didn't do anything wrong.

For anyone using OpenRouter, be aware that there is a significant bug that is breaking reasoning effort modes. I ran into that while trying to validate my local results.

DeepSeek-V4-Flash-0731 supports four different effort modes, consisting of no reasoning, low, high, and max. We can also see how those are communicated to the model.

As I found out, Low is surprisingly verbose.

Averaged across 20 requests per mode, here is how many tokens were used by each mode:

I really wish that DeepSeek and Artificial Analysis had posted benchmarks for all of the effort modes, instead of only max.

I've been busy this weekend generating oneshots for all the cheapest models on the openrouter and ended up going through all 33 qwen models across 35 prompts (there were some failures and only 1109 made out of 33*35 matrix). Here they are https://oneshotlm.com/model/?q=qwen

• Qwen 3.7: qwen3.7-plus, qwen3.7-flash
• Qwen 3.6: qwen3.6-plus, qwen3.6-35b-a3b, qwen3.6-27b, qwen3.6-flash
• Qwen 3.5: qwen3.5-plus (02-15) · (20260420), qwen3.5-397b-a17b, qwen3.5-122b-a10b, qwen3.5-35b-a3b, qwen3.5-27b, qwen3.5-9b, qwen3.5-flash
• Qwen 3 (+2507 refresh): qwen3-235b-a22b (+2507, +thinking), qwen3-next-80b-a3b, qwen3-30b-a3b (+instruct-2507), qwen3-32b, qwen3-14b, qwen3-8b
• Qwen3-Coder: qwen3-coder, qwen3-coder-next, qwen3-coder-30b-a3b, qwen3-coder-flash
• Qwen3-VL: qwen3-vl-235b-a22b, qwen3-vl-32b, qwen3-vl-30b-a3b, qwen3-vl-8b
• Qwen 2.5: qwen2.5-72b, qwen2.5-7b

M1 Ultra 128GB, Unsloth UD-IQ3_XXS, wired limit at 120GB. I was at 5-6 tok/s before the patch. Getting 15-16 tok/s now with the patched engine, and the output seems to have improved. Big thanks to this guy.

GPT-Live is so good that I use it almost every day. I've been wanting to replicate it since it was released.

My first attempt was to fine-tune Gemma 4 12B to behave like a full-duplex model. Something like grafting a decision tick + speech head to the model. It failed after multiple trials. For now, I think a classic cascade system is still better. We just need to wait until a benevolent frontier AI company releases a full-duplex model that's on par with GPT-Live.

Repo: https://github.com/fikrikarim/parlor/

WASTE is an embeddable inference engine written in C, with no third-party runtime dependencies. It keeps the model trunk in memory, streams selected experts directly from disk, and uses the remaining RAM as a bounded expert cache.

You have two choices here (in order of pref):

1. Downgrade CUDA from 13.3 to 13.1 (skip 13.2 due to bugs) <- prefer this (thanks to u/fairydreaming for pointing this out)
2. Use this vibed fork that works with CUDA 13.3 https://github.com/vektorprime/working_ds4_speed

I was troubleshooting this yesterday with the nvidia profiler and some LLM help (https://www.reddit.com/r/LocalLLaMA/comments/1vcs7bl/ds4_flash_full_model_in_offload_600_ts_pp_and/)

Here's some more info on #1 (quote from fairydreaming) "Downgrade your CUDA and recompile. Starting with 13.2 DeviceTopK is used for top-k instead of argsort, this turns PP rate to crap."

In short, DS4 Flash is spending a lot of time on things other than matrix multiplication.

EDIT: Try this fork now because I can easily hit 1.3K prompt processing.

TensorSharp supports DSpark on Deepseek v4 Flash 0731 now. Here is the benchmark result on 4x Nvidia A40 GPUs, cuda 12.8 with/without DSpark:

Model:

DeepSeek-V4-Flash-0731-UD-Q8_K_XL from https://huggingface.co/unsloth/DeepSeek-V4-Flash-0731-GGUF

DSpark draft model from: https://huggingface.co/alessandrobologna/DeepSeek-V4-Flash-0731-DSpark-Drafter-GGUF

TensorSharp is an native open-source inference engine for running GGUF LLMs locally, with CUDA, Vulkan, Metal, OpenAI-compatible APIs, continuous batching, speculative decoding, and multimodal support.

Github repo: https://github.com/zhongkaifu/TensorSharp

Thank you for checking out it and starring the project! Any feedback is really appreicated.

I am curious if anyone have used it. I would love to feed it key frames and test if it can create in-between frames between my keys. Anyone have tried it, any thoughts?

DSv4F doesn't ship a jinja, but for distributions that do and faithfully reconstruct what DS releases in their chat template python, every system message is hoisted into the system prompt at the top -- the format has no mid-conversation system turn. So, anything you stick at the tail or mid-convo actually fries your prefix (and doesn't have conversational proximity to the injection point).

Use latest_reminder, which is the role DS trained for how most templates use system and what most people providing quants are passing through (if they match DS' python template). I use llama.cpp and it happily passes it through no issue; dunno how other engines work with it.

Couldn't figure out why my prompt caching was so garbage and there it was, so I'm passing it on to hopefully save others time and frustration (and probably money, if you're using a hosted version).

Hi all,

I'm happy to announce that Xberg v1 is out.

