Add Vulkan longhaul support

docs/longhaul.md

@@ -10,11 +10,13 @@ This mode is intended for running a model whose full expert weights do not fit i
 llama-cli \
     --model /path/to/model.gguf \
     --longhaul \
-    --longhaul-cache 2 \
+    --longhaul-cache 0.5 \
     --n-gpu-layers 99
 ```
 
-`--longhaul-cache` is the expert cache budget in GiB. It is required when `--longhaul` is used. The same options are accepted by `llama-server`.
+`--longhaul-cache` is the expert cache budget in GiB and accepts decimal values.
+It is required when `--longhaul` is used. The same options are accepted by
+`llama-server`.
 
 Longhaul may reduce `--ubatch-size` so that every expert selected by one graph segment can be present in the cache at the same time. The effective value is logged during context creation. If one token selects more experts than the cache has slots, the routed MoE computation is split into multiple stages and the partial results are summed. This permits smaller caches at the cost of additional graph work.
 
@@ -24,23 +26,81 @@ The normal startup warmup is skipped automatically in longhaul mode. Routed expe
 
 Longhaul currently requires:
 
-- macOS with the Metal backend
-- Qwen3.5 MoE or Laguna architecture
-- all repeating model layers assigned to Metal
+- macOS with Metal, or Linux with Vulkan
+- Qwen3.x MoE or Laguna architecture
+- all repeating model layers assigned to one supported GPU
 - text generation without embeddings or LoRA adapters
 
-Longhaul does not restrict the GGUF quantization type. Individual tensor types must still be supported by Metal.
+Longhaul does not restrict the GGUF quantization type. Individual tensor types
+must still be supported by the selected GPU backend.
 
-MTP/speculative decoding, tensor validation during loading, vocabulary-only loading, and CPU or mixed CPU/Metal layer placement are not supported. Both single-file and split GGUF models are supported; routed expert tensors are read from the shard that owns each tensor.
+MTP/speculative decoding, tensor validation during loading, vocabulary-only
+loading, multi-GPU placement, and CPU or mixed CPU/GPU layer placement are not
+supported. Both single-file and split GGUF models are supported; routed expert
+tensors are read from the shard that owns each tensor.
 
-The cache budget covers the compact routed-expert tensors. It does not include dense weights, attention weights, the KV cache, graph allocations, or the temporary buffer used for one expert read. Disk reads bypass the macOS unified file cache where supported, avoiding a second long-lived copy of streamed weights in system RAM.
+The cache budget covers the compact routed-expert tensors. It does not include
+dense weights, attention weights, the KV cache, graph allocations, or temporary
+staging buffers. Disk reads bypass the macOS unified file cache where supported.
+On Linux, completed streamed reads are released from the kernel page cache. Both
+avoid a second long-lived copy of streamed weights in system RAM.
 
 This implementation synchronizes at each routed MoE layer to discover the selected experts, populate missing cache slots, and continue execution with cache-local expert IDs. Storage speed and expert reuse therefore have a large effect on generation speed.
 
 Expert IDs are planned as a batch at each synchronization point. Experts already
 needed by that batch are protected from eviction, duplicate IDs are loaded only
-once, and independent expert slices are read concurrently on shared-memory Metal
-devices. Private Metal buffers use a staged fallback.
+once, and independent expert slices are read concurrently. Shared host or Metal
+buffers can be populated directly. Private Metal and Vulkan device buffers use
+staged uploads that are synchronized before routed computation continues.
+
+## Arch Linux and a 4 GiB GTX 1050 Ti
+
+Install the Vulkan build dependencies and confirm that the NVIDIA GPU is visible:
+
+```sh
+sudo pacman -S --needed base-devel cmake ninja shaderc spirv-headers \
+    vulkan-headers vulkan-icd-loader vulkan-tools
+vulkaninfo --summary
+```
+
+A GTX 10-series card needs a Vulkan driver that still supports Pascal. Driver
+installation is system-specific; do not continue until `vulkaninfo` lists the
+GTX 1050 Ti.
+
+Build Longhaul with Vulkan and list the runtime device names:
+
+```sh
+cmake -S . -B build-vulkan -G Ninja \
+    -DCMAKE_BUILD_TYPE=Release \
+    -DGGML_VULKAN=ON
+cmake --build build-vulkan --target llama-cli llama-bench test-longhaul
+./build-vulkan/bin/llama-cli --list-devices
+```
+
+Use the `VulkanN` name corresponding to the discrete NVIDIA card. This matters
+on systems that also expose an integrated Radeon GPU.
+
+For `Qwen3.6-35B-A3B-UD-IQ4_XS.gguf`, the non-expert weights use about 2.38 GiB
+and each cache slot uses about 56.5 MiB. A 0.5 GiB cache provides nine slots for
+the model's eight selected experts:
+
+```sh
+./build-vulkan/bin/llama-cli \
+    --model /path/to/Qwen3.6-35B-A3B-UD-IQ4_XS.gguf \
+    --device VulkanN \
+    --n-gpu-layers 99 \
+    --longhaul \
+    --longhaul-cache 0.5 \
+    --ctx-size 512 \
+    --no-kv-offload \
+    --prompt "Write a short hello message." \
+    --n-predict 32
+```
+
+Keep the model on the fastest available drive. A hard drive works correctly,
+but random expert reads can make generation substantially slower. If Vulkan
+runs out of memory while the desktop is active, close GPU-heavy applications
+before reducing the cache below eight slots.
 
 ## Benchmarking prompt processing
 
@@ -50,7 +110,7 @@ devices. Private Metal buffers use a staged fallback.
 llama-bench \
     --model /path/to/model.gguf \
     --load-mode longhaul \
-    --longhaul-cache 2 \
+    --longhaul-cache 0.5 \
     --n-gpu-layers 99 \
     --n-prompt 2048 \
     --n-gen 0