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docs: collapse WINDOWS.md/windows.md case collision into one file (#371)

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docs/WINDOWS.md (the #329 install walkthrough) and docs/windows.md (the concise
reference from the README restructure) differ only in case. On case-insensitive
filesystems — every default Windows and macOS checkout — the two paths collide:
the working tree can never be clean, and `git rebase`/`git status` refuse to
run. @KingIcyCreamProjects reported it.

Everything links to the lowercase docs/windows.md (README.md, README.zh-TW.md,
docs/cuda.md), so that is the canonical name. This keeps it and folds in both
contents: the step-by-step walkthrough (download-first, Smart App Control, CUDA
DLL, first run, reference numbers, failure index) followed by the build-flags
reference (AVX-VNNI, warmup). docs/WINDOWS.md is removed. No links change.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
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2026-07-18 01:15:48 +02:00
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# Windows 11 native install — a complete walkthrough (no WSL)
A start-to-finish, reproducible path from a fresh Windows 11 machine to GLM-5.2 generating tokens, with the GPU tier. Every step and every failure mode below was hit and verified on real hardware: Core Ultra 9 285K (AVX-VNNI) / RTX 5080 (sm_120) / 128 GB RAM / Windows 11 24H2 (issue #306). Steps are ordered so the long downloads run while you build.
## 0. What you need
| Piece | Why | Get it |
|---|---|---|
| git, Python 3 | clone + `coli` launcher | winget / python.org |
| MinGW-w64 gcc + make | builds the engine (MSVC can't) | `scoop install mingw-winlibs`, MSYS2, or portable **w64devkit** (no admin, unzip and go) |
| CUDA Toolkit ≥ 12.8 | GPU tier; ≥12.8 required for Blackwell/sm_120 | `winget install Nvidia.CUDA` |
| MSVC Build Tools (C++ workload) | nvcc's host compiler for the CUDA DLL | `winget install Microsoft.VisualStudio.2022.BuildTools` + "Desktop development with C++" |
| ~400 GB free on a local NVMe | the int4 model (~370–384 GB) | NTFS is fine; **never** a network mount |
RAM: 16 GB minimum, more = bigger expert cache = faster. The build itself needs none of the CUDA/MSVC pieces — do the CPU build first, add the GPU tier later.
## 1. Start the model download first (it's the long pole)
```powershell
python -m pip install -U "huggingface_hub[hf_transfer]"
$env:HF_HUB_ENABLE_HF_TRANSFER = "1"
hf download <model-repo> --local-dir D:\glm52_i4
```
Use the container recommended in the README (with **int8 MTP heads** — int4 heads silently give 0% draft acceptance). The download is resumable: if it stops, rerun the same command. Expect hours; everything below fits inside them.
## 2. Build the engine (CPU)
From a normal PowerShell, in the repo's `c\` directory:
```powershell
make glm.exe ARCH=native # ARCH=native unlocks AVX-VNNI on Alder Lake+/Arrow Lake
make iobench.exe # disk benchmark, useful before committing to the download
```
Warnings about `#pragma comment` and unused variables are normal (MSVC-isms gcc ignores). The engine banner should print `idot: avx-vnni` on VNNI-capable CPUs — if it says avx2, you built without `ARCH=native`.
### ⚠️ Smart App Control will block your fresh binary
On Windows 11 machines with **Smart App Control** enforced (`VerifiedAndReputablePolicyState = 1`), running your self-compiled `glm.exe` fails with:
```
Program 'glm.exe' failed to run: An Application Control policy has blocked this file
```
This is not Defender and not Mark-of-the-Web — SAC blocks *all* unsigned, unknown binaries, which includes anything you compile yourself. **Fix:** Windows Security → App & browser control → Smart App Control settings → **Off**, then **reboot** (the policy only reloads on restart). Note SAC is one-way: re-enabling later requires resetting Windows. If the settings page is missing, the registry equivalent is setting `HKLM:\SYSTEM\CurrentControlSet\Control\CI\Policy\VerifiedAndReputablePolicyState` to `0` (admin PowerShell), then rebooting. Check your current state before touching anything:
```powershell
(Get-ItemProperty "HKLM:\SYSTEM\CurrentControlSet\Control\CI\Policy").VerifiedAndReputablePolicyState
# 0 = off, 1 = enforced, 2 = evaluation
```
## 3. Build the CUDA DLL (GPU tier)
nvcc needs MSVC as host compiler, so this one step must run from a shell with the MSVC environment: open **"x64 Native Tools Command Prompt for VS 2022"** from the Start menu (plain PowerShell will fail the `cl` check). Then:
```cmd
make cuda-dll CUDA_ARCH=sm_120 # match your GPU: sm_120 Blackwell, sm_89 Ada, ...
