Thread

1. syntaxing · 1790023047 · focus · HN ↗

   All these new models are such tease for us folks with 128GB of shared memory. Buying another unit now to expand to 256GB is a mortgage payment but it’s getting tempting…

   1. brcmthrowaway · 1790023227 · focus · HN ↗

      Is there a gamechanger around the corner to reduce DRAM requirements?

      1. stymaar · 1790023551 · focus · HN ↗

         n-gram per-layer embeddings[1][2] might be it.

         [1] <a href="https:&#x2F;&#x2F;sebastianraschka.com&#x2F;llm-architecture-gallery&#x2F;per-layer-embeddings&#x2F;" rel="nofollow">https:&#x2F;&#x2F;sebastianraschka.com&#x2F;llm-architecture-gallery&#x2F;per-la...

         [2]: See DS 4.1-Flash and Qwen-3.8-Next.

         1. verdverm · 1790023718 · focus · HN ↗

            this is to offload VRAM to DRAM (for GP comment), and makes no difference for URAM

            1. zozbot234 · 1790024709 · focus · HN ↗

               You can definitely offload n-gram embeddings to storage; they&#x27;re very sparsely used (only a few KB fetched per token) so this is quite effective. Loading to DRAM only becomes necessary if they are a bottleneck to overall performance (which might happen if you&#x27;re doing very wide batches and everything else uses super fast VRAM&#x2F;HBM).

               1. verdverm · 1790024874 · focus · HN ↗

                  I was looking at the qwen-next-flash, and the weights would fill my OEM Spark on their own, before the n-gram. I&#x27;m unclear if offloading to disk can work here, is that what you are implying is possible?!

                  1. girvo · 1790024933 · focus · HN ↗

                     Check out eugr’s TP=1 sparkrun recipe :)

                     It’s an NVFP4 quant, but it fits, and is surprisingly capable.

                     1. verdverm · 1790025043 · focus · HN ↗

                        do you have a HF link? HF search is not uncovering it for me

                        (or is it somewhere else)

                        1. verdverm · 1790026316 · focus · HN ↗

                           looks like this is likely it

                           <a href="https:&#x2F;&#x2F;github.com&#x2F;spark-arena&#x2F;eugr-recipes" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;spark-arena&#x2F;eugr-recipes

                        2. girvo · 1790026465 · focus · HN ↗

                           <a href="https:&#x2F;&#x2F;github.com&#x2F;spark-arena&#x2F;eugr-recipes&#x2F;blob&#x2F;main&#x2F;recipes&#x2F;qwen3.8-flash-next-nvfp4-solo.yaml" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;spark-arena&#x2F;eugr-recipes&#x2F;blob&#x2F;main&#x2F;recipe...

                           This one!

                           1. verdverm · 1790026884 · focus · HN ↗

                              I have a quirky vLLM on k8s on 2x OEM sparks with about 9 models available to me. I&#x27;m not keen to run nightly vLLM, too many issues with it in the past

                              I have a watchful eye on the diffusion ~ Jev&#x2F;Kev PR

                              <a href="https:&#x2F;&#x2F;github.com&#x2F;vllm-project&#x2F;vllm&#x2F;pull&#x2F;57250" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;vllm-project&#x2F;vllm&#x2F;pull&#x2F;57250

                              1. girvo · 1790027372 · focus · HN ↗

                                 For what it&#x27;s worth, Flash Next outperforms every other model that is available to us on the GB10 in all of my testing; though if you have two sparks then the TP=2 version is even better and easier (I don&#x27;t think you&#x27;ll need the nightly for that at all, just use the recipe)

                                 I&#x27;m so tempted to buy a second one...

                                 1. verdverm · 1790027605 · focus · HN ↗

                                    prices have gone up quite a bit...

                                    I&#x27;m running embedding, reranking, and policy tuned models too. Flash Next is not a substitute for those

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