One downside is your kernel isn't going to reserve that memory away from userland. You will still see all the memory at system level as "free". As the GPU driver starts using it, other apps/the OS will try to use the "free" memory, not knowing how much of it is in use (it may show up as "cache", or not at all). Then OOM killer starts going or programs start crashing, and at some point the OS tips over or GPU driver crashes. You can add loads of swap as a compromise and it works okay, if a bit slow.
In any case, loading a gigantic model just to use system RAM is absurdly slow (due to mem bandwidth), like 1-5 t/s, so it's not practical. It'd take a whole day to process one 86k token request. Just pay a cloud provider $0.01 to do it in 10 seconds.
The point is not how fast it is now. The point is that this opens new possibilities that can be built on. Potentially models that are trained with slightly different architectures to optimize to this use case. Possibly others come to improve this path. Possibly HW manufacturers make a few small adjustments that remove bottlenecks. Who knows, the next person may combine CPU compute with this mem sharing to get another token a second. Then the next person does predictive loading into memory to keep that bandwith 100% maxed and usable. Then the next does and the next does. Before you know it there is a real thing there that never existed.
This is a great project. I love the possibilities it hints at. Thanks for building it!
It’s architecturally not a good approach. System RAM is much slower so you should put data that doesn’t need to be used often on it. That knowledge is at the application layer. Adding a CUDA shim makes system RAM appear like VRAM, which gets things to run, but it will never run very well.
The benchmarks at the bottom mention memory tiering and manually controlling where things go, but if your application already does that, then you probably don’t also need a CUDA shim. The application should control the VRAM to system memory transfers with boring normal code.
Not true for unified systems. And for strix halo you need to dedicate the amount which is annoying.
You’re basically stating that swapping is also a bad idea.
And to take it further, any memory or storage is a bad idea because there’s L1 cache/SRAM which is faster then the rest
The fundamental problem here is that the workload of LLMs is (vastly simplified) a repeated linear read of all the weights, in order. That is, there is no memory locality in time. There is literally anti-locality; When you read a set of weights, you know you will not need them again until you have processed everything else.
This means that many of the old approaches don't work, because time locality is such a core assumption underlying all of them. The best you can do is really a very large pool of very fast ram.
In the long term, compute is probably going to move towards the memory.
The main blocker with swapping is not even the limited bandwidth, it's actually the extreme write workload on data elements such as the per-layer model activations - and, to a much lesser extent, the KV-cache. In contrast, there are elements such as inactive experts for highly sparse MoE models, where swapping makes sense since any given expert will probably be unused. You're better off using that VRAM/RAM for something else. So the logic of "reserve VRAM for the highest-value uses, use system RAM as a second tier, finally use storage as a last resort or for read-only data" is still quite valid.
It's not true for unified systems, because they have no secondary RAM that could be used to extend the GPU memory.
It's pretty weird to insist on a counterargument that has no implications or consequences to the presented argument.
Yes, swapping is a bad idea.
Your second argument also falls flat, because the standard CUDA hardware setup doesn't use CXL so cache coherence isn't available. You're left with manual memory synchronization. Pretending that GPUs have cache for system RAM when they don't is pretty suspect.
From my experience, accessing system RAM from the GPU is so slow, it might as well count as "does not work". It's orders of magnitudes faster to memcpy large swaths of memory that you are going to use to the GPU, rather than accessing system mem from a kernel which then takes ages to wait for that small block/page of memory, then waits again for the next small page/block of memory, etc. Latency hiding doesnt work anymore if the latency is that large.
> any case, loading a gigantic model just to use system RAM is absurdly slow (due to mem bandwidth), like 1-5 t/s, so it's not practical. It'd take a whole day to process one 86k token reques
So don't use it for large requests. Ideal for when you just want to categorise things, for example, "does this task need a shell" or "bucket this email into one of help request, bill due or personal comms".
The best use is actually for a layer that "almost fits" into VRAM, such that automated offloading to system RAM will be rare enough that it doesn't impact performance.
Speed and reliability. A connector of any kind reduces signal quality. Data lines need to be longer, because the memory slot won't fit under the radiator where the memory chips are now, and that adds even more electrical interference and degrades signal.
Also, we had memory slots on '90s cards. They were extremely expensive and proprietary. Ever saw a Matrox VRAM card? I never did.
This is really interesting engineering, but I agree with the other commentators that the benchmarking makes it hard to understand the contribution various factors are having.
The ExLlamaV3 EXL3 2bpw (8 GB, full VRAM) row is an order of magnitude faster than the baseline - but the baseline seems to be the 32GB model running with the KV cache shared to system memory only (I think?)