make glm.exe CUDA_DLL=1 ARCH=native # relink host with the runtime loader
```
Two pitfalls, both fixed on current `dev` (#314) but worth knowing on older checkouts:
- **Spaces in `CUDA_HOME`** (`C:\Program Files\...`) used to break the recipe → fixed; nvcc now comes from PATH and `"$(NVCC)"` is quoted.
- **`make glm.exe CUDA_DLL=1` after a CPU-only build** used to report `up to date` and silently keep the CPU-only binary (GPU tier never engages, no error). Current `dev` has a build-config stamp that forces the relink. On older trees: delete `glm.exe` first.
Sanity check: first GPU run should print `[CUDA] device 0: <your GPU>, ... sm_XX` and `[CUDA] mode: routed experts + resident dense tensors`.
## 4. First run
```powershell
cd <repo>\c
$env:OMP_NUM_THREADS = "<physical cores>"
python coli run "Explain what a mixture-of-experts model is." --model D:\glm52_i4 --ngen 48
```
The first run is cold — expect the profile to be dominated by `expert-disk` while the cache warms; hit rate climbs run over run. GPU tier on top:
```powershell
$env:COLI_CUDA="1"; $env:COLI_GPU="0"; $env:CUDA_DENSE="1"; $env:CUDA_EXPERT_GB="4"
python coli run "..." --model D:\glm52_i4 --ngen 64
```
Size `CUDA_EXPERT_GB` so dense (~10 GB) + experts + working set stays under your VRAM. Note MTP speculation is off by default under CUDA (#293, float-accumulation divergence between draft and verify) — `COLI_CUDA_MTP=1` opts back in.
## 5. Reference numbers from this walkthrough's hardware
285K / RTX 5080 / 128 GB / NVMe at 5.85 GB/s random-read (19 MB blocks, `iobench`): 0.26 tok/s cold CPU → 0.30 warm CPU (MTP 2.2–2.3 tok/forward) → 0.42 tok/s GPU tier + auto-pin, expert hit 66%, ~65% of wall time in expert-disk. Disk-bound is the expected shape at ~25% expert residency — a faster disk and more RAM move the floor, the GPU moves the compute.
## Quick failure index
| Symptom | Cause | Fix |
|---|---|---|
| `An Application Control policy has blocked this file` | Smart App Control | §2 — turn SAC off + **reboot** |
| `cuda-dll ... Error 1` immediately | old tree: spaced CUDA_HOME / MSVC rejects `-Wextra` | update to current `dev` (#314) |
| `glm.exe is up to date` but GPU never engages | old tree: stale CPU-only binary | update to `dev`, or delete `glm.exe` and rebuild |
| `cl.exe (MSVC) not in PATH` | built from plain PowerShell | use the x64 Native Tools prompt |
| `nvcc fatal: unsupported gpu architecture 'sm_120'` | CUDA < 12.8 | install CUDA 12.8+ |
| MTP `0% (0/0)` on CPU path | int4 MTP heads in the container | use the int8-MTP container |
| MTP `draft=0` under CUDA | intended default since #293 | `COLI_CUDA_MTP=1` to opt in |
docs/windows.md
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# Windows 11 (native, no WSL)
# Windows 11 native install — a complete walkthrough (no WSL)
colibrì builds and runs natively on Windows 11 x86-64 with MinGW-w64. The port
adds a `_WIN32` compatibility layer in `c/compat.h` that maps POSIX I/O to the
Windows API (pread → ReadFile+OVERLAPPED, posix_fadvise no-op, aligned
allocation, MoveFileEx rename, GlobalMemoryStatusEx RAM detection). All platform
differences stay in `compat.h`; the engine source is unchanged.
