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Comments (69)

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  fedpost
  We desperately need to keep local models competitive with the hyper scalers. I won't want to be beholden to them and their whims forever. With anthropic now restricting game reverse engineering I think we're about to cross a Rubicon of censorship.
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  roenxi
  That's pretty interesting, because with the Bonsai 2 quantisations it is quite achievable to get Qwen 27B running at 50 t/s on reasonably priced gaming machine [0]. Which in turn suggests that Fable 5 coding performance can be achieved locally with a bit of clever harnessing.Although the same harnessing should also improve Fable's performance. The important thing here is the cap for local performance looks like it could be quite high.[0] https://store.steampowered.com/hwsurvey/Steam-Hardware-Softw... suggests 16GB VRAM, 32 GB system.
• zihotki
  I'd wait for more benchmarks before making any conclusions since this benchmark has only "The first ten problems of their LiveCodeBench Hard list". And LiveCodeBench Hard is a several years old benchmark. Could be very saturated
• cloudking
  Benchmarks are one thing, but I've yet to see anything substantial actually built with local models. Most of the posts on X are about benchmarks and tok/s. Has anyone actually built a quality production app or game with real users?
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  mkl
  GVS5H, which this is based on, was discussed here a week ago: https://news.ycombinator.com/item?id=50228167
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  xlayn
  Remember those gpus into chips https://inference-docs.cerebras.ai/models/qwen-3.8-27b , where you have this chip that implements a model that run at those extremely high speeds... the ones that people ask "why do we need that"... if this is true... one of those chips with the correct harness is all that's needed to get fable like intelligence in every single computer in this planet...Happy hallowen Tax Ramltman and Amowhateverwayyournameiswritten...
• 74948494i4
  This is good timing. My setup at home involves a two servers with 5080s each and an Rtx spark laptop with 128gb. I plan I have qwen flash next on the latter and the 27b variant on the former. I would’ve preferred to have purchased a dgx spark but everything is just ridiculously expensive now. My own setup would be 3x the cost to buy again.I’m mainly trying to replicate Gemini flash and muse agent for my family of 4 without breaking the bank (already had a 5080 for gaming and the other for image gen).It is unclear if this memdoor works for agentic tasks, e.g.: “check daily if dune 3 tickets are for sale and if they are buy tickets for 4 where all 4 seats are next to each other in the same row and as center as possible.”There is a huge cliff once you go past 128gb. Basically you need either Rtx 6000 pros (20k+) or a cluster of dgx sparks (15k+)
• egeres
  This also appeared on HN last week: https://news.ycombinator.com/item?id=50228167, it's the other method they mention, GVS5H
• anon
  undefined
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  walrus01
  I'd like to see this done with qwen 3.8-flash-next (Q8 or full precision) which beats 3.8-27B on just about every coding/command line use benchmark. In particular comparisons like terminalbench 4.0.https://artificialanalysis.ai/models/comparisons/qwen3-8-fla...
• 0xbadcafebee
  So someone used a small model with many requests & a massive amount of tokens, to eventually beat a large model with a single request & tiny amount of tokens. .....Cool? Were they not expecting that to work?
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  BrandoDev_it
  Fable 5 is not anymore frontier and even if, I hardly believe five very small 27b models can do like Fable 5. I think this is whishful thinking more than a fact
• topspin
  So far, the comments have focused on hopes for the effectiveness of open-weight models versus proprietary frontier models. That's fine, but I'm thinking about the approach and the results, as opposed to the model.Taking the results at face value, what is it about the "team" that makes the system so effective? That's the key here: presumably, the approach should work regardless of the model, open-weight or otherwise.My guess would be that the division of labor makes the separate contexts more effective. This is an optimization around the limits of context. Some of that is simply the net amount of context brought to bear: each team member has a context, so the net amount of context is multiplied by the number of team members. Also, since the division limits the scope of each team member's focus, the context a team member has is used more effectively.
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  m3kw9
  can it bet a team of 5 fable 5's?
• drix10
  [flagged]