But if a 8GB model gives sufficient quality then it seems like that would have worked without the shared memory thing?
I think the useful apples-to-apples benchmark is currently the Ollama + GreenBoost shim (baseline) (2-5 tps) vs ExLlamaV3 + GreenBoost cache (8–20 tps) comparison.
It would be really useful to see this compared with the existing llama CPU/memory offload. There is a note at the start ("Offload layers to CPU — works, but drops token/s by 5–10× because CPU RAM has no CUDA coherence") - but it is unclear if that 5-10x token speed drop is compared to running a model completely in GPU or compared to the greenboost approach.
I think it is vs GPU, in which case it seems likely the performance is similar to what greenboost is giving but probably much more stable.
ExLlamaV3 EXL3 2bpw is likely the 30b parameter GLM 4.7 Flash quantised down to 2 bits, the unstated assumption is that you need to check the 2bpw quantisation works well enough for your use case.
The reported size of the ModelOpt FP8, 16 GB, sounds wrong to me. If its 8 bits per parameter it is going to be a similar size to the glm-4.7-flash:q8_0. They repeat this a few times in the readme.
> it can generate a 50 steps 512x512 image around 1 minute and 50 seconds.
I have the 4650G APU, and the best way to describe it is: lacking of support. This was even more true 3 yo than now. rocm (is) was absolutely dogshit then, I know this because I tried to do the same when that post was made. You have to compile everything from scratch, get the relevant patches, and even then, xformers which is a library that accelerate diffusion model inferencing was not supported for renoir or rocm back then. Yes, you can generate an image, but it was much slower, and rigged with bugs. You couldn'rt update rocm because it broke compatibility, and it was partly the reason I got into nixos. That being said, those APUs are a power house. Nowadays I can run decent agentic workflows on them (I have 64gb of ddr4 ram, ie APU can suck as much as it needs with the latest linux kernels).
Just note, diffusion models are still second class citizens on AMD apus even GPUs. But then again, nothing close right now on the market except for what apple offers.
The Ryzen AI CPU/GPUs (Ryzan AI 395+ etc) seem to have increasing support - https://lemonade-server.ai/ now has support for the NPU as well as the combined CPU/GPU (which I guess is a APU but is different to the G series of APUs I think?)
But I'm always interested in first hand experiences of how good is it really - I'm pretty cynical about the idea that AMD actually knows what it takes to build good software end-to-end.
I also have one, and indeed support is very much frictionless now compared to a year ago. But again, not thanks to AMD, as initially it was purely community driven. Strix halo was not even supported by ROCm (officially), and we had to deal with therock images, then donato made the toolbox, and then lemonade came through. I am really surprised how AMD approached this. They made big promises, they threw the hardware out, it really is amazing piece of hardware given what you can do with it, but it was left hanging without support for AI stack for months even though it had it in its name. Contrast that with the DGX spark (yes it had and still had bugs in its kernels, but cuda worked on day 1) and you can see the difference. Nvidia is selling an ecosystem, AMD is selling hardware. I really hope AMD focus on the software layer more.
Note that Lemonade Server uses NPU low-level code that's proprietary, not available as open source. It would be nice to work on a fully open alternative, perhaps by exposing the NPU itself as a Vulkan Compute-capable device, that shaders can be auto-compiled to.
How does this differ from anything llama.cpp offers, regarding offloading layers? The repo consistently refers to "DDR4". Is there a reason DDR5 won't work with this?
I was wondering the same, but llama.cpp was written to offload to system ram. If this really works, then the advantage could be that one could run transformers / sglang, etc or other tools that don't offload to system ram. However, I want to see the numbers. Perhaps I'll give this a try, but I need a throw away box I could trash if something goes wrong, but have none at the moment.
> I have an RTX 5070 with 12 GB VRAM and I wanted to run glm-4.7-flash:q8_0, which is a 31.8 GB model. The standard options are:
> Offload layers to CPU — works, but drops token/s by 5–10× because CPU RAM has no CUDA coherence. You end up waiting.
Use a smaller quantization — you lose quality. At q4_0 the model is noticeably worse on reasoning tasks.
> Buy a bigger GPU — not realistic for consumer hardware. A 48 GB card costs more than a complete workstation.
> None of those felt right, so I built an alternative: route the overflow memory to DDR4 via DMA-BUF, which gives the GPU direct access to system RAM over PCIe 4.0 without a CPU copy involved.
