flatrun 0.1.0

The streaming inference runtime for open-weight LLMs.

Flatrun

The streaming inference runtime for open-weight LLMs.

Run GGUF, SafeTensors, and MLX models layer-by-layer directly from storage, enabling inference on models larger than available system RAM.

GitHub: https://github.com/flatseek/flatrun  ·  Organization: https://github.com/flatseek

Part of the Flatseek ecosystem

Flatseek (Keyword Search) • Flatvec (Vector Search) • Flatask (RAG Runtime) • Flatweight (AI Model Storage) • Flattune (LLM Fine-Tuning) • Flatlens (Data Visualization)

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Try It

Download a sample GGUF model from Hugging Face and start an interactive chat:

wget https://huggingface.co/lmstudio-community/SmolLM2-135M-Instruct-GGUF/resolve/main/SmolLM2-135M-Instruct-Q4_K_M.gguf -O smoll.gguf

flatrun chat --model smoll.gguf

Example session:

Detected format: gguf
Building tokenizer from GGUF metadata (SmolLM2-135M-Instruct-Q4_K_M.gguf) ...
Tokenizer vocab: 49152
Chat template: Qwen2 ChatML
Loaded model in 0.53 s; layers=30

Chat mode (max_new=128/turn, history=True).
Type your message; Ctrl-D (EOF) or 'exit' to quit.

You: where is paris?

Assistant:
Paris is the capital and largest city of France. It is located in the
north-central part of the country along the Seine River and is one of
Europe's major cultural, historical, and economic centers.

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Overview

A pure Python and NumPy runtime for AI inference research and optimization.

Flatrun is an experimental inference runtime built to explore how large language models can execute without loading an entire checkpoint into memory.

Instead of treating a model as a single monolithic file, Flatrun streams weights layer-by-layer directly from disk. Only the layer currently being executed resides in memory while the remaining weights stay memory-mapped on storage.

This streaming architecture allows models significantly larger than available system RAM to run on commodity hardware, trading throughput for dramatically lower memory usage.

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Why Flatrun?

Flatrun is designed for developers and researchers who want to understand how LLM inference works.

It provides a transparent streaming runtime where every stage—from tensor loading to KV cache management and forward execution—can be inspected, profiled, modified, and optimized.

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Highlights

• Streaming layer-by-layer execution
• Constant-memory architecture
• Pure Python + NumPy runtime
• Optional native C++ acceleration
• GGUF, SafeTensors, and MLX support
• Q1/Q4/Q5/Q6/Q8 quantization
• Python API and interactive chat
• Layer profiler and memory tracing

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Supported Models

Family	GGUF	SafeTensors	MLX
Llama 1 / 2 / 3	✅	✅	—
Qwen2	✅	✅	—
Qwen2.5	✅	✅	—
Qwen2.5 Coder	✅	✅	—
Qwen3	✅	✅	—
Qwen3.5	✅	—	—
SmolLM2	✅	✅	—
Gemma 3	—	—	✅
Bonsai Q1_0	✅	—	—

Support for additional architectures is continuously expanding.

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Installation

Install from PyPI:

pip install flatrun

Or clone + editable install:

git clone https://github.com/flatseek/flatrun.git

cd flatrun

make install

Requirements:

• Python 3.10+
• NumPy

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Quick Start

Run a GGUF model:

flatrun \
    --model model.gguf \
    --prompt "The capital of France is" \
    --max-new 32

Interactive chat:

flatrun chat \
    --model model.gguf

Run a SafeTensors checkpoint:

flatrun \
    --model /path/to/model \
    --prompt "Explain quantum computing."

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Backend Selection

Flatrun provides two interchangeable execution backends.

Python Backend (Default)

The reference runtime is implemented entirely in pure Python and NumPy, making every stage of inference easy to inspect, profile, and modify.

flatrun \
    --backend python \
    --model model.gguf \
    --prompt "Hello"

This is the default backend, so --backend python can be omitted.

Native Backend

For higher performance, Flatrun can execute performance-critical operations using the native C++ backend located in src/flatrun_native.

flatrun \
    --backend native \
    --model model.gguf \
    --prompt "Hello"

The native backend preserves the same streaming execution pipeline while accelerating compute-intensive operations such as quantized GEMM and dequantization.

Backend options:

• --backend python (default) — Pure Python and NumPy runtime.
• --backend native — Native C++ accelerated runtime.

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Python API

Flatrun is fully scriptable. The same runtime that powers the CLI is exposed as a Python library — pick a local model file, hand it a prompt, and read the logits back. Nothing in the public surface requires downloading a model from a hub.

