4 ms·
Orthrus-Qwen3: up to 7.8×tokens/forward on Qwen3, identical output distribution
- FranckDernoncou 5mo agoPaper: https://arxiv.org/abs/2605.12825 https://arxiv.org/abs/2605.12825 ; Code+models: https://github.com/chiennv2000/orthrus https://github.com/chiennv2000/orthrus ; Disclosure: co-author. Idea: Inject a trainable diffusion attention module into each layer of a frozen AR Transformer. Both heads share one KV cache. Diffusion head projects K=32 tokens in parallel; AR head verifies in a second pass and accepts the longest matching prefix. Output distribution is provably identical to the base model. Results: - Up to 7.8x TPF, ~6x wall-clock on MATH-500. - 16% of params trained, <1B tokens, 24h on 8xH200. - vs. diffusion LMs (Dream, Fast-dLLM-v2, SDAR, Mercury, Gemini Diffusion): they modify base weights and lose accuracy (Fast-dLLM-v2: -11 pts on MATH-500). Orthrus freezes the backbone; accuracy matches Qwen3-8B exactly. - vs. Speculative Decoding (EAGLE-3, DFlash): no external drafter, no separate cache, zero TTFT penalty (no drafter to init/sync). KV overhead is O(1) (~4.5 MiB flat). Acceptance length on MATH-500: 11.7 vs. 7.9 (DFlash) vs. 3.5 (EAGLE-3). - Single-step denoising beats multi-step (6.35 vs. 3.53 TPF). KL distillation beats CE on acceptance rate. Limitations: strictly bounded by the frozen base model (inherits its biases, hallucinations, knowledge gaps); Qwen3-only evaluation; greedy + rejection sampling only.
- ilaksh 5mo agoAmazing. Is it possible to do this with Qwen 3.6 27B? Will it work with quants (I assume so)?
- sleepyeldrazi 5mo agoFrom a quick and shallow view of the paper, it looks very feasible (with a little tinkering ) to be adapted to qwen3.6 27B. The process looks somewhat similar to training a LoRA, or in a way distilling your own model so that a mini model learns how to imitate it, and you glue them. I might bite the bullet and rent a gpu to do it for 3.6 27b, as this will solve a lot of my problems.
- sleepyeldrazi 5mo agoScratch that, I don't have that kind of money, and 3.5's architecture is a little more divergent from 3's, so it will be a bit less trivial. It does look possible, just not on a student's paycheck.
- Boranbruh 5mo agoThere are websites that let you rent GPUs for cheap, such as QuickPod. Have you checked those P2P GPU rentals out?
- sleepyeldrazi 5mo agoMy plan is to validate it first using qwen3.5 0.8B if it even works (as it has the same architecture as qwen3.6 27b, just scaled down a bit) on my 3090. If it does, I'll make a git about the process if anyone wants to use my approach, while I try to convince my uni to lend me h100s for a day.
- sleepyeldrazi 5mo agoIf anyone is interested in watching my 0.8B experiments: https://orthrus.kokoham.com/ https://orthrus.kokoham.com/ . The current code is here: https://git.kokoham.com/sleepy/qwen_orthrus https://git.kokoham.com/sleepy/qwen_orthrus . The hard part was that the original Orthrus works with transformers, but 3.5(and 3.6) is Hybrid: 75% GatedDeltaNet + 25% GatedAttention. I am testing a trick that might make is work with the GatedDeltaNet, and dry runs are promising, but only a full train will reveal if it works. More information in the repo and on the site under the "What is this all about?" button. Note: i may restart it or try different configs at different points, if the site is down there is probably some sort of result/conclusion in the repo.
- dot_treo 5mo agoI would probably treat the (3 GatedDeltaNet + 1 GatedAttention) Blocks as one transformer block, when generating next steps one would therefore use the kv cache for the gated attention and skip the entire delta nets.
- 0-_-0 5mo ago3.6 already supports multi token generation AFAIK
- jbellis 5mo agoYes, but not diffusion based, it's still doing token-at-a-time speculation.
- 0-_-0 5mo agoI thought it can do multiple tokens at a time
- sleepyeldrazi 5mo agoThink of this as another way of achieving that. This theoretically has a higher ceiling of how much it can predict at a time. And more importantly is a lot more memory efficient during actual inference.
- regularfry 5mo agoThere was a chart from the Unsloth folks posted to Reddit in the last couple of days which showed that the draft sweet spot for MTP was 2-3 tokens ahead depending on the quant. Thats not much, and I think this might do a lot better. The whole "provably identical distribution" thing is doing a lot of work in my head, and I don't think that's true of the MTP model in qwen's architecture.
- littlestymaar 5mo agoSo, it's D-Flash but at each transformer layer and share the KV cache of the original model? Very smart!
- foobar10000 5mo agoKindof yeah - predictivity is a question though for larger layers - when trying to scale this up. But yeah, this is a "95% predictor in latent space is a 7x improvement in speed if done right" approach.
- dot_treo 5mo agoDo you plan on releasing the training code?
- jbellis 5mo agoReally cool work! Does the training data budget scale with model size? How would you compare the Gemma 4 draft model which is also integrated with the base kv cache?
- jbellis 5mo agoBTW the paper says > Since only (Qdiff,Kdiff,Vdiff) are updated during training, the total number of trainable parameters is approximately 16% of the full model. But the code defines q_proj_diff, k_proj_diff, v_proj_diff, and o_proj_diff, and it only matches 16% when you include the O term.