And then limps home with this caveat on the closest thing to a benchmark:
> The PCIe 4.0 link (~32 GB/s) is the bottleneck when the model overflows VRAM. The best strategy is to shrink the model until it fits — either with EXL3 quantization or ModelOpt PTQ — and use GreenBoost's DDR4 pool for KV cache only.
I think the reason it refers it to DDR4 is because that is how the user explained it to their coding agent. LLMs are great at perpetuating unnecessary specificity.
Given that 32 GB/s is significantly worse than CPU to RAM speeds these days, does the additional compute really make it any faster in practice? The KV cache is always on the GPU anyway unless you're doing something really weird, so it won't affect ingestion, and generation is typically bandwidth bound. With something like ×16 PCIe 6.0 it would actually make sense, but nothing less than that, or maybe for smaller dense models that are more compute bound with 8x PCIe 6.0 or 16x 5.0 but that's already below DDR5 speeds.
Additional compute is generally a win for prefill, while memory bandwidth is king for decode. KV cache however is the main blocker for long context, so it should be offloaded to system RAM and even to NVMe swap as context grows. Yes that's slow on an absolute basis but it's faster (and more power efficient, which makes everything else faster) than not having the cache at all, so it's still a huge win.
CUDA has had managed memory that pages between VRAM and system RAM for a decade. Problem is doing so is unusably slow for AI purposes. Seems like an unnecessary layer here.
That slowness is almost useful. It makes the failure mode obvious instead of letting a 'transparent' layer hide it until some sloppy alloc or tensor blowup starts paging through system RAM or NVMe and the whole job turns into a smoke test for your storage stack.
For actual training, explicit sharding and RAM mapping are ugly, but at least you can see where the pressure is and reason about it. 'Transparent' often just means performance falls off a cliff and now debugging it sucks.
Presumably it means that software doesn’t have to write the same sort of layer offloading support. It’ll “just work” as if you had X GB of VRAM all along.
> The best strategy is to shrink the model until it fits — either with EXL3 quantization or ModelOpt PTQ — and use GreenBoost's DDR4 pool for KV cache only.
Does this make sense? I'd have thought the KV is guaranteed to be used 100% of the time while say in a MoE the same can't be said of the weights.
Though I suppose if you're shooting for huge context then having that allocation go into ram makes sense specially when its allocated but not used yet
KV cache is, well, a cache that can fill up and trigger eviction. You require enough space to execute at least 1 fwd pass of 1 request at your context length. KV cache hits reduce TTFT by avoiding prefill. You don’t get to skip decode.
MoE is kinda related in terms of lower usage requirements vs a dense model of same total param size, but I think your mental model is a bit off.
KV cache is also eminently swappable if you have fast storage, since it mostly sees small append-only writes per token - it's not rewritten continuously like the activations. (I believe it's even better if you use cached input tokens across requests, since that portion of KV cache can then be recycled and save a single ~KV-cache sized write per request.) Accessing swapped-out cache may be slow, but it's highly preferable to not having that cache amount at all and recomputing from scratch.
The physical bottleneck to system memory remains. Therefore, I assume that better results are achieved by manually adjusting which layers are offloaded.
I would prefer to use system memory to cache different models, focusing on things like embedding, rerankers, and TTS. This is sufficient to run a more complex RAG locally, for example, via Mem0, and then use a larger LLM via the cloud.
This has been fun we can task our nemotron-3-super model to run over night when our desktops are idle. 4070s and 96gb of ram works fine. Slow but does it's job.
You can of course monitor SMART wearout indicators to check whether this is happening. Casual use of swap for non LLM-use is actually fine since "cold" ephemeral data will be swapped out first and that will never get written to; KV cache is mostly fine since it's similarly append-only so writes are tolerably small; but yes, more general LLM inference totally breaks that limited-writes pattern and will wear out/kill your media.
> Do not put swap on an SSD you care about at all.
This.
Many people rediscovering what the purpose of swap files are, but will still find a way to abuse it without knowing that they are actually destroying their SSD.
Could be a very useful way to do some overnight tasks using spare RAM. Possibly things like LLM-based categorisation, labelling, data cleansing. That's what comes to mind for me anyway.
Doesn't Windows already do this by default? I can already run models bigger than my GPU VRAM and it will start using up to 50% of my system RAM as "shared memory". This is on a Desktop PC without a shared memory architecture.
Yep I had a GeForce 750 Ti (2 GB) and I was able to run a ton of things on Windows without any issues at all.
