101–110 of 345 posts
MTP support is being addedto llama.cpp, at least for the Qwen models ( https://github.com/ggml-org/llama.cpp/pull/20533 ) and I'd imagine Gemma 4 will come soon. The performance uplift on local/self-hosted models in both quality and speed has been amazing in the last few months.
There is a newer PR which will probably be merged soon: https://github.com/ggml-org/llama.cpp/pull/22673
Earlier quoted context omitted.
Is it really no quality degradation? I'm curious where my understanding is wrong, but I didn't think you necessarily got the exact same output with how I understand speculative decoding to be used. I thought that if the small model produces tokens that are "good enough", meaning within the top few tokens the larger model produces, they're accepted. I thought it doesn't necessarily have to produce the exact same token…
It really is. This is because LLMs with a single output/user are strongly bandwidth limited. Although the hardware can generate multiple tokens simultaneously, it is slowed down if the tokens depend on each other, as is the case with regular text generation. The draft model essentially predicts the next token quickly, enabling you to start generating the subsequent token in parallel. If the guess is right, the second…
This is the crux. What makes the guess "right"?
I think the acceptance criteria is not that the token is exactly the token the big model would have produced. It's accepted of the big model verifies that the probability of that token was high enough.
How close it is to the same output (or same distribution of outputs) you'd get from running the big model would be dependent on temperature, top-k, top-p settings, or other inference parameters.
How is this different from the speculative decoding that we had before? You could pair a big and small model like qwen 32b with qwen 4b and had that same dynamic of the small model generating tokens and the big one "certifiying" them. The blog says something about re-using the big model's data?
I recently set up the 26B A4B model up on vLLM on an RTX3090 (4-bit) after a hiatus from local models. Just completely blown away by the speed and quality you can get now for sub-$1k investment. I tried first with Qwen but it was unstable and had ridiculously long thinning traces!
So this is like branch prediction for operating systems? Except we have probability baked into the model itself so it’s even more reliable.
How is this different from the speculative decoding that we had before? You could pair a big and small model like qwen 32b with qwen 4b and had that same dynamic of the small model generating tokens and the big one "certifiying" them. The blog says something about re-using the big model's data?
Google has now provided small models for each of the previous Gemma 4 models, e.g. "gemma-4-26B-A4B-it-assistant" for "gemma-4-26B-A4B-it".
The difference vs. Qwen is that here each small model is not some general-purpose smaller model, but a model that has been optimized specifically for this task, to predict the output of the bigger model with which it is paired.
This specialization and optimization of the Google "gemma-4-*-assistant" models ensures that they are much smaller and thus much faster than general-purpose small models.
Earlier quoted context omitted.
How does this get added in practice?
According to the linked PR, the original model does come with MTP which is another "head" (=output path) in the same model and (supposedly) runs faster. The current implementation ignores that head but the PR let the tool recognize it, plus does proper integration (run the MTP while running the slower main path then compare the result, I believe.)
Google is singlehandedly carrying western open source models. Gemma 4 31B is fantastic. However, it is a little painful to try to fit the best possible version into 24GB vram with vision + this drafter soon. My build doesn't support any more GPUs and I believe I would want another 4090 (overpriced) for best performance or otherwise just replace it altogether.
Qwen is still better that Gemma though. Also you can tune it more for different tasks, which means that you can prioritize thinking and accuracy versus inference speed.
Watching the computer write text sort of reminds me of using a modem to call a BBS in the old days. This seems like going from 300 baud to 1200 - a significant improvement, but still pretty slow, and someday we will wonder how we put up with it.
This is something I've been thinking about for a while...the current state of things really does feel kind of like the dialup era, wondering what the "broadband" era could look like. Watching tokens stream in is reminiscent of watching a jpeg load a few rows of pixels at a time, and the various different loading and connecting animations that applications implemented before things got fast enough to make them less re…
MTP support is being addedto llama.cpp, at least for the Qwen models ( https://github.com/ggml-org/llama.cpp/pull/20533 ) and I'd imagine Gemma 4 will come soon. The performance uplift on local/self-hosted models in both quality and speed has been amazing in the last few months.