humility VS firecracker

A lot of questions in there! Taking these in order:

1. We aren't making standalone servers: the Oxide compute sled comes in the Oxide rack. So are not (and do not intend to be) a drop in replacement for extant rack mounted servers.

2. We have taken a fundamentally different approach to firmware, with a true root of trust that can attest to the service processor -- which can turn attest to the system software. This prompts a lot of questions (e.g., who attests to the root of trust?), and there is a LOT to say about this; look for us to talk a lot more about this

3. In stark contrast (sadly) to nearly everyone else in the server space, the firmware we are developing is entirely open source. More details on that can be found in Cliff Biffle's 2021 OSFC talk and the Hubris and Humility repos.[0][1][2]

4. Definitely not vaporware! We are in the process of shipping to our first customers; you can follow our progress in our Oxide and Friends podcast.[3]

[0] https://www.osfc.io/2021/talks/on-hubris-and-humility-develo...

[1] https://github.com/oxidecomputer/hubris

[2] https://github.com/oxidecomputer/humility

[3] https://oxide-and-friends.transistor.fm/

It's a mix of embedded work and improving the system's tooling (faster builds, debugger support, etc)

Other folks have mentioned this, but it's important to understand the limitations of Rust with respect to safety. In particular: every stack operation is -- at some level -- an unsafe operation as it operates without a bounds check. This isn't Rust's fault per se; non-segmented architectures don't have an architecturally defined way to know the stack base. As a result, even an entirely safe Rust program can make an illegal access to memory that results in fatal program failure. That, of course, assumes memory protection; if you don't have memory protection (or, like many embedded operating systems, you don't make use of it), stack overflows will plow into adjacent memory.

But wait, it gets worse: stack overflows are often not due to infinite stack consumption (e.g., recursion) but rather simply going deep on an unusual code path. If stack consumption just goes slightly beyond the base of the stack and there is no memory protection, this is corrupt-and-run -- and you are left debugging a problem that looks every bit like a gnarly data race in an unsafe programming language. And this problem becomes especially acute when memory is scarce: you really don't want a tiny embedded system to be dedicating a bunch of its memory to stack space that will never ("never") be used, so you make the stacks as tight as possible -- making stack overflows in fact much more likely.

Indeed, even with the MPU, these problems were acute in the development of Hubris: we originally put the stack at the top of a task's data space, and its data at the bottom -- and we found that tasks that only slightly exceeded their stack (rather than running all of the way through its data and into the protection boundary) were corrupting themselves with difficult-to-debug failures. We flipped the order to assure that every stack overflow hit the protection boundary[0], which required us to be much more intentional about the stack versus data split -- but had the added benefit of allowing us to add debugging support for it.[1]

Stack overflows are still pesky (and still a leading cause of task death!), but without the MPU, each one of these stack overflows would be data corruption -- answering for us viscerally what we "need the MPU for."

[0] https://github.com/oxidecomputer/hubris/commit/d75e832931f67...

[1] https://github.com/oxidecomputer/humility#humility-stackmarg...

In addition to Cliff's talk/blog -- which are absolutely outstanding -- I would recommend listening to the Twitter Space we did on Hubris and Humility last week.[0] It was a really fun conversation, and it also serves as a bit of a B-side for the talk in that it goes into some of the subtler details that we feel are important, but didn't quite rise to the level of the presentation. And of course, be sure to check out the source itself![1][2]

[0] https://www.youtube.com/watch?v=cypmufnPfLw

[1] https://github.com/oxidecomputer/hubris

[2] https://github.com/oxidecomputer/humility

Humility (the debugger)

This architecture leverages microVMs for rapid scaling and high-density workloads. But does it work for GPU? The answer is no. You can look at the old 2019 GitHub issue and the comments to it to get the bigger picture of why it is so.

Hi, I'm the CEO of the company that built this SDK.

We're a company called E2B [0]. We're building and open-source [1] secure environments for running untrusted AI-generated code and AI agents. We call these environments sandboxes and they are built on top of micro VM called Firecracker [2].

You can think of us as giving small cloud computers to LLMs.

We recently created a dedicated SDK for building custom code interpreters in Python or JS/TS. We saw this need after a lot of our users have been adding code execution capabilities to their AI apps with our core SDK [3]. These use cases were often centered around AI data analysis so code interpreter-like behavior made sense

The way our code interpret SDK works is by spawning an E2B sandbox with Jupyter Server. We then communicate with this Jupyter server through Jupyter Kernel messaging protocol [4].

We don't do any wrapping around LLM, any prompting, or any agent-like framework. We leave all of that on users. We're really just a boring code execution layer that sats at the bottom that we're building specifically for the future software that will be building another software. We work with any LLM. Here's how we added code interpreter to Claude [5].

Our long-term plan is to build an automated AWS for AI apps and agents.

Happy to answer any questions and hear feedback!

[0] https://e2b.dev/

[1] https://github.com/e2b-dev

[2] https://github.com/firecracker-microvm/firecracker

[3] https://e2b.dev/docs

[4] https://jupyter-client.readthedocs.io/en/latest/messaging.ht...

[5] https://github.com/e2b-dev/e2b-cookbook/blob/main/examples/c...

As far as I know, Fly uses Firecracker for their VMs. I've been following Firecracker for a while now (even using it in a project), and they don't support GPUs out of the box (and have no plan to support it [1]).

I'm curious to know how Fly figured their own GPU support with Firecracker. In the past they had some very detailed technical posts on how they achieved certain things, so I'm hoping we'll see one on their GPU support in the future!

[1]: https://github.com/firecracker-microvm/firecracker/issues/11...

I pass through a GPU and USB hub to a VM running on a machine in the garage. An optical video cable and network compatible USB extender brings the interface to a different room making it my primary “desktop” computer (and an outdated laptop as a backup device). Doesn’t get more silent and cool than this. Another VM on the garage machine gets a bunch of hard drives passed through to it.

That said, hardware passthrough/VFIO is likely out of the current realistic scope for this project. VM boot times can be optimized if you never look for hardware to initialize in the first place. Though they are still likely initializing a network interface of some sort.

“MicroVM” seems to be a term used when as much as possible is stripped from a VM, such as with https://github.com/firecracker-microvm/firecracker

According to their own FAQ it is indeed: https://github.com/firecracker-microvm/firecracker/blob/main...

What about microVMs like firecracker?

Dynamic memory management - Firecracker's RAM footprint starts low, but once a workload inside allocates RAM, Firecracker will never return it to the host system. After running several workloads inside, you end up with an idling VM that consumes 32 GB of RAM on the host, even though it doesn't need any of it.

Firecracker has a balloon device you can inflate (ie: acquire as much memory inside the VM as possible) and then deflate... returning the memory to the host.

https://github.com/firecracker-microvm/firecracker/blob/main...

Very few things in those companies are being written in Rust, and half of those projects chose Rust around ideological reasons rather than technical, with plenty of 'unsafe' thrown in for performance reasons

https://github.com/firecracker-microvm/firecracker/search?q=...

The fact that 'unsafe' even exists in Rust means it's no better than C with some macros.

Don't get me wrong, Rust has it's place, like all the other languages that came about for various reasons, but it's not going to gain wide adoption.

Future of programming consists of 2 languages - something like C that has a small instruction set for adopting to new hardware, and something that is very high level, higher than Python with LLM in the background. Everything in the middle is fodder.

https://github.com/firecracker-microvm/firecracker is the one that comes to mind, but most of these are internal.