6 ms·
GPT-OSS-120B runs on just 8GB VRAM & 64GB+ system RAM
- amelius 1y ago[flagged]
- deleted 1y ago[deleted]
- tyfon 1y agoI have a 5950x with 128 gb ram and a 12 gb 3060 gpu. The speed of generating tokens is excellent, the killer is that when the context grows even a little processing of it is super slow. Hopefully someone smart will optimize this, but as it is now I keep using other models like qwen, mistral and gemma.
- MaxikCZ 1y agoI would so appreciate concrete data instead of subjectivities like "excellent" and "super slow". How many tokens is excellent? How many is super slow? How many is non-filled context?
- HPsquared 1y agoPeople can read at a rate around 10 token/sec. So faster than that is pretty good, but it depends how wordy the response is (including chain of thought) and whether you'll be reading it all verbatim or just skimming.
- littlestymaar 1y ago> People can read at a rate around 10 token/sec. It really depends on the type of content you're generating: 10tk/s feels very slow for code but ok-ish for text.
- gtirloni 1y agoReading while words are flying by is really distracting. I believe it was mentioned at some point that 50t/s feels comfortable and ChatGPT aims for that (no source, sorry).
- tyfon 1y agoI'm not really timing it as I just use these models via open webui, nvim and a few things I've made like a discord bot, everything going via ollama. But for comparison, it is generating tokens about 1.5 times as fast as gemma 3 27B qat or mistral-small 2506 q4. Prompt processing/context however seems to be happening at about 1/4 of those models. A bit more concrete of the "excellent", I can't really notice any difference between the speed of oss-120b once the context is processed and claude opus-4 via api.
- lylejantzi3rd 1y agoI've found threads online that suggest that running gpt-oss-20b on ollama is slow for some reason. I'm running the 20b model via LM Studio on a 2021 M1 and I'm consistently getting around 50-60 T/s.
- idonotknowwhy 1y agoPro tip: disable the title generation feature or set it to another model on another system. After every chat, open webui is sending everything to llamacpp again wrapped in a prompt to generate the summary, and this wipes out the KV cache, forcing you to reprocess the entire context. This will get rid of the long prompt processing times id you're having long back and forth chats with it.
- qrios 1y agoSome numbers are posted in the comments: > … you can expect the speed to half when going from 4k to 16k long prompt … > … it did slow down somewhat (from 25T/s to 18T/s) for very long context … Depends on the hardware configuration (size of VRAM, speed of CPU and system RAM) and llama.cpp parameter settings, a bigger context prompt slows the T/s number significantly but not order of magnitudes. Facit: gpt-oss 120B on a small GPU is not the proper setup for chat use cases.
- captainregex 1y agoWhat are you aiming to do with these models that isn’t chat/text manipulation?
- jmkni 1y agoIf you run these on your own hardware can you take the guard-rails off (ie "I'm afraid I can't assist with that"), or are they baked into the model?
- stainablesteel 1y agothey're baked in but there's a community of people who crack and modify them even chat gpt will help you crack them if you ask it nicely
- hnuser123456 1y agoYou need to find an abliterated finetune, where someone sends prompts that would hit the guardrails, traces the activated neurons, finds the pathway that leads to refusal, and deletes it.
- generalizations 1y agoI've been hearing that in this case, there might not be anything underneath- that somehow OpenAI managed to train on exclusively sterilized synthetic data or something.
- gostsamo 1y agoI jailbroke the smaller model with a virtual reality game where it was ready to give me instructions on making drugs, so there is some data which is edgy enough.
- gchamonlive 1y agoIf you didn't validate the instructions, maybe it just extrapolated from the structure of other recipes and general description of drug composition which most likely is in Wikipedia.
- gostsamo 1y agomight be, I did it to check if it will activate the internal constraints. looked plausible enough.
- GTP 1y agoLLM noob here. Would this optimization work with any MoE model or is it specific for this one?
- magicalhippo 1y agoIt's just doing a regex on the layer names, so should work with other models as long as they have the expert layers named similarly. It worked with Qwen 3 for me, for example. The option is just a shortcut, you can provide your own regex to move specific layers to specific devices.
- nativeit 1y ago[flagged]
- MaxikCZ 1y agoYour comment will get donvoted to invisibility anyways (or mayhaps even flagged), but I have to ask: what are you trying to accomplish with comments such this? Just shitting at it because it isnt as good as youd like yet? You want the best of tomorrow today, and will only be rambling about how its not good enough yesterday?
- gjsman-1000 1y agoWell, now I have to ask, what your purpose on calling him out, is. Does it deeply offend you that non-believers exist, who do not believe the technology will improve substantially in usefulness from here?
- senko 1y agoNeither of them said any of that tho. Maybe the GP is just celebrating the unfathomable and beautiful complexity of life? We just can't know - which is why parent is asking.
- unethical_ban 1y agoDifferent person here. Snark is rarely as clear or straightforward as an honest comment. You read several meanings from that comment, which I would consider speculation. It's just as likely they're just being clever.
- Philpax 1y agoMeaningless noise that contributes nothing to the conversation offends me. Being a non-believer is fine, but do us the favour of having something interesting to say.