Full minimal example

from flatrun import KVCache, StreamingExecutor, load_huggingface
from flatrun.model import make_qwen2_forwarder
from flatrun.model.sampling import Sampler
from flatrun.runtime.backend import get_backend
from flatrun.tokenizer import auto_load

# 1. Open a local GGUF
loaded = load_huggingface("/Users/me/models/qwen3-0.6b-q4_k_m.gguf")

# 2. Tokenize
tokenizer = auto_load("/Users/me/models/qwen3-0.6b-q4_k_m.gguf")
prompt = "The capital of France is"
tokens = list(tokenizer.encode(prompt))

# 3. Build the executor
backend = get_backend("python")
forwarder = make_qwen2_forwarder(
    loaded.config, enable_dequant_cache=True, backend=backend,
)
kv_cache = KVCache(capacity=4096)
scheduler = loaded.runtime.build_scheduler(loaded.manifest.layers)
executor = StreamingExecutor(scheduler, forwarder, kv_cache=kv_cache)

# 4. Prefill + decode
sampler = Sampler(temperature=0.0001, top_k=1)
step = executor.step(tokens)
next_id = sampler.sample(step.last_hidden[-1])
out = list(tokens) + [next_id]
for _ in range(32):
    step = executor.step([next_id])
    next_id = sampler.sample(step.last_hidden[-1])
    out.append(next_id)

# 5. Decode
print(tokenizer.decode(out))

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CLI

flatrun run
flatrun chat

Supports:

• Text generation and sampling
• Python and native backends
• Runtime and cache configuration
• Quantization overrides
• Layer selection
• Profiling and debugging

See flatrun run --help for the complete command reference.

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How It Works

Model on Disk
      │
      ▼
 Load Layer
      │
      ▼
 Execute
      │
      ▼
 Update KV
      │
      ▼
Release Layer
      │
      ▼
 Next Layer

Instead of loading an entire checkpoint into memory, Flatrun streams one decoder layer at a time. Only the active layer and KV cache remain resident in memory, keeping peak RAM nearly constant regardless of model size.

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Performance

Flatrun prioritizes memory efficiency over maximum throughput.

Typical performance on modern Apple Silicon:

Model	Peak RAM	Speed
SmolLM2-360M Q8	~400 MB	~40 tok/s
Qwen3-0.6B Q4	~512 MB	~12 tok/s
Qwen3-14B Q4	~2–30 GB	~0.7–1.5 tok/s

Performance depends on:

• Model size
• Quantization
• Storage bandwidth
• Cache configuration

Profile every stage of execution using:

flatrun \
    --profile-detailed \
    --profile-save profile.json

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Native Backend

While the reference runtime is intentionally written in pure Python, Flatrun also includes an optional native backend located in src/flatrun_native.

The native backend accelerates performance-critical components using C++ while preserving the exact same streaming architecture and runtime API.

Current focus includes:

• Quantized GEMM kernels (fused dequant + matmul)
• Per-quant coverage: Q4_K, Q6_K, Q8_0
• SIMD vectorization (ARM NEON 128-bit intrinsics)
• Multi-threaded row-parallel matmul (std::thread pool, one per call)
• Batched matmul API for prefill (no per-token Python overhead)
• Reduced Python overhead via pybind11 marshalling

The native backend is selected with --backend native. When the C++ extension is not built (no pybind11 toolchain available), or when a specific kernel rejects an unusual layout, the call falls back to the numpy path transparently — --backend native is always safe.

Each supported quant type has a parity test in src/flatrun_native/tests/:

Quant	Test	Tolerance
Q4_K	test_q4_k_parity.py	~3e-6 max rel
Q6_K	test_q6_k_parity.py	~3e-4 max rel
Q8_0	test_q8_0_parity.py	~2e-5 max rel

See docs/native.md for the kernel design notes and the multi-threading / batched-API rationale.

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Research Focus

Flatrun serves as a platform for exploring new ideas in AI runtime design.

Current research areas include:

• Streaming inference
• Layer scheduling
• Memory-aware execution
• Quantized inference
• Dequantization optimization
• KV cache architectures
• Native kernel acceleration
• Runtime instrumentation
• Pure NumPy performance

The codebase is intentionally designed to be readable, modular, and easy to modify, making it suitable for experimentation, benchmarking, and runtime research.

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Limitations

Current limitations include:

• CPU-only execution
• Pure NumPy reference runtime
• No CUDA backend
• No Metal kernels
• Lower throughput than GPU runtimes
• Some architectures are still under active development

Flatrun is built primarily for experimentation, research, and runtime innovation rather than large-scale production serving.

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License

Apache License 2.0

See LICENSE.