AXIOM
AXIOM is a bootable Rust kernel that optimizes transformer inference by replacing generic OS abstractions with inference-specific primitives.
AXIOM
What is AXIOM?
AXIOM vs Similar AI Tools
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How to use AXIOM?
- 1Set up Rust nightly toolchain and install bootimage.
- 2Pack model weights using the provided Python script (pack_weights.py).
- 3Build the kernel with cargo +nightly run --release.
- 4Boot the kernel in QEMU or on bare metal; it will perform inference and output telemetry.
AXIOM Key Features
- Tensor-native memory allocation with pre-reserved pools
- Layer-boundary scheduling to prevent mid-layer preemption
- Double-buffered weight streaming to overlap I/O and compute
- LayerLock scheduler for cache-resident execution
- Quantized inference (Q4) for SmolLM2-135M and TinyLlama-1.1B
AXIOM Use Cases
- Running large language models on memory-constrained systems
- Optimizing inference latency for transformer models
- Research in OS-level optimizations for AI workloads
- Evaluating the impact of kernel-level scheduling on inference throughput
AXIOM Pricing & Free Credits
AXIOM currently operates on a Free model.
This tool is completely free to use
AXIOM Pros & Cons
Pros
- Reduces streaming overhead from seconds to microseconds per layer
- Optimizes memory layout for predictable inference access patterns
- Open source and extensible for research
- Demonstrates significant throughput improvements (14.8x TPS in benchmark)
Cons
- Currently limited to specific models (SmolLM2-135M, TinyLlama-1.1B Q4)
- Requires bare-metal NVMe for intended low-memory 7B-class evaluation
- Compute kernels (FFN projection) remain a bottleneck
- Not a general-purpose OS; lacks userspace, networking, filesystem
What is AXIOM best for?
- Researchers in AI systems and operating systems
- Developers optimizing inference on constrained hardware
- Engineers interested in kernel-level performance engineering for AI
AXIOM FAQ
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