Mach-1-Additive-35B — GGUF
GGUF build of Mach-1-Additive-35B for local inference on consumer hardware (NVIDIA CUDA, Windows/Linux Vulkan, CPU; on Apple Silicon the codec runs on CPU — use Mach Studio for GPU inference on macOS).
[!IMPORTANT] This GGUF requires the Mach-1 fork of llama.cpp: SyzygyResearch/llama.cpp-mach1. Mainline llama.cpp will not load it, our model uses custom trellis-coded tensor payloads and decode ops that only exist in the fork.
Files
| File | Size | Notes |
|---|---|---|
Mach-1-Additive-35B.mach1.gguf |
7.87 GB | text model, payload v3 (additive) |
Quick start
Build the fork — pick the backend for your hardware (NVIDIA → CUDA, AMD/Intel → Vulkan):
git clone https://github.com/SyzygyResearch/llama.cpp-mach1
cd llama.cpp-mach1
# NVIDIA (requires the CUDA toolkit)
cmake -B build -DGGML_CUDA=ON
# AMD / Intel / other (requires the Vulkan SDK, incl. glslc)
cmake -B build -DGGML_VULKAN=ON
cmake --build build --config Release -j
Run directly from Hugging Face:
./build/bin/llama-cli -hf SyzygyResearch/Mach-1-Additive-35B-GGUF
or from a local download:
# interactive chat
./build/bin/llama-cli -m Mach-1-Additive-35B.mach1.gguf
# single-turn / scripted use (without -st, llama-cli enters interactive
# conversation mode after answering the prompt)
./build/bin/llama-cli -m Mach-1-Additive-35B.mach1.gguf -st -p "your prompt"
Benchmarks
| Mean retention, 12 benchmarks | |
|---|---|
| Mach-1 Small | 96.3% |
| Ternary Bonsai 27B (PrismML) | 93.6% |
| Gemma 4 Q2_K_XL (Unsloth) | 85.6% |
Per-benchmark Retention (Score divided by that of the respective full precision model)
| Benchmark | Mach-1 Small | Ternary Bonsai 27B | Gemma 4 Q2_K_XL |
|---|---|---|---|
| AIME26 | 95.8% | 92.7% | 67.7% |
| MATH-500 | 99.2% | 98.2% | 95.6% |
| AIME25 | 93.9% | 91.7% | 67.2% |
| GSM8K | 99.8% | 100.2% | 97.3% |
| MBPP+ | 98.1% | 98.4% | 92.2% |
| HumanEval+ | 96.5% | 98.7% | 94.1% |
| MMLU-Redux | 97.0% | 94.0% | 96.9% |
| IFEval | 94.8% | 89.8% | 95.5% |
| MuSR | 100.4% | 91.6% | 91.1% |
| BFCL-v3 | 96.3% | 98.9% | 95.7% |
| τ²-bench | 94.1% | 91.2% | 73.1% |
| IFBench | 89.8% | 77.7% | 61.3% |
| Mean | 96.3% | 93.6% | 85.6% |
Mach-1 Small's own scores and teacher scores:
| Benchmark | Score | Teacher (Qwen3.6-35B-A3B BF16) | Retention |
|---|---|---|---|
| AIME26 | 86.25 | 90.00 | 95.8% |
| MATH-500 | 97.80 | 98.60 | 99.2% |
| AIME25 | 82.92 | 88.33 | 93.9% |
| GSM8K | 95.98 | 96.21 | 99.8% |
| MBPP+ | 94.18 | 96.03 | 98.1% |
| HumanEval+ | 91.77 | 95.12 | 96.5% |
| MMLU-Redux | 89.88 | 92.68 | 97.0% |
| IFEval | 84.42 | 89.05 | 94.8% |
| MuSR | 66.93 | 66.66 | 100.4% |
| BFCL-v3 | 72.19 | 74.98 | 96.3% |
| τ²-bench | 74.82 | 79.51 | 94.1% |
| IFBench | 58.33 | 64.97 | 89.8% |