QEMU VS firecracker

My most-wanted QEMU feature: https://github.com/qemu/qemu/commit/a2260983c6553

Using `gic-version=3` on macOS you can now use more than 8 cores on ARM chips.

A better solution is just to write a plain ass shell script that tests if various C snippets compile.

https://github.com/oilshell/oil/blob/master/configure

https://github.com/oilshell/oil/blob/master/build/detect-pwe...

Not an unholy mix of m4, shell, and C, all in the same file.

---

These are the same style as a the configure scripts that Fabrice Bellard wrote for tcc and QEMU.

They are plain ass shell scripts, because he actually understands the code he writes.

https://github.com/qemu/qemu/blob/master/configure

https://github.com/TinyCC/tinycc/blob/mob/configure

OCaml’s configure script is also “normal”.

You don’t have to copy and paste thousands of lines of GNU stuff that you don’t understand.

(copy of lobste.rs comment)

Related:

A fast Pascal (Delphi) WebAssembly interpreter:

https://github.com/marat1961/wasm

WASM-4:

https://github.com/aduros/wasm4

Curated list of awesome things regarding WebAssembly (wasm) ecosystem:

https://github.com/mbasso/awesome-wasm

Also, it would be nice if there was a WASM (soft) CPU for QEMU, which (if it existed!) would go here:

https://github.com/qemu/qemu/tree/master/target

> architectural registers are always updated

In tiny code, the guest registers (global TCG variables) are stored in the host's registers until you either call an helper which can access the CPU state or you return (`git grep la_global_sync`). This is the reason why QEMU is not so terribly slow.

But after a check, this also happens when you access the guest memory address space! https://github.com/qemu/qemu/blob/master/include/tcg/tcg-opc... (TCG_OPF_SIDE_EFFECTS is what matters)

But still, in the end, it's the same problem. What QEMU does, can be done in LLVM too. You could probably be more efficient in LLVM by using the exception handling mechanism (invoke and friends) to only serialize back to memory when there's an actual exception, at the cost of higher register pressure. More or less what we do here: https://rev.ng/downloads/bar-2019-paper.pdf

Um, in case you don't know, UTM (based on QEMU) is out for quite a while.

Some of these tools include Oracle VM VirtualBox (that I've used since before the acquisition of Sun Microsystems by Oracle), VMWare Workstation Player, and QEMU, but last year, I found out about Multipass.

For C/C++ projects that use meson as the build system, there is an excellent way to manage dependencies:

https://mesonbuild.com/Wrapdb-projects.html

https://mesonbuild.com/Wrap-dependency-system-manual.html

meson will download and build the libraries automatically and give you a variable which you pass as a regular dependency into the built target:

https://github.com/qemu/qemu/tree/005ad32358f12fe9313a4a0191...

https://github.com/harfbuzz/harfbuzz/tree/main/subprojects

https://github.com/harfbuzz/harfbuzz/blob/37457412b3212463c5...

Or, if you're using proper operating systems, they're managed by the usual package manager, just like everything else.

For all the users of the Linux platform, QEMU is the VM that you should go for. This software comes without any price tag and works as an emulator of various machines with utmost ease and completion; the software uses dynamic translations to emulate hardware peripherals and enhances its overall performance. If you are using QEMU as a virtualizer, then it will function exactly like the host system (provided you have the right set of hardware).

In this tutorial, we set up macOS and Windows virtual machines on UTM, a macOS application that provides a GUI wrapper for QEMU, a powerful open-source emulator and virtualizer. UTM allows you to easily manage and run virtual machines without memorizing complex commands. It also has special handling for macOS, making it simpler to install compared to other virtual machine software.

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.