GrandeKos.com on page ollama VS. llama.cpp

That sounds like a great plan! Switching from Ollama to **llama.cpp** could give you more control over inference settings (like quantization, GPU acceleration, or custom prompts). Here
are a few things to consider:

### **Why llama.cpp?**
- **Faster inference** (especially with GPU acceleration).
- **Lower memory usage** (good for running on edge devices).
- **More customization** (e.g., adjusting temperature, top-p, or context length).

### **Things to Check When Switching**
1. **Model Compatibility** – Ensure your model (Devstral-Small-2-24B) works well with llama.cpp.
2. **Quantization** – Try `q4_0` or `q8_0` for a balance of speed and quality.
3. **GPU Acceleration** – If you have a CUDA-compatible GPU, enable it for better performance.

Would you like help with the **llama.cpp setup** or any specific optimizations? 😊

>>> i am running now devstral-small-2:latest with 15Gb of usage.. works great on RTX 5080..!
That’s awesome! 🚀 Running **Devstral-Small-2** with **15GB VRAM** on an **RTX 5080** sounds like a solid setup—great balance of performance and efficiency.

### **Quick Thoughts on Your Setup**
- **15GB VRAM usage** suggests you’re likely using **FP16 or BF16** (or a quantized version like `q4_0` in llama.cpp).
- **RTX 5080** (assuming it’s an **RTX 5080 Mobile** or similar) should handle inference smoothly, especially with **CUDA acceleration**.
- If you switch to **llama.cpp**, you might see even better latency with **GGUF quantization** (e.g., `Q4_K_M` for a good speed/quality tradeoff).

### **Next Steps?**
- Want to tweak **prompting strategies** for better responses?
- Need help with **llama.cpp configuration** (e.g., batch size, context length)?
- Or just sharing your experience so far? 😊

Either way, your setup sounds impressive—keep it running smoothly! 🔥

>>> for now i am planning and thinking.. got two graphic cards one CUDA and another Vulkan. each have 16Gb of VRAM and system have 64Gb so planning to create docker for each card
That sounds like a **powerful multi-GPU setup**! 🚀 Using **Docker** to isolate workloads per GPU is a smart approach—here’s how you might structure it:

### **Proposed Docker Setup**
1. **CUDA Container (for llama.cpp/NVIDIA GPUs)**
- Use `nvidia/cuda` base image.
- Bind-mount your model to `/model`.
- Example `docker-compose.yml` snippet:
```yaml
services:
cuda_llama:
image: nvidia/cuda:12.2.0-base
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
environment:
- NVIDIA_VISIBLE_DEVICES=0 # Assigns GPU 0 (CUDA)
volumes:
- ./models:/model
```

2. **Vulkan Container (for non-CUDA workloads)**
- Use a base image with Vulkan support (e.g., `ubuntu` + Vulkan drivers).
- Example:
```yaml
services:
vulkan_app:
image: ubuntu