mlx-flash 0.6.1

Run AI models too large for your Mac's memory — expert caching, speculative execution, and 15+ research techniques for MoE inference on Apple Silicon

MLX-Flash

Run AI models too large for your Mac's memory — at near-full speed.

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Your MacBook has 32-48GB of RAM, but the best AI models need 100-200GB+. MLX-Flash makes them run anyway by intelligently caching the most-needed parts in RAM and streaming the rest from your SSD.

How It Works

Think of it like Netflix streaming: instead of downloading the entire movie before watching, you buffer what you need and stream the rest. MLX-Flash does this for AI model weights:

┌─────────────────────────────────┐
│    Your Mac's RAM (fast)        │
│  ┌──────────┐ ┌──────────────┐  │
│  │Hot Cache  │ │Mixed Precis. │  │
│  │85%+ hits  │ │4-bit / 2-bit │  │
│  └──────────┘ └──────────────┘  │
└──────────────┬──────────────────┘
               │ cache hit: 0.08ms
┌──────────────┴──────────────────┐
│    Smart Cache Layer            │
│  • LCP Eviction (layer-biased) │
│  • Speculative Prefetch (97%)  │
│  • Memory Monitor              │
│  • Speculative Execution       │
└──────────────┬──────────────────┘
               │ cache miss: 0.6ms
┌──────────────┴──────────────────┐
│    Your SSD (big)               │
│  Full model weights — 200GB+   │
│  Entropy-coded — 65% smaller   │
└─────────────────────────────────┘
         │
         ▼
   MLX GPU Inference

Result: A 200GB AI model runs on your 48GB Mac at 2-3x faster than naive SSD streaming.

Quick Start

pip install mlx-flash

# Interactive chat
mlx-flash-chat

# API server (works with LM Studio, Cursor, Claude Code, Codex, OpenAI SDK)
mlx-flash --port 8080

# With KV cache quantization (45% less KV memory)
mlx-flash --port 8080 --kv-bits 8

# See what models fit your hardware
mlx-flash-browse

Performance

Technique	Speedup	How It Works
LCP Smart Cache	2.80x	Keeps frequently-used model parts in RAM
+ Async Prefetch	2.93x	Loads next part from SSD while GPU computes
Mixed Precision	1.80x smaller	Rarely-used parts stored at lower quality
Skip Fallback	2.67x	Gracefully skip uncached parts instead of waiting
Speculative Execution	14-42% TPOT	Execute predicted experts before router confirms
Adaptive Top-K	10-30% compute	Skip low-confidence secondary experts

Real Hardware (M3 Max 36GB)

Memory pressure recovery:
  Without optimization:    43.5 tok/s
  With mixed precision:   104.5 tok/s  → 2.4x faster

Cache warm-up:
  Token  0:  83.3ms (cold start)
  Token  8:   5.7ms (warming up)
  Token 24:   0.5ms (full speed) → 41x speedup

What's Inside

35 Python modules + Rust sidecar implementing 15+ research techniques:

Category	Modules
Expert Streaming	GPU lookup tables, speculative execution, skip-fallback, adaptive top-k
Prediction (97%+)	Residual-stream predictor, shadow MLP, cross-layer 3-hop prefetch
Cache Management	Layer-biased LCP, Belady-optimal eviction, vertical splitting, expert merging
Compression	Entropy coding (Huffman uint4), mixed precision (4-bit/2-bit)
Memory	Real-time pressure monitoring, wired memory optimization, mx.clear_cache()
Serving	OpenAI-compatible API, KV cache 8-bit quantization, SSE streaming
Rust Sidecar	axum HTTP/SSE, mach2 memory (0.1ms), DashMap LCP, Unix socket bridge

Integration

Works with any OpenAI-compatible tool:

# Start server
mlx-flash --port 8080 --preload

# Point any tool at it
# LM Studio: Settings → Server → http://localhost:8080/v1
# Cursor: Settings → Models → OpenAI Compatible → http://localhost:8080/v1
# Claude Code: OPENAI_API_BASE=http://localhost:8080/v1
# continue.dev: apiBase: http://localhost:8080/v1

# Python SDK
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8080/v1", api_key="not-needed")
response = client.chat.completions.create(
    model="local",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

Expert Streaming (for large MoE models)

from mlx_flash_compress.expert_streaming import (
    enable_expert_streaming, enable_skip_fallback
)

# Enable streaming with 50% capacity + adaptive skipping
streaming = enable_expert_streaming(model, capacity_per_layer=64)
enable_skip_fallback(model, streaming.caches, adaptive_skip_threshold=3.0)
streaming.warmup()

Research Techniques Implemented

From 15+ papers (2024-2026):

Technique	Paper	Status
Expert streaming (GPU lookup)	HOBBIT arXiv:2411.01433	Implemented
Residual-stream predictor (97%+)	Speculating Experts arXiv:2603.19289	Implemented
Speculative execution (14-42% TPOT)	MoE-SpAc arXiv:2603.09983	Implemented
Belady-optimal eviction	MoE-SpeQ arXiv:2511.14102	Implemented
Cross-layer 3-hop prefetch	FATE arXiv:2502.12224	Implemented
Layer-depth cache bias	FATE arXiv:2502.12224	Implemented
Vertical expert splitting (2x coverage)	MoEpic paper	Implemented
Expert merging (15-30% fewer params)	DEK/EEP arXiv:2509.19781	Implemented
Entropy coding (65% compression)	EntroLLM arXiv:2505.02380	Implemented
Adaptive top-k (10-30% compute savings)	LExI arXiv:2509.02753	Implemented
Mixed precision per-expert	HOBBIT arXiv:2411.01433	Implemented
KV cache 8-bit quantization	mlx-moe / mlx-lm	Implemented

Requirements

• macOS with Apple Silicon (M1/M2/M3/M4/M5)
• Python 3.10+
• 16GB+ RAM (more = better caching = faster)

Project Stats

• 15,000+ lines of code (Python + Rust)
• 224 tests (192 Python + 32 Rust)
• 35 Python modules + Rust sidecar
• 15+ research papers implemented

Links

• GitHub
• Documentation
• Integration Guide
• Competitive Analysis

License

MIT