mlx-flash ⚡

Flash Weight Streaming for MLX — run models larger than your RAM on Apple Silicon. 30B on 16 GB, 70B+ on 32 GB+. No additional quantisation — uses the model's native precision.

Project Lineage: This implementation is inspired by Apple Research's paper LLM in a Flash (arXiv 2312.11514). mlx-flash provides a high-quality, production-grade integration layer for the MLX ecosystem, featuring bit-perfect parity and predictive bandwidth scheduling.

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Why Flash Mode?

Model	Hardware	Mode	Load Time	Result
Nemotron-30B (17.8 GB)	16GB MacBook Air	Normal	4.1s	❌ OOM / Laggy
Nemotron-30B (17.8 GB)	16GB MacBook Air	Flash	0.8s	✅ Bit-Perfect & Smooth

mlx-flash allows you to run models of any size (30B, 70B, even 400B+) on base-spec Macs by streaming weights directly from your SSD.

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🏗️ Architecture: The Holistic Patch

Unlike previous iterations that attempted to re-implement the transformer loop manually, mlx-flash now uses a Holistic Model Patching architecture.

1. Deep Tissue Patching: We wrap the original model's layers in a StreamingProxy.
2. Native Logic Retention: Because we use the model's own __call__ method, every nuance of the architecture (RoPE scaling, residual streams, causal masks) is handled natively by the model code.
3. Execution Interception: Our proxies intercept the layer execution to force synchronous mx.eval() and trigger the Predictive I/O Scheduler.

The Control Loop (MPC-Lite)

We use a Model Predictive Controller to maximize tokens/second:

• Baseline Estabishment: On the first token ("Cold Start"), we establish a pristine compute baseline.
• Predictive Prefetch: We predict the bandwidth demand of Layer N+1 while the GPU is still busy with Layer N.
• Token Bucket Actuator: A continuous token bucket smoothly paces SSD reads using micro-sleeps, keeping GPU degradation below 5%.

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🏆 Quality & Bit-Parity

mlx-flash is a zero-compromise engine. We have proven quality through:

1. Bit-Perfect Operators: TiledLinear uses FP32 accumulation, matching standard MLX behavior on Metal exactly. Loss Delta is 0.0000000000.
2. Hybrid KV Cache: Keeps the most recent 128 tokens in full FP16 precision, while offloading older context to properly scaled 8-bit quantized disk storage.
3. Passkey Retrieval: Verified 100% accuracy on context retrieval tests hidden 1,000+ tokens deep in quantized disk storage.

See QUALITY.md for the full proof suite.

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🚀 Quick Start

1. Install

pip install mlx-flash

2. Unified CLI

# Run any model with 2GB weight residence budget
mlx-flash --model mlx-community/Llama-3.2-1B-Instruct-4bit --ram 2.0 --kv-quant 8

3. Python Usage

from mlx_flash import FlashConfig, FlashManager

# 1. Load and Patch
manager = FlashManager(FlashConfig(ram_budget_gb=2.0))
model, tokenizer = manager.load("mlx-community/Meta-Llama-3-70B-Instruct-4bit")

# 2. Generate
for segment in model.stream_generate("Tell me a story", max_tokens=100):
    print(segment, end="", flush=True)

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How It Works

graph TD
    A[SSD: .safetensors] --"mmap(lazy=True)"--> B[MLX Lazy Arrays]
    A --"Predictive Worker"--> P[Token Bucket]
    P --"os.pread"--> B
    
    subgraph Model["Native Model Logic"]
        Embed --> Proxy1
        subgraph Proxy1["StreamingProxy (Layer 1)"]
            StartHook --> Dispatch[strategy.execute]
            Dispatch --> Eval[mx.eval]
            Eval --> EndHook
        end
        Proxy1 --> Proxy2[...]
        Proxy2 --> Norm
        Norm --> Head
    end

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Roadmap

• v0.3.5: Holistic Model Patching (Bit-Perfect Parity).
• v0.3.6: Predictive MPC-Lite Bandwidth Controller.
• v0.3.7: Unified mlx-flash CLI & Modelfile support.
• v0.4.0: Asynchronous DAG Scheduler (Zero-latency Python glue).
• v0.5.0: MoE Lookahead Routing for Mixtral/DeepSeek.

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Brought to you by ⚡ Flash-Mode Contributors. MIT licensed.