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   Run models too big for your Mac's memory

Hypura is a storage-tier-aware LLM inference scheduler for Apple Silicon. It places model tensors across GPU, RAM, and NVMe tiers based on access patterns, bandwidth costs, and hardware capabilities — enabling models that exceed physical memory to run without crashing the system.

Run a 31 GB Mixtral 8x7B on a 32 GB Mac Mini at 2.2 tok/s. A 40 GB Llama 70B at 0.3 tok/s. Vanilla llama.cpp crashes on both.

Consumer hardware (MacBook Pro, Mac Studio) ships with fast unified memory and NVMe storage, but limited capacity. A 32 GB M1 Max cannot naively load a 40 GB model — the OS will swap-thrash until the OOM killer intervenes.

Hypura solves this by understanding the model architecture:

• Norms and embeddings are tiny but accessed every token — pinned to GPU
• MoE expert routing exploits sparsity — only 2 of 8 experts fire per token. Router interception identifies selected experts in the eval callback, then loads only the needed expert strides from NVMe (75% I/O reduction). A neuron cache tracks loaded expert slices across tokens, achieving 99.5% hit rate from temporal locality. Co-activation tracking predicts which experts will fire next for speculative prefetch.
• Dense FFN weights (gate, up, down — ~60% of model size) stream from NVMe through a dynamically-sized pool buffer while attention + norms stay GPU-resident. Prefetch lookahead depth scales automatically with available memory.

The result: models that would crash your machine under naive mmap become runnable. Models that fit in memory run at full Metal GPU speed with zero overhead.

Hypura reads the GGUF file, profiles your hardware (GPU working set, RAM, NVMe bandwidth), and solves a placement optimization that assigns every tensor to a tier:

• GPU (Metal) — Attention layers, norms, embeddings. Fastest access, limited by recommendedMaxWorkingSetSize.
• RAM — Overflow layers that don't fit in the GPU working set. Accessed via mmap.
• NVMe — Remaining layers loaded on-demand via direct I/O (F_NOCACHE + pread), prefetched ahead of the forward pass.

Hypura selects the best inference mode automatically based on model size, architecture, and available memory:

• Full-resident — Model fits in GPU+RAM. No NVMe I/O. Full Metal speed.
• Expert-streaming — For MoE models (Mixtral). Only non-expert tensors (~1 GB) stay on GPU. Expert tensors stream from NVMe through a pool buffer on demand, with a neuron cache (99.5% hit rate) that eliminates most I/O after warmup.
• Dense FFN-streaming — For dense models too large for GPU (Llama 70B). Attention + norms stay on GPU (~8 GB). FFN tensors (~32 GB) stream from NVMe through a dynamically-sized pool buffer, with scaled prefetch lookahead.

Pool buffer size, prefetch depth, and memory budgets are computed automatically from your hardware profile — no manual tuning needed.

All benchmarks on M1 Max, 32 GB unified memory, ~5.1 GB/s NVMe sequential read.

Key takeaway: For models that fit in memory, Hypura adds zero overhead. For models that don't fit, Hypura is the difference between "runs" and "crashes." Expert-streaming on Mixtral achieves usable interactive speeds by keeping only non-expert tensors on GPU and exploiting MoE sparsity (only 2/8 experts fire per token). Dense FFN-streaming extends this to non-MoE models like Llama 70B. Pool sizes and prefetch depth scale automatically with available memory.

Hypura builds from source with Cargo. You'll need Rust 1.75+ and CMake (for the vendored llama.cpp).

git clone --recurse-submodules https://github.com/hypura/hypura.git
cd hypura
cargo build --release

The binary is at target/release/hypura.

Homebrew tap coming soon.

# Profile your hardware (runs once, cached)
hypura profile

# Run inference on a GGUF model
hypura run ./model.gguf --prompt "Hello, world"

# Interactive chat
hypura run ./model.gguf --interactive

# Benchmark: Hypura scheduling vs naive baseline
hypura bench ./model.gguf

# Inspect model placement plan without loading
hypura inspect ./model.gguf

Start with --max-tokens 10 on untested models before scaling up.

Hypura exposes an Ollama-compatible HTTP API, making it a drop-in replacement for any tool that talks to Ollama — including OpenClaw.

hypura serve ./model.gguf
# Hypura serving Mixtral 8x7B Instruct v0.1
#   Endpoint: http://127.0.0.1:8080
#   Ollama-compatible API: /api/generate, /api/chat, /api/tags

Point OpenClaw at Hypura by setting the Ollama base URL in ~/.openclaw/openclaw.json:

{
  "models": {
    "providers": {
      "ollama": {
        "baseUrl": "http://127.0.0.1:8080",
        "api": "ollama"
      }
    }
  }
}

Or via the CLI:

openclaw config set models.providers.ollama.baseUrl "http://127.0.0.1:8080"

Hypura speaks native Ollama protocol (/api/chat with NDJSON streaming), so no compatibility shims are needed.

hypura serve <MODEL> [OPTIONS]

Options:
  --host <HOST>        Host to bind to [default: 127.0.0.1]
  --port <PORT>        Port to bind to [default: 8080]
  --context <N>        Maximum context length [default: 4096]

Hypura is a Cargo workspace with two crates:

• hypura — Main binary and library. CLI in src/main.rs, all logic in src/lib.rs modules.
• hypura-sys — FFI bindings to llama.cpp (vendored at vendor/llama.cpp/, built via CMake).

No. Hypura only reads from your SSD during inference — it never writes to it.

SSD wear is caused by write cycles (program/erase cycles on NAND flash cells). Reads do not degrade flash cells. Hypura's entire NVMe I/O path uses read-only pread() calls with F_NOCACHE to stream tensor weights from the GGUF file into RAM/GPU memory pools, where all computation happens. The SSD is used as cold storage, not as working memory.

The only writes Hypura performs are negligible: benchmark result JSON files (~KB), co-activation statistics (~KB to ~/.hypura/), and the one-time hypura optimize command if you choose to run it. Normal inference generates zero SSD writes.

• bench --baseline is blocked when the model exceeds RAM minus 4 GB headroom. Use --force to override at your own risk.
• Always start with --max-tokens 10 on untested models.
• Test models belong in ./test-models/ (not checked in).

MIT

I feel morally obligated to say I did not write the code in this repository myself. This project is an exploration of using LLMs to carry out tasks based on my direction. The majority of prompts I used to get here were derived using the socratic method, genuine curiosity, and a hunch that NVMe supporting inference is underutilized despite being a (slow but) perfectly valid form of memory.