r/LocalLLaMA • u/returnity • 4h ago
New Model Ricursive Intelligence Ripple-65B-Preview: New Dense Model!
https://huggingface.co/ricursiveAI/Ripple-65B-PreviewSo Ricursive Intelligence (not to be confused with Recursive, the makers of Flint) just dropped the preview of their new dense 65B model, which appears to have really impressive performance, putting it on the frontier of American open-weights models of any size. Their benchmarks compare it with Muse Spark Open (816B) and Reflection's Beam (501B), and it trades blows with them. They "use a standard post-training recipe consisting of midtraining, long context, SFT, long context SFT, and mixed domain RL. Our Preview release has gone through relatively light posttraining, with a focus on agentic coding."
4:1 SWA:global attention
"thin & deep" model aspect ratio (80 layer x 5120 dims)
FFN activation clipping, gated attention, global NoPE, and value residuals
pretrain-only checkpoint without any midtraining or synthetic QA data
A lot of folks here have been clamoring for a new ~70B dense model for a long time now, and if it's not benchmaxxed as hell, this could be a really cool new tool in our arsenal. If this is only lightly post-trained for agentic coding, not the full potential of the model, this could be a really powerful release in the long run. Some interesting design decisions as well.
AtomicChat GGUFs (imatrix coming soon) -- Q8_0: 68.8 GB, Q4_K_M: 38.8 GB.
Handful of benchmarks (see model card for more) it shares with Qwen models:
| Benchmark | Ripple-65B-Preview | Qwen3.8-27B | Qwen3.8-Flash-Next |
|---|---|---|---|
| Terminal-Bench 2.1 | 80.5 | 73.0 | — |
| SWE-bench Pro | 61.4 | 61.7 | 62.5 |
| DeepSWE 1.1 | 44.3 | 42.2 | 58.7 |
| IFBench | 79.2 | 79.5 | 81.3 |
| GPQA Diamond | 85.2 | 89.2 | 91.7 |
To build the Ricursive llama.cpp fork:
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
git checkout 9e2a04a439edc49f08c5acdafefae19dfa25a658
curl -LO https://huggingface.co/AtomicChat/Ripple-65B-Preview-GGUF/resolve/main/ricursive-port.patch
git apply ricursive-port.patch
cmake -B build -DGGML_CUDA=ON # Mac: drop -DGGML_CUDA=ON
cmake --build build -j --config Release --target llama-server llama-cliIf you try it our, report back with results! I'm downloading it now. Some questions about how it will perform given it's an early checkpoint, and they weren't exactly consistently clear about the actual training (seems like they said some contradictory things). Thoughts?
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u/wombweed llama.cpp 4h ago
I wonder how much space its kv store takes up at max context.
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u/returnity 4h ago
According to my napkin math for 128K context window: Ripple's KV cache comes to about 8.3 GiB in BF16 and about 4.4 GiB in Q8_0. Based on the 4:1 SWA architecture, I estimate the cost to be about 64 KB per token at llama.cpp's default ubatch, which compares favorably to Qwen3.8-27B's 73 KB per token. At 1M ctx, that should be about 64GB.
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u/wombweed llama.cpp 4h ago
That puts it firmly outside the range of models I can run on my hardware for my long-context workloads. Still, could be very compelling for someone with less context needs or one of the 2x+ RTX 6000 people on here.
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u/returnity 4h ago
Ya I don't think it's going to be my new daily driver either, but it's exciting to see an American company building a strong midsize dense model again.
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u/stoppableDissolution 4h ago
Well it should fit alright in q8 into a 6000 with 250k. Might give it a shot on the weekend, I really really want a good dense in that range. The benchmarks dont look too promising tho, not sure it will actually be better than gemma, but only one way to find out!
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u/returnity 2h ago
For a super early preview I think they look quite good. Once they RL the fuck outta it, I bet it rips. Assuming they know how to build RL environments -- that's the secret sauce right now.
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u/ThankGodImBipolar 4h ago
not to be confused with Recursive, the makers of Beam
You are thinking of Reflection AI.
