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Posted by jonesy827 17 hours ago

Unsloth Dynamic 3.0 GGUFs(unsloth.ai)
281 points | 99 commentspage 2
jadbox 15 hours ago|
The new IQ4XS has been working pretty well so far on 4090 16gb.
kamranjon 15 hours ago|
What size context are you able to squeeze in with less than 2gb of headroom? I have had some luck using a quantized kv cache but i fear that also decreases overall quality.
beacon294 4 hours ago||
Try the llama.cpp fork by thetom. It's called turboquant after the technique
QuantumNomad_ 15 hours ago||
Is it possible to use a model that needs around 64 GB VRAM if you have four GPUs with 16 GB VRAM each?
gruturo 14 hours ago||
Yes, and if you have the PCIe lanes (say, an x16 lane - actually delivering 16 lanes! - to each GPU) it's also quite performant - it's called a tensor split in llama-server.

If your motherboard/cpu doesn't actually have those (few do outside some xeons, epycs and threadrippers) you can still do it - it's called a layer split and will work even with 1 lane per GPU. Each GPU will work at its maximum speed, but only 1 will be active at any given instant - imagine a relay race.

(Didn't mention which PCIe generation - obviously the higher the better. At v4 and up, even 8 lanes per GPU would be enough for a performant tensor 4-way split)

Edit: If you have more than 1 user at a time, the GPU can actually all be working all the time, if there are enough parallel requests to serve. But you need enough KV cache for all the sessions you're running in parallel.

xlayn 15 hours ago|||
I do use 2 amd gpus and I get high 40 for generation, 500 for pp and low 20/100 by the end of the context of 256k.

llama-server --host 0.0.0.0 --port 8089 -m Qwen3.8-27B-UD-Q8_u.gguf --spec-type draft-mtp,ngram-mod --spec-draft-n-max 3 --spec-draft-n-min 1

if you have an igpu and want to exclude or just use some gpus you can use

--device Vulkan3,Vulkan2,Vulkan1

in my case vulkan because of amd, you can see your devices with

llama-server2 --list-devices

Available devices: Vulkan0: AMD Radeon Graphics (RADV RAPHAEL_MENDOCINO) (33515 MiB, 29349 MiB free) Vulkan1: AMD Radeon RX 7900 XTX (RADV NAVI31) (24560 MiB, 4911 MiB free) Vulkan2: AMD Radeon RX 7900 XTX (RADV NAVI31) (24560 MiB, 7681 MiB free)

strictnein 13 hours ago|||
Yes, you can. Ideally though, you want to minimize the number of cards and maximize the amount of memory in each card.

Using multiple cards is one of the things that the models and software that Unsloth releases does really well in terms of ease of use and relatively good performance.

jambalaya8 13 hours ago||
How many do you recommend?
strictnein 12 hours ago||
I've run 4, 6, and 8. Adding more video cards increases total available memory, so you can load larger models, but there are definitely some drawbacks.

More cards = more communication over PCIe. The prompts don't come in and just get magically split between each card, they move sequentially through them.

Also, with 2x24GB cards you don't really have 48GB of usable memory to load a model, closer to ~42GB + context.

And then there are power concerns, motherboard limitations (PCIe slots and lanes - a lot of motherboards with multiple 16x PCIe slots don't actually have 16x lanes to each of those slots), and more. 8x GPUs are going to easily draw 2000W on their own, if not substantially more. You'll need wiring and a circuit that can support 3000W without a risk of starting a fire in your wall.

For $5k, a single 32GB 5090 might be a better choice for a lot of people versus 4x3090s with 24GB each. It will definitely perform substantially better on smaller 27B models.

For hardware:

A good motherboard with lots of PCIe lanes (7x full 16x PCIe 4.0), DDR4 support, etc:

https://www.asus.com/us/motherboards-components/motherboards...

Add in a 3xxx series Threadripper PRO, 128 or 256GB of DDR4 (going higher becomes really expensive), and a ~1400 watt power supply. You can underpower/undervolt Nvidia cards really easily, and capping them at 250W loses you minimal performance.

sharmajai 15 hours ago|||
I am getting 14 t/s on my 16 GB card at full context with the UD-Q3_K_XL quant. Model link: https://huggingface.co/unsloth/Qwen3.8-27B-GGUF.
zenoprax 9 hours ago|||
For some reason the unsloth models leave hardly any room for context. I've switched to the regular (non-unsloth) and get about 25 t/s and get about 80,000 more context tokens for the same quant.
Balinares 13 hours ago|||
Wow, interesting. What KV cache quantization do you use?
walrus01 12 hours ago|||
Yes, google "llama-server split model multiple GPU" for some concrete examples.
charcircuit 15 hours ago|||
Of course. Models don't actually require VRAM. Nor do they require regular RAM. You could have 1 GB of RAM and swap the model to disk as you need different parts of it. And if you didn't have enough disks you could access weights via a network connection.
leoooodias 14 hours ago|||
You don’t even need electricity. You could print the model weights onto millions of sheets of paper, and hire a team of carrier pigeons to fly them into your office one by one. No VRAM!
BonerWiener 14 hours ago|||
IP over Avian Carriers: https://www.rfc-editor.org/info/rfc2549/
chuckadams 13 hours ago||||
Coincidentally that’s also Google’s new method for distributing Android sources.
revolvingthrow 14 hours ago|||
The bitrot would be excessive
kQq9oHeAz6wLLS 8 hours ago|||
The key to running in lower amounts of VRAM is patience. It'll be slow, but it'll work.
segbrk 15 hours ago||
Yes, but unless they support NVLink (they don't), it's quite slow.
tetsuo420 15 hours ago||
It seems the NVFP4 quants have a preview version of this Unsloth Dynamic 3.0. Is this close to the finished version, or would it be better to switch to one of the newer quants?
jedbrooke 8 hours ago||
huh, sounds like they’re talking about over fitting and datasets etc, it seems like this is almost more like a fine tune/distill than just a pure quantization
josh-wrale 14 hours ago||
Sidebar: single threaded inference isn’t good enough anymore
sosodev 14 hours ago|
What about do you mean by single threaded? Each token is predicted by using parallel computation on the GPU.
josh-wrale 13 hours ago||
Multiple agents need tokens. Should optimize for that instead of one agent blocking the others.
sosodev 12 hours ago||
One agent typically blocks the others on a local device because the GPU is already completely utilized either in terms of memory or compute. You can have true parallelism at home, but you need an absurd amount of resources. It's not a simple threading problem.
zozbot234 10 hours ago|||
The typical bottleneck to wider batching on consumer hardware is memory capacity for the KV-cache, not compute (even unified memory/iGPU-based platforms have enough compute to allow for some batching, and SSD offloading changes the scenario entirely). Qwen models tend to have bulky KV-caches for any given token count. But agentic swarms might end up sharing a large cache prefix, so there's scope for potential gains there.
redox99 10 hours ago||||
I have no problem running two or three sequences of qwen 27B with a 3090. It's basically the recommended way, LLM inference without batching is super inefficient.
josh-wrale 8 hours ago|||
Yes, but I haven’t seen it in Unsloth/llama.cpp. I see it in Sglang and vllm. Unsloth should default to sglang imo
acuozzo 14 hours ago||
Can this help tiny models like Qwen3.5-0.8B?
skyde 12 hours ago||
Would converting those quant to MLX preserve the accuracy/size ? Or this only work with GGUF?
spwa4 15 hours ago||
No MLX versions for 3.8 though.
freemindcore 3 hours ago||
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DisceetPlug 10 hours ago|
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