- gkapur 5mo agoOn the limitation side: Do you think this would scale to larger transformer models with more parameters per layer? How would this work with MOE models or sparse models?
- deflator 5mo agoI'm sure I don't understand all the technical aspects, but I do understand that this is frickin' cool. Nice work.
- xiphias2 5mo agoThe most interesting part of this idea for me is how it wasn't tried / implemented before, as it makes sense. I haven't read the paper but of course DTree tricks work here as well
- bertili 5mo agoDoes this translate into a similar reduction in compute? What's the catch?
- dot_treo 5mo agoIt is all about moving the bottleneck. During prompt processing everything can be calculated in parallel, while during token generation you create a single token at a time. For example, using an RTX 4000 Ada, I'm getting 2700 t/s for prompt processing, and 48 t/s for token generation using an 8B class model. Their approach is essentially a speculative decoding approach where multiple tokens are predicted at once and then verified. Therefore getting more tokens to be created at a speed that is closer to the prompt processing speed. It seems to be special because their approach yields the exact same output distribution as the base model and it only takes a negligable amount of additional memory. The main catch is that if your prompt processing speed is already bad, it will not help you all that much. For example, the M-series Macs (up to M4) have a relative high generation speed compared to their prompt processing speed. That means they will not benefit as much (if at all). With the M5 the prompt processing speed has increased 4x, so those can expect to see a good uplift.
- littlestymaar 5mo ago> Does this translate into a similar reduction in compute? No, quite the opposite actually. Like with speculative decoding this model will compute more tokens and discard the invalid ones. > What's the catch? LLMs[1] are limited by memory latency and not by compute[2]: because they process tokens one at a time, you spend more time loading and unloading the weights on the GPU registers from VRAM than waiting for compute to happen. Techniques like these allow to process multiple tokens in parallel instead of one by one, and as such exploit better the compute of your graphic card. They do so by predicting which tokens are likely to occur and then verifying that the guess was correct. For instance if the previous token is “hello”. A regular autoregressive LLM will compute: “hello” => “! ”, then “hello! ” => “how ”, “hello! how ” => “are ”, “hello! how are ” => “you”. and finally “hello! how are you” => “?<end>” One at a time. Loading and unloading every weights 5 times from the GPU memory to its compute units. With speculative decoding (I'd say this one isn't strictly speculative decoding, but it's a variant of the same principle), you have something that guesses that the whole sentence is going to be “how are you today?”, so the LLM can generate “hello” => “! ”, “hello! ” => “how ”, “hello! how ” => “are ”, “hello! how are ” => “you”. “hello! how are you” => “?<end>” “hello! how are you today” => “?<end>” In parallel. So each weight would have been loaded only once from the VRAM instead of 5. The last token will be discarded though, as the prefix “how are you today” doesn't match what has actually been generated. So in that particular example, you'd have gotten your 5 tokens 5 times faster than with pure autoregressive inference, but at the expense of a 6th token being generated and discarded immediately. So 5 times more token throughtput, but 20% compute cost increase per token. [1]: autoregressive LLMs, that is. Which are the ones everybody uses because they are the most performant. [2]: at least when run at low batch size, on your own computer for your personal use. On a datacenter, with many concurrent users, GPUs are actually compute-bound.
- DeathArrow 5mo agoIf someone can make this work with GGUF and Quantized Qwen 3.6 or Deepseek 4 it would greatly help running local models.
- hedgehog 5mo agoMulti-token prediction is available now, I'm still getting it set up but it sounds like it'll be 1.5x or 2x on the bigger models.
- dot_treo 5mo agoI've tried MTP, and that got me about 1.5x on average with a very spec friendly benchmark. I didn't run the full benchmark with the demo code, just picked up a single prompt from it. The prompt is about 1300 token, the response is about 3200 token. Baseline: 44.8 t/s With Orthrus: 164.6 t/s Note: Don't use the `use_diffusion_mode=` config flag in their example to collect a baseline. Something about how the fallback to "normal" makes it grind to a crawl.
- spwa4 5mo agoI don't understand. This distills a diffusion transformer out of Qwen3. And while the provably identical is nice, a full diffusion transformer would be a lot faster still.
- yorwba 5mo agoA full diffusion transformer would need more forward passes (thus being slower) or produce worse output (because it can't properly account for dependencies between tokens when generating them independently in parallel), or both. Keeping the output identical to the autoregressive baseline ensures the speedup doesn't come at the cost of quality degradation.
- holotherapper 5mo ago[dead]
- dnlserrano 5mo agoI wonder what our man @antirez will make of this
- GeorgeToresco 5mo ago[flagged]
- boredatoms 5mo agoSo will this help openai/anthropic have lower congestion in the afternoons if they implement something similar?
- pornel 5mo agoNo, it would make it worse. This adds more computation and sacrifices throughput to improve latency of a serial single-user generation. Large scale providers run inference in batches, sacrificing latency to gain throughput.
- yencabulator 4mo agoNon-predicted token generation requires num_of_tokens_output passes over the weights. Correctly-predicted token generation, requires num_of_tokens_output/prediction_size passes over the weights, plus a much smaller model to make those predictions. Incorrectly-predicted token generation adds some overhead to the above, relative to the hit rate. It sounds like good predictions would actually decrease the total overhead while improving latency. (Same FLOPs, but less memory bandwidth consumed -> probably run just as hot, but get more done.)
- p0w3n3d 5mo agoI hope it helps finally running large models on normal hardware. Tying our work to two companies in the world is a bad bad thing. Quite risky. Against any threat modelling
- deleted 5mo ago[deleted]