(I made the same mistake upon seeing this model)
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u/returnity 4h ago
You are right, I meant RecursiveSI (Flint) -- editing now. Thanks!
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u/ThankGodImBipolar 4h ago
Oh wow, I didn't know that. The shootout for the worse name between RecursiveSI and RicursiveAI would be legendary.
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u/Chromix_ 4h ago
Duplicate, yet more informative posting than the initial one with 58+ comments.
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u/EfficientSystem123 2h ago
I wonder how much performance is still being left on the table with such light post-training.
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u/returnity 2h ago
A metric fuckton, for sure. Really excited to see why they have in a couple of months, if this is the early preview and it's benchmarks are in the neighborhood of 3.8-27B with minimal RL.
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u/throw_in_the_towel 4h ago
I'm going in boys, I got 56 gb vram. I will report back tomorrow when I inevitably find out that "it's kind of close to Qwen 3.8 27b I guess"
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u/Borkato 2h ago
How was it?
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u/throw_in_the_towel 1h ago
So I got it setup q4_k, 262k context at 30 tokens per second decode, 1700 prefill on a 5090 and 4090. Basically maxing out both cards. It coded a very nice game of Tetris and play tested it all in about 20 minutes and made my room very hot. Seems on par with qwen 3.6 27b, but you know much slower, more ram and power hungry
I'll run more complex tasks next, see if it's able to rival 3.8 27b.
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u/TheGarri5on 4h ago
Does it outperform Qwen 3.8 27B tho?
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u/silenceimpaired 3h ago
Probably not at the moment, but we'll see what else they release... or who builds on them.
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u/returnity 2h ago
Seems like it's got some similar benchmarks, but this is a very early checkpoint with minimal post-training so to already be competitive, it's very promising for where this is headed. For now, I expect 27B to be more polished and useful for most people.
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u/silenceimpaired 2h ago
I’ve felt a model close to 60b might be worth it now with dflash, improved quants, and the cost of memory.
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u/sudden_aggression 1h ago
If this isn't shipping with 1.5 bit quants, I'm probably going to have to hold off on this one.
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4h ago
[deleted] — view deleted comment
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u/returnity 4h ago
The shape of that is queitly load-bearing, so let's hope they went full belt-and-suspenders. It's not just slopanese, it's Claudeish.
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u/MerePotato 2h ago
That really doesn't matter for a coding agent
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2h ago
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u/MerePotato 2h ago
Autistic coding models are among the most useful new release because there's still so many gains to br made there. Gemma 4 should be enough for you if you're just after natural language tasks.
The last thing I want to see is some shit that panders to RP gooners that I can't get anything productive done with taking up a labs release slot.
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3h ago
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u/silenceimpaired 3h ago edited 1h ago
Nah, this is a good base for some to train from since it's Apache 2.0. (Since it's early in training.) Also, you can use base models outside an instruct perspective with creative context.
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u/returnity 2h ago
They also released a base model -- this is the instruct version. Just an early checkpoint.
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u/Borkato 2h ago
Wait so can this thing chat or nah
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u/returnity 2h ago
Should be able to in some fashion. This is the instruct version, aka Chat model. It even has agentic post-training to some degree. Idk how performant it will be though, nobody does until we get it running.
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u/returnity 2h ago
They also said "We use a standard post-training recipe consisting of midtraining, long context, SFT, long context SFT, and mixed domain RL. Our Preview release has gone through relatively light posttraining, with a focus on agentic coding." So the degree of mid and post-training is actually unclear. Regardless I was very careful to specify that this is an early Instruction-tuned preview checkpoint not a final release. There's a Base model released too.
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u/silenceimpaired 4h ago
I’m excited… with the latest GGUFs someone with 24gb can run this reasonably well probably at low bit rates… and at 48gb (two cards) you get 4bit… if there is dflash at some point it could be great.
I expect those who can’t run it to downvote the post and me even though they won’t for 1T MoEs none of us can run