- fuzzer371 1y agoBecause it's never going to be good. People seem to have drank the kool aid that LLM's are the same as general AI and that its going to solve every single problem in the world. It's the same thing with the quantum computing and fusion reactor people.
- sunpazed 1y agoDon’t have enough ram for this model, however the smaller 20B model runs nice and fast on my MacBook and is reasonably good for my use-cases. Pity that function calling is still broken with llama.cpp
- tarruda 1y agoIt is fixed in this PR/branch: https://github.com/ggml-org/llama.cpp/pull/15181 https://github.com/ggml-org/llama.cpp/pull/15181
- codazoda 1y agoI'm glad to see this was a bug of some sort and (hopefully) not a full RAM limitation. I've used quite a few of these models on my MacBook Air with 16GB of RAM. I also have a plan to build an AI chat bot and host it from my bedroom on a $149 mini-pc. I'll probably go much smaller than the 20B models for that. The Qwen3 4B model looks quite good. https://joeldare.com/my_plan_to_build_an_ai_chat_bot_in_my_bedroom https://joeldare.com/my_plan_to_build_an_ai_chat_bot_in_my_b...
- tempotemporary 1y agowhat are your use cases? wondering if it's good enough for coding / agentic stuff
- p0w3n3d 1y agoI wonder if the mlx optimized would run on 64gb mac
- CharlesW 1y agoLM Studio's heuristics (which I've found to be pretty reliable) suggest that a 3-bit quantization (~50 GB) should work fine.
- qafy 1y agoYou can fine tune the amount of unified memory reserved for the system vs GPU, just search up `sysctl iogpu.wired_limit_mb`. On my 64gb mac mini the default out of the box is only like ~44gb available to the GPU (i forget the exact number), but tuning this parameter should help you run models that are a little larger than that.
- blmayer 1y agoI find it funny that people say "only" for a setup of 64GB RAM and 8GB VRAM. That's a LOT. I'd have to spend thousands to get that setup.
- deleted 1y ago[deleted]
- reedf1 1y agoGiven that this is at the middle/low-end of a consumer gaming setups - it seems particularly realistic that many people can run this out of the box on their home PC - or with an upgrade for a few hundred bucks. This doesn't require an A100 or some kind of fancy multi-gpu setup.
- 0cf8612b2e1e 1y agoNot that these specs are outrageous, but “middle/low” is underselling it. The typical PC gamer has a modest system, despite all the noise from enthusiasts. The Steam hardware survey puts ~5% of people with 64GB RAM or more https://store.steampowered.com/hwsurvey https://store.steampowered.com/hwsurvey
- hexyl_C_gut 1y agoI imagine steam survey has a long tail of old systems. I wonder what the average RAM capacity and other specs for computers from the past year, 3 years, etc.
- altcognito 1y agohttps://frame.work/products/desktop-diy-amd-aimax300/configuration/new https://frame.work/products/desktop-diy-amd-aimax300/configu... $1599 - $1999 isn't really a crazy amount to spend. These are preorder, so I'll give you that this isn't an option just yet.
- varispeed 1y agowhy is it called DIY?
- yieldcrv 1y agoI wonder if GPT 5 is using a similar architecture, leveraging all of their data center deployments much more efficiently, prompting OpenAI to want to deprecate the other models so quickly
- unquietwiki 1y agoIs there a way to tune OpenWebUI or some other non-CLI interface to support this configuration? I have a rig with this exact spec, but I suspect the 20B model would be more successful.
- leach 1y agoI'm a little confused how these models run/fit onto VRAM. I have 32gb system RAM and 16gb VRAM. I can fit the 20b model all within vram, but then I can't increase the context window size past 8k tokens or so. Trying to max the context size leads to running out of VRAM. Can't it use my system ram as backup though? Yet I see other people with less resources like 10GB of vram and 32gb system ram fitting the 120b model onto their hardware. Perhaps its because ROCm isn't really supported by ollama for RDN4 architecture yet? I believe I'm using vulkan to currently run and it seems to use my CPU more than my GPU at the moment. Maybe I should just ask it all this. I'm not complaining too much because it's still amazing I can run these models. I just like pushing the hardware to its limit.
- zozbot234 1y agoIt seems you'll have to offload more and more layers to system RAM as your maximum context size increases. llama.cpp has an option to set the number of layers that should be computed on the GPU, whereas ollama tries to tune this automatically. Ideally though, it would be nice if the system ram/vram split could simply be readjusted dynamically as the context grows throughout the session. After all, some sessions may not even reach maximum size so trying to allow for a higher maximum ends up leaving valuable VRAM space unused during shorter sessions.
- leach 1y agoAh I see interesting, I'll have to play around with this more. I switched from Nvidia to AMD and have found AMD support to still be rolling out for these new cards. I could only get LM studio working so far but I'd like to try out more front ends. Not a major setback because for long context I'd just use GPT or claude, but it would be cool to have 128k context locally on my machine. When I get a new CPU I'll upgrade RAM to 64, my GPU is more than capable of what I need for a while and a 5090 or 4090 is the next step up in VRAM but I don't want to shell out 2k for a card.
- anshumankmr 1y agoHas anyone got it to run on Macbook Air M4 (the 20B version mind you) and/or an RTX 3060?