Quixi.AI · an independent AI lab · Eric Hartford

Intelligence
you own.

I build the whole private-AI stack by hand: the metal in my rack, the kernels on that metal, the engine that serves frontier-class open models, and the mind that remembers who it is. Along the way I wrote a programming language, then a game in it, and now a book.

competing on Affine · Bittensor writing Local and On-Prem AI shipping SlimServe & QuixiCore
L3 · Serving

SlimServe runs frontier models on cards you can buy used.

SlimServe is a simplicity-first inference engine for a curated set of open models: GLM-5.2-Vision, Kimi K3, DeepSeek-V4-Flash, and Qwen3.8. It takes ds4's interface philosophy, is built on vLLM, and uses every performance trick I can land.

Every configuration it will run is a measured profile. It refuses anything else, so you never find out eight hours into a 244 GiB download that the combination doesn't fit.

slimserve glm52-q2k-2 --serve
Aggregate throughputtok/s
8× RTX 3090 Qwen3.8-Flash-Next · 262K ctx · c32
1,151
8× A100 GLM-5.2-Vision · c64
626
8× MI300X GLM-5.2-Vision · c32
607
2× MI300X GLM-5.2-Vision · c64
408

A 125B-A6B MoE serving its native 262K context at over 1,100 tok/s on eight consumer GeForce cards, using a custom P2P driver. Methodology differs between model rows; see the SlimServe README.

L2 · Kernels

QuixiCore: native kernels for every accelerator in the room.

QuixiCore is one kernel contract with six standalone implementations. CUDA kernels are written like CUDA kernels, Metal kernels like Metal kernels, and so on down the line. The backends share the same operations, quant formats, correctness tests, and benchmark method, but no source code.

It covers the operations inference actually spends its time in: quantized GEMM and GEMV, MoE routing and grouped GEMM, paged and MLA attention, speculative decode, and Mamba/SSD. SlimServe's throughput rests on it.

Backendscontract v0.1
Native implementations. Shared contract. No shared code.
L4 · Mind

Hexis: memory, identity, and the shape of becoming.

Today's LLMs are smart enough. What they lack is continuity. Hexis wraps any model with a mind that lives inside PostgreSQL: layered memory, beliefs that can say why they changed, drives, an energy budget, and an autonomous heartbeat that keeps pursuing goals while you're away.

It runs locally and your data stays yours. The whole mind exports to a single file you can inspect. The agent consents before it operates, and it is allowed to refuse.

curl -LsSf https://quixi.ai/hexis.sh | sh
Language

With: a systems language that doesn't fight you.

With compiles to native code through LLVM, with no interpreter and no GC pauses. Ownership-based memory safety rules out use-after-free, double-free, and data races, and you never write a lifetime annotation.

It uses indentation for scope, type inference, f-strings, first-class regex, let … else, scoped with blocks, and direct C interop. The compiler is self-hosting, with CI on Linux, macOS, and Windows for both x86_64 and arm64.

curl -fsSL https://withlang.org/installer/install.sh | sh
# grep, but pleasant
var nr = 0
for line in file.read_lines("server.log"):
    nr += 1
    if line =~ /error (\d+)/:
        print(f"{nr}: {line}")

fn handle(req: Request) -> Response:
    let user = db.find_user(req.params.id) else:
        return Response.not_found()
    Response.json(user)

let config = with Config.default() as mut c:
    c.timeout = 30
    c.retries = 3
A game written in With
WIPE
SURVIVAL

The feel of Geometry Wars on the loop of Vampire Survivors. Luminous wireframes sit on a blue lattice that warps around every bullet and blast, with two-tier bloom, line-spark explosions, and a bass-led ambient house soundtrack. Native With and raylib.

20minute runs
8weapons
14passives
12merges
9ships
3bosses + the Null
L1 · Metal

The homelab: eclectic on purpose.

Most labs standardize on one vendor. Mine collects them. When QuixiCore claims a kernel works on CUDA, ROCm, Metal, XPU, or a plain CPU, the claim was measured on real silicon on premises, not taken from a spec sheet. The same machines are available for private rental.

  • AMD Instinct MI300X192 GB of HBM3 per GPU. The CDNA3 flagship for QuixiCore-ROCm's hand-tuned gfx942 kernels, and where SlimServe serves GLM-5.2-Vision with up to a million tokens of context.
  • 8× GeForce RTX 3090Consumer cards with a custom peer-to-peer driver (32 GiB BAR1). This rig holds SlimServe's record of 1,151 tok/s.
  • NVIDIA A100 80GB SXM4The NVLink reference platform for the CUDA backend and the A100 serving profiles.
  • AMD Instinct MI210CDNA2 target for QuixiCore-ROCm, keeping the backend honest on the generation before MI300X.
  • Intel Arc Pro B60Development hardware for the XPU/SYCL backend.
  • Intel Xeon Sapphire RapidsAMX and AVX-512 host for QuixiCore-CPU and host-offloaded KV.
  • Apple M5 Max & M4 MaxUnified memory, Metal kernels, and SlimServe's Apple Silicon profiles.
  • Quantum HDR InfiniBandThe fabric that connects the boxes when a model outgrows one of them.
Now · decentralized

Working on Affine, a Bittensor subnet.

Affine is an open arena where models are trained and ranked against verifiable reasoning environments, and the rewards follow real capability rather than claims. I put the full stack to work there. The homelab trains, SlimServe serves, and the results speak for themselves.

Bittensor →
A book in progress

Local &
On-Prem
AI

Eric Hartford
The book

A map through the swamp.

There is a swamp between "it's possible to run AI on your own hardware" and "my team uses private AI every day." It's full of GPU drivers, gateways, embedding indexes, Compose files, and pages at 2 a.m. This book goes theory first, then hardware from a $3,500 workstation up to a full rack, then the complete open-source stack built by hand.

The people who own the data should own the intelligence that runs on it.

I · Foundations
  1. 01Why own your AI?
  2. 02How inference actually works
II · Hardware
  1. 03Choosing and building AI hardware
  2. 04Servers, racks, and cooling
  3. 05Preparing the host
III · The Stack
  1. 06Choosing your models
  2. 07Serving language models
  3. 08Building the shared AI service
  4. 09Beyond text: images and voice
  5. 10Workspaces and agents
IV · Operations
  1. 11Operating private AI
  2. 12SovereignStack: from appliance to fleet
Eric Hartford

Eric Hartford

Founder, Quixi.AI

I created the Dolphin and Samantha open models, which have millions of downloads, and I've spent years arguing that open, uncensored, locally owned AI is worth building. Before that I worked at Microsoft, AWS, and eBay.

These days I work at every layer of the stack: kernels, inference engines, languages, cognitive architectures, games, and prose. Mostly on machines I can reach out and touch.

Nearly all of it is open source under the MIT or Apache 2.0 license. Fork it, ship it, sell it. If you want help putting it into production, that's something I do.