As soon as I switched to Linux I had all sorts of problems on Wayland where as soon as that 2 GB was reached, apps would segfault or act in their own unique ways (opening empty windows) when no GPU memory was available to allocate.
Turns out this is a problem with NVIDIA on Wayland. On X, NVIDIA's drivers act more like Windows. AMD's Linux drivers act more like Windows out of the box on both Wayland and X. System memory gets used when VRAM is full. I know this because I got tired of being unable to use my system after opening 3 browser tabs and a few terminals on Wayland so I bought an AMD RX 480 with 8 GB on eBay. You could say my cost of running Linux on the desktop was $80 + shipping.
It's the drivers but it was a relatively recent addition, think it was added when either the 30xx or 40xx series shipped and the lower cards had pitiful VRAM so they enabled it by default so they'd work with all games.
Most people who know it does this turns it off because it kicks in too early so if you have 24GB it'll offload to RAM and tank your inference speed when you hit around 22GB use.
Nobody mentioning how this project is vibecoded slop?
> The code is really bad with completely uneeded parts. The LLM (Qwen 2.5 7B) has hardcoded the i9 14700KF topology, and has variables related to it never used... It's even funnier that the show hardware function always prints the same string. There are even random pip log files. Why did this slop got coverage here?
Some simpler benchmark table would be great. May I suggest Ollama on base machine, Ollama with T1, Ollama with T1+T2 etc. on midsize and big models to compare token/sec?
In any case, loading a gigantic model just to use system RAM is absurdly slow (due to mem bandwidth), like 1-5 t/s, so it's not practical. It'd take a whole day to process one 86k token request. Just pay a cloud provider $0.01 to do it in 10 seconds.
This is a great project. I love the possibilities it hints at. Thanks for building it!
The benchmarks at the bottom mention memory tiering and manually controlling where things go, but if your application already does that, then you probably don’t also need a CUDA shim. The application should control the VRAM to system memory transfers with boring normal code.
You’re basically stating that swapping is also a bad idea. And to take it further, any memory or storage is a bad idea because there’s L1 cache/SRAM which is faster then the rest
The fundamental problem here is that the workload of LLMs is (vastly simplified) a repeated linear read of all the weights, in order. That is, there is no memory locality in time. There is literally anti-locality; When you read a set of weights, you know you will not need them again until you have processed everything else.
This means that many of the old approaches don't work, because time locality is such a core assumption underlying all of them. The best you can do is really a very large pool of very fast ram.
In the long term, compute is probably going to move towards the memory.
Is that a crazy thing to say? I can't recall the last time I was grateful for swap; it might've been before 2010.
It's pretty weird to insist on a counterargument that has no implications or consequences to the presented argument.
Yes, swapping is a bad idea.
Your second argument also falls flat, because the standard CUDA hardware setup doesn't use CXL so cache coherence isn't available. You're left with manual memory synchronization. Pretending that GPUs have cache for system RAM when they don't is pretty suspect.
So don't use it for large requests. Ideal for when you just want to categorise things, for example, "does this task need a shell" or "bucket this email into one of help request, bill due or personal comms".
Also, we had memory slots on '90s cards. They were extremely expensive and proprietary. Ever saw a Matrox VRAM card? I never did.
In general soldered ram seems to get much higher bandwidth than removeable ram. See ryzen AI Max vs 9950x max ram throughputfor example
The ExLlamaV3 EXL3 2bpw (8 GB, full VRAM) row is an order of magnitude faster than the baseline - but the baseline seems to be the 32GB model running with the KV cache shared to system memory only (I think?)
But if a 8GB model gives sufficient quality then it seems like that would have worked without the shared memory thing?
I think the useful apples-to-apples benchmark is currently the Ollama + GreenBoost shim (baseline) (2-5 tps) vs ExLlamaV3 + GreenBoost cache (8–20 tps) comparison.
It would be really useful to see this compared with the existing llama CPU/memory offload. There is a note at the start ("Offload layers to CPU — works, but drops token/s by 5–10× because CPU RAM has no CUDA coherence") - but it is unclear if that 5-10x token speed drop is compared to running a model completely in GPU or compared to the greenboost approach.
I think it is vs GPU, in which case it seems likely the performance is similar to what greenboost is giving but probably much more stable.
The reported size of the ModelOpt FP8, 16 GB, sounds wrong to me. If its 8 bits per parameter it is going to be a similar size to the glm-4.7-flash:q8_0. They repeat this a few times in the readme.
"I turned a $95 AMD APU into a 16GB VRAM GPU and it can run stable diffusion!"
