If you're in SF and weighing this decision, it's easy to get tilted in the buy direction because the rental stock is so horrific. Landlords have very little incentive to update properties or provide basic amenities that people take for granted in other major cities (good luck getting a washer/dryer).
With the 3.5 release, the Plus model was just a rebrand of the open weight 397B. But I suspect that will change going forward. They haven’t released the weights for 3.6 but they did make it available through a few US providers.
I give them a try about twice a year. I write a lot of Rust which should be squarely in their wheelhouse.
This last time I was pleasantly surprised to find they mostly fixed their SSH remote editing support. But then it started truncating rustc inline error messages and I couldn’t figure out how to view the whole thing easily. When you’re just trying to get something done little bits like this can add up quickly. Punted back to Cursor for now.
They are but the IDE needs to be integrated with them.
Qwen specifically calls out FIM (“fill in the middle”) support on the model card and you can see it getting confused and posting the control tokens in the example here.
This uses Nvidia’s CUDA snapshot API under the hood, but you have to pair it with a host side snapshot as well. Modal uses gVisor for this, which is notoriously high overhead.
Does anyone know of a more efficient alternative if you’re running a trusted container?
I’m not sure if it would be of much utility because this would presumably be for tensor parallel workloads. In that case you want the ranks in your cluster to be uniform or else everything will be forced to run at the speed of the slowest rank.
You could run pipeline parallel but not sure it’d be that much better than what we already have.
Sparks are built for this and actually have Connect-X 7 NICs built in! You just need to get the SFPs for them. This means you can natively cluster them at 200Gbps.
The way it typically works in an attention block is: smaller portions of the Q, K and V linear layers are assigned to each node and are processed independently. Attention, rope norm etc is run on the node-specific output of that. Then, when the output linear layer is applied an "all reduce" is computed which combines the output of all the nodes.
EDIT: just realized it wasn't clear -- this means that each node ends up holding a portion of the KV cache specific to its KV tensor shards. This can change based on the specific style of attention (e.g., in GQA where there are fewer KV heads than ranks you end up having to do some replication etc)
> Instead of one brittle giant, we orchestrate a Mixture of Experts…
“mixture of experts” is a specific term of art that describes an architectural detail of a type of transformer model. It’s definitely not using smaller specialized models for individual tasks. Experts in an MoE model are actually routed to on a per token basis, not on a per task or per generation basis.
I know it’s tempting to co-opt this term because it would fit nicely for what you’re trying to do but it just adds confusion.
Because it depends on how much better “best” is. If it’s only incrementally better than open source models that have other advantages, why would you bother?
OpenAI’s moat will only come from the products they built on top. Theoretically their products will be better because they’ll be more vertically integrated with the underlying models. It’s not unlike Apple’s playbook with regard to hardwares and software integration.
Not quite a frontier model but definitely built by a frontier lab: Grok 2 was recently open sourced and I believe it uses a fairly standard MHA architecture with MoE.