This is a submission for the Gemma 4 Challenge: Write About Gemma 4
I set the agent running just before midnight, did a quick mental count of my r...
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The MTP angle is interesting, haven't experimented with that yet. I've been focused on the vision side of Gemma 4 for edge deployment. Running detection tasks on a Raspberry Pi at 7.5W power draw. Curious how MTP performs on resource constrained hardware, have you tested it on anything smaller than a desktop?
I haven't tried on edge devices myself but there are small variants such as gemma4 e2b, and seems to be having some gains already. I read that the draft model for these as small as 70mb (if I recall correctly). Here are official numbers for smaller models on mobile gpu speed ups:
By nature of mtp you won't notice speed ups at image understanding initially but generation speeds (+reasoning) might get some small boosts...
Oh nice, 70MB for the drafter is way smaller than I expected. That actually makes me want to try pairing it with the E2B variant on the Pi and see if the reasoning step gets any faster. Right now my pipeline spends most of its time on the generation side after the image is already processed, so even a small boost there would be noticeable. That speed-up chart is really useful too, thanks for sharing it. The Pixel TPU and Apple M4 numbers are interesting, feels like on-device MTP is closer than most people think. Good point about image understanding not getting the speed-up though, that makes sense since MTP is about token prediction not the vision encoder. Appreciate the detailed answer!
local model fixes the quota problem, not the recovery problem. if there's no restart logic, you're still only as reliable as your weakest link
it's frustrating when the infrastructure you rely on fails unexpectedly, especially after investing time and resources. having control over your own setup can make a huge difference. with moonshift, you can deploy a full next.js + postgres + auth app in about 7 minutes, and the code is yours on github. if you're curious, I can set you up with a free run to give it a shot.
Speculative Decoding sounds cool, but I'm curious about real-world scenarios where it really beats the energy consumption issue. You mention using local models like Gemma 4 + MTP as more reliable for long-run tasks, but isn't there still a trade-off with local infrastructure demands? I've been using prachub.com for system design mocks, and they provide good insights on optimizing these setups. It's essential to find a balance between speed and resource use, right?
There are always trade off's with these ofc. But at the end you can also allocate better by forming more in depth scenarios where you can give the decision making to bigger models and leave the minions for hard working etc. A lot of dimensions to balance out depending on the task
Key lesson from running Gemma 4 27B (Q4_K_M) on long batch jobs: set --ctx-size explicitly rather than letting it auto-scale. The model will allocate context until the system starts swapping, dropping throughput from 15 tok/s to 2. Fixed context at 8192 for batch work, bump to 32K only when needed. MTP (multi-token prediction) shines most on repetitive structured output — generating test cases at roughly 1.8x faster than standard autoregressive decoding. Sprint vs marathon framing is the right mental model.
A local model that doesn't sleep as a marathon engine is a great framing, because it captures the real edge of self-hosted models that the benchmark-chasing crowd misses: it's not about beating the frontier on any single hard task, it's about zero-marginal-cost endurance, a model that can run continuously on the long tail of routine work without a meter ticking. That changes what you build, because once inference is effectively free you can afford to run it constantly (monitoring, drafting, triaging, pre-processing) in ways that'd be financially insane on a per-call API. The MTP angle is the right lever for that use case too, throughput is what makes a marathon engine viable, since endurance work is bottlenecked by tokens-per-hour, not peak smarts. The architecture this points at is the one I keep coming back to: the local model is the tireless workhorse for the cheap, high-volume majority, and you route the genuinely hard, judgment-heavy slice to a frontier model, so you get endurance and capability without paying frontier prices for the easy 80%. Run the local engine for the marathon, call the big model for the sprints. That right-size-and-route-by-endurance-vs-difficulty instinct is core to how I think about cost in Moonshift. What kind of long-running workload are you pointing the marathon engine at, monitoring/batch, or interactive work where the always-on matters?
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