I have the 4650G APU, and the best way to describe it is: lacking of support. This was even more true 3 yo than now. rocm (is) was absolutely dogshit then, I know this because I tried to do the same when that post was made. You have to compile everything from scratch, get the relevant patches, and even then, xformers which is a library that accelerate diffusion model inferencing was not supported for renoir or rocm back then. Yes, you can generate an image, but it was much slower, and rigged with bugs. You couldn'rt update rocm because it broke compatibility, and it was partly the reason I got into nixos. That being said, those APUs are a power house. Nowadays I can run decent agentic workflows on them (I have 64gb of ddr4 ram, ie APU can suck as much as it needs with the latest linux kernels).
Just note, diffusion models are still second class citizens on AMD apus even GPUs. But then again, nothing close right now on the market except for what apple offers.
But I'm always interested in first hand experiences of how good is it really - I'm pretty cynical about the idea that AMD actually knows what it takes to build good software end-to-end.
But yes I agree with you about their lack of prioritization for software!
> I have an RTX 5070 with 12 GB VRAM and I wanted to run glm-4.7-flash:q8_0, which is a 31.8 GB model. The standard options are:
> Offload layers to CPU — works, but drops token/s by 5–10× because CPU RAM has no CUDA coherence. You end up waiting. Use a smaller quantization — you lose quality. At q4_0 the model is noticeably worse on reasoning tasks.
> Buy a bigger GPU — not realistic for consumer hardware. A 48 GB card costs more than a complete workstation.
> None of those felt right, so I built an alternative: route the overflow memory to DDR4 via DMA-BUF, which gives the GPU direct access to system RAM over PCIe 4.0 without a CPU copy involved.
And then limps home with this caveat on the closest thing to a benchmark:
> The PCIe 4.0 link (~32 GB/s) is the bottleneck when the model overflows VRAM. The best strategy is to shrink the model until it fits — either with EXL3 quantization or ModelOpt PTQ — and use GreenBoost's DDR4 pool for KV cache only.
I think the reason it refers it to DDR4 is because that is how the user explained it to their coding agent. LLMs are great at perpetuating unnecessary specificity.
For actual training, explicit sharding and RAM mapping are ugly, but at least you can see where the pressure is and reason about it. 'Transparent' often just means performance falls off a cliff and now debugging it sucks.
Does this make sense? I'd have thought the KV is guaranteed to be used 100% of the time while say in a MoE the same can't be said of the weights.
Though I suppose if you're shooting for huge context then having that allocation go into ram makes sense specially when its allocated but not used yet
MoE is kinda related in terms of lower usage requirements vs a dense model of same total param size, but I think your mental model is a bit off.
I would prefer to use system memory to cache different models, focusing on things like embedding, rerankers, and TTS. This is sufficient to run a more complex RAG locally, for example, via Mem0, and then use a larger LLM via the cloud.
(Feels especially deceptive when there is another top story right with the headline “nvidia nemoclaw” which is an official project)
(Still cool, still would benefit from better benchmarks)
Do not put swap on an SSD you care about at all.
This.
Many people rediscovering what the purpose of swap files are, but will still find a way to abuse it without knowing that they are actually destroying their SSD.
As soon as I switched to Linux I had all sorts of problems on Wayland where as soon as that 2 GB was reached, apps would segfault or act in their own unique ways (opening empty windows) when no GPU memory was available to allocate.
Turns out this is a problem with NVIDIA on Wayland. On X, NVIDIA's drivers act more like Windows. AMD's Linux drivers act more like Windows out of the box on both Wayland and X. System memory gets used when VRAM is full. I know this because I got tired of being unable to use my system after opening 3 browser tabs and a few terminals on Wayland so I bought an AMD RX 480 with 8 GB on eBay. You could say my cost of running Linux on the desktop was $80 + shipping.
A few months ago I wrote a long post going over some of these details at https://nickjanetakis.com/blog/gpu-memory-allocation-bugs-wi.... It even includes videos showing what it's like opening apps both on Wayland and X with that NVIDIA card.
Great way to backstab you if you prefer inference speed.
Most people who know it does this turns it off because it kicks in too early so if you have 24GB it'll offload to RAM and tank your inference speed when you hit around 22GB use.
https://nvidia.custhelp.com/app/answers/detail/a_id/5490/~/s...
https://i.imgur.com/c0a3vUy.png
and instead of improving the actual product, it decided to "solve the problem in software"
I expect this greenboost to fall and burn, honestly...
This isn't made by nvidia
I really appreciate thriftful & resourceful points of view. Exploring what if, looking for use is such a great virtue.