A start-to-finish, reproducible path from a fresh Windows 11 machine to GLM-5.2 generating tokens, with the GPU tier. Every step and every failure mode below was hit and verified on real hardware: Core Ultra 9 285K (AVX-VNNI) / RTX 5080 (sm_120) / 128 GB RAM / Windows 11 24H2 (issue #306). Steps are ordered so the long downloads run while you build.
**Toolchain:** GCC via [winlibs](https://winlibs.com/) or MSYS2 MinGW-w64.
Tested with GCC 16.1.0 (x86_64-ucrt-posix-seh).
## 0. What you need
| Piece | Why | Get it |
|---|---|---|
| git, Python 3 | clone + `coli` launcher | winget / python.org |
| MinGW-w64 gcc + make | builds the engine (MSVC can't) | `scoop install mingw-winlibs`, MSYS2, or portable **w64devkit** (no admin, unzip and go) |
| CUDA Toolkit ≥ 12.8 | GPU tier; ≥12.8 required for Blackwell/sm_120 | `winget install Nvidia.CUDA` |
| MSVC Build Tools (C++ workload) | nvcc's host compiler for the CUDA DLL | `winget install Microsoft.VisualStudio.2022.BuildTools` + "Desktop development with C++" |
| ~400 GB free on a local NVMe | the int4 model (~370–384 GB) | NTFS is fine; **never** a network mount |
RAM: 16 GB minimum, more = bigger expert cache = faster. The build itself needs none of the CUDA/MSVC pieces — do the CPU build first, add the GPU tier later.
## 1. Start the model download first (it's the long pole)
```powershell
# One-time toolchain install (pick one):
scoop install mingw-winlibs # portable, no shell needed
# or: pacman -S mingw-w64-x86_64-gcc make # via MSYS2
python -m pip install -U "huggingface_hub[hf_transfer]"
$env:HF_HUB_ENABLE_HF_TRANSFER = "1"
hf download <model-repo> --local-dir D:\glm52_i4
```
# Build (from c/ directory):
make glm.exe # GLM-5.2 engine (static, no DLL dependencies)
make olmoe.exe # OLMoE engine (same shims)
make iobench.exe # disk I/O benchmark
make test-c # run C tests
make test-python # run Python tests (requires python)
Use the container recommended in the README (with **int8 MTP heads** — int4 heads silently give 0% draft acceptance). The download is resumable: if it stops, rerun the same command. Expect hours; everything below fits inside them.
## 2. Build the engine (CPU)
From a normal PowerShell, in the repo's `c\` directory:
```powershell
make glm.exe ARCH=native # ARCH=native unlocks AVX-VNNI on Alder Lake+/Arrow Lake
make iobench.exe # disk benchmark, useful before committing to the download
```
Warnings about `#pragma comment` and unused variables are normal (MSVC-isms gcc ignores). The engine banner should print `idot: avx-vnni` on VNNI-capable CPUs — if it says avx2, you built without `ARCH=native`.
### ⚠️ Smart App Control will block your fresh binary
On Windows 11 machines with **Smart App Control** enforced (`VerifiedAndReputablePolicyState = 1`), running your self-compiled `glm.exe` fails with:
```
Program 'glm.exe' failed to run: An Application Control policy has blocked this file
```
This is not Defender and not Mark-of-the-Web — SAC blocks *all* unsigned, unknown binaries, which includes anything you compile yourself. **Fix:** Windows Security → App & browser control → Smart App Control settings → **Off**, then **reboot** (the policy only reloads on restart). Note SAC is one-way: re-enabling later requires resetting Windows. If the settings page is missing, the registry equivalent is setting `HKLM:\SYSTEM\CurrentControlSet\Control\CI\Policy\VerifiedAndReputablePolicyState` to `0` (admin PowerShell), then rebooting. Check your current state before touching anything:
```powershell
(Get-ItemProperty "HKLM:\SYSTEM\CurrentControlSet\Control\CI\Policy").VerifiedAndReputablePolicyState
# 0 = off, 1 = enforced, 2 = evaluation
```
## 3. Build the CUDA DLL (GPU tier)
nvcc needs MSVC as host compiler, so this one step must run from a shell with the MSVC environment: open **"x64 Native Tools Command Prompt for VS 2022"** from the Start menu (plain PowerShell will fail the `cl` check). Then:
```cmd
make cuda-dll CUDA_ARCH=sm_120 # match your GPU: sm_120 Blackwell, sm_89 Ada, ...