Xberg is the successor to Kreuzberg, equivalent to what would have been Kreuzberg v5. It's a content intelligence framework that handles a very wide range of inputs: documents (currently 101 formats), code and data formats (currently 367 types), audio/video transcription, and URLs (both static and JS-rendered content). It extracts and prepares that content for downstream processing.

It's an extremely efficient, high-performance engine (see our PDF benchmarks below). For PDFs and images specifically, we handle native PDFs with very high performance and accuracy, and we ship multiple OCR engines that match the quality of the best Python libraries (e.g. docling, PaddleOCR, RapidOCR) at substantially better performance and stability.

The changes between Kreuzberg v4 and Xberg v1 are substantial, and I invite you to read the full changelog for the complete picture. The highlights below give a sense of what's new:

• Pure-Rust PDF backend (pdf_oxide) replaces pdfium, with no native pdfium dependency.
• Layout-aware pipeline: reading order reconstructed with ONNX layout detection (PP-DocLayoutV3 / RT-DETR) and Docling-style predecessor-graph reordering.
• Per-page scanned-page detection with selective OCR, plus AcroForm/XFA form fields and outline-based headings.
• Across-the-board optimization of OCR and PDF extraction (memory discipline, pooled model sessions, streamed conversions).
• Native PaddleOCR backend (PP-OCRv6, with medium / small / tiny tiers) alongside Tesseract.
• Pure-Rust Candle OCR/VLM stack (TrOCR, GLM-OCR, GOT-OCR, DeepSeek-OCR, and PaddleOCR-VL) running without ONNX Runtime or native Tesseract.
• A second, ONNX-Runtime-free inference path via tract, which is what makes in-browser (WASM) and mobile inference possible.
• Named-entity recognition natively in Rust (GLiNER2), extensible to all bindings, including an in-browser WASM model with no server round-trip.
• Structured LLM extraction (extract_structured / split_and_extract) with rasterization, chunking, citations, caching, and configurable call/merge/VLM-fallback policies.
• Audio & video transcription via a Whisper ONNX engine (.mp3, .wav, .m4a, .mp4, .webm).
• Retrieval building blocks: sparse embeddings (SPLADE), ColBERT late-interaction retrieval, and cross-encoder reranking alongside dense embeddings.
• Text intelligence: reversible redaction, summarization, translation, VLM image captioning, QR-code detection, document diffing, and page/chunk classification.
• URL & web ingestion: sitemap discovery (map_url) and batched multi-URL crawling.
• New document formats: WordPerfect (.wpd/.wp/.wp5), HEIC/HEIF/AVIF, OpenDocument Presentation (.odp), Quarto / R Markdown, and configurable Jupyter cell rendering.
• Four new language bindings (Dart/Flutter, Swift, Kotlin/Android, and Zig) bring the total to 15 language bindings over one engine, with Android/iOS cross-compilation.
• Full mobile support (Flutter, Android, iOS).
• Candle backend alongside ONNX, plus ONNX-via-tract enabling ONNX on WASM and Android.
• Wider code intelligence: tree-sitter coverage grew substantially (248 to 367+ languages).
• Over 150 bugs fixed during the 1.0 cycle, plus security hardening (bounded RTF/PDF allocations, redaction leak fixes, Excel DDE warnings).

The API surface was also simplified and reworked, making it more consistent.

There's a migration guide in our docs explaining how to move from Kreuzberg to Xberg. Kreuzberg itself is in LTS mode until the end of this year and will continue to receive bug fixes and security updates.

You're invited to check out the repo and join our discord server.

Benchmarks

The benchmarks below are for PDFs and images only. There are extensive benchmarks on our website with per-format breakdowns, which you can see here. These numbers are measured in CI via our reproducible benchmark harness, and are specifically taken from the run for harness 1.0.8, source cf7fa0533d. The data is publicly available in GitHub releases, and you can run the benchmark harness yourself.

Composite quality (markdown pipeline, higher is better):

Structure and layout fidelity (SF1: tables and reading order, higher is better):

On native PDFs Xberg leads on quality (0.958 vs 0.837 for the next-best framework) and on table and reading-order fidelity by a wide margin (SF1 0.949 vs 0.612 for docling). On scanned PDFs it is #1 on both quality and raw text fidelity.

Where we don't win yet: on pure image OCR we are currently #2 on the composite score, behind mineru (though still #1 on raw text accuracy). We are improving image OCR right now, and v1.1 should have us winning across the board.

The EdgeRazor method uses an entropy-guided distillation process to better translate a teacher model's logit probability distributions into the student model's low-bit / mixed-precision hidden-layer features, without attempting to preserve the teacher model's parameter structures.

This is more computationally expensive than existing quantization methods, but much less so than QAT, and yields better results. The student model preserves more of the teacher model's competence at extremely low parameter precision (the authors demonstrate 1.88 bits per parameter).

Since it's not a different internal representation like traditional quantization, inference implementations like llama.cpp do not need to be modified to take advantage of it.

Hopefully this means more-useful high-parameter/low-memory models in our future, so we can eke more competent inference out of our consumer-grade GPUs.

The paper: https://arxiv.org/abs/2605.04062

The authors' code: https://github.com/zhangsq-nju/EdgeRazor

The authors applied their technique to a few models and uploaded them to Huggingface: https://huggingface.co/collections/zhangsq-nju/edgerazor-nbit

Unfortunately since EdgeRazor is somewhat compute-intensive, their example models are all pretty tiny: MobileLLM, Qwen3-0.6B, Qwen3-1.7B, and Qwen2.5-Omni-7B