make glm.exe CUDA_DLL=1 ARCH=native # relink host with the runtime loader
```
Two pitfalls, both fixed on current `dev` (#314) but worth knowing on older checkouts:
- **Spaces in `CUDA_HOME`** (`C:\Program Files\...`) used to break the recipe → fixed; nvcc now comes from PATH and `"$(NVCC)"` is quoted.
- **`make glm.exe CUDA_DLL=1` after a CPU-only build** used to report `up to date` and silently keep the CPU-only binary (GPU tier never engages, no error). Current `dev` has a build-config stamp that forces the relink. On older trees: delete `glm.exe` first.
Sanity check: first GPU run should print `[CUDA] device 0: <your GPU>, ... sm_XX` and `[CUDA] mode: routed experts + resident dense tensors`.
## 4. First run
```powershell
cd <repo>\c
$env:OMP_NUM_THREADS = "<physical cores>"
python coli run "Explain what a mixture-of-experts model is." --model D:\glm52_i4 --ngen 48
```
The first run is cold — expect the profile to be dominated by `expert-disk` while the cache warms; hit rate climbs run over run. GPU tier on top:
```powershell
$env:COLI_CUDA="1"; $env:COLI_GPU="0"; $env:CUDA_DENSE="1"; $env:CUDA_EXPERT_GB="4"
python coli run "..." --model D:\glm52_i4 --ngen 64
```
Size `CUDA_EXPERT_GB` so dense (~10 GB) + experts + working set stays under your VRAM. Note MTP speculation is off by default under CUDA (#293, float-accumulation divergence between draft and verify) — `COLI_CUDA_MTP=1` opts back in.
## 5. Reference numbers from this walkthrough's hardware
285K / RTX 5080 / 128 GB / NVMe at 5.85 GB/s random-read (19 MB blocks, `iobench`): 0.26 tok/s cold CPU → 0.30 warm CPU (MTP 2.2–2.3 tok/forward) → 0.42 tok/s GPU tier + auto-pin, expert hit 66%, ~65% of wall time in expert-disk. Disk-bound is the expected shape at ~25% expert residency — a faster disk and more RAM move the floor, the GPU moves the compute.
## Quick failure index
| Symptom | Cause | Fix |
|---|---|---|
| `An Application Control policy has blocked this file` | Smart App Control | §2 — turn SAC off + **reboot** |
| `cuda-dll ... Error 1` immediately | old tree: spaced CUDA_HOME / MSVC rejects `-Wextra` | update to current `dev` (#314) |
| `glm.exe is up to date` but GPU never engages | old tree: stale CPU-only binary | update to `dev`, or delete `glm.exe` and rebuild |
| `cl.exe (MSVC) not in PATH` | built from plain PowerShell | use the x64 Native Tools prompt |
| `nvcc fatal: unsupported gpu architecture 'sm_120'` | CUDA < 12.8 | install CUDA 12.8+ |
| MTP `0% (0/0)` on CPU path | int4 MTP heads in the container | use the int8-MTP container |
| MTP `draft=0` under CUDA | intended default since #293 | `COLI_CUDA_MTP=1` to opt in |
---
## Reference: build flags & warmup
# AVX-VNNI: Intel Alder Lake+ (and Meteor Lake+) CPUs have a 128-bit int8
# dot-product instruction (VPDPBUSD) the engine can use for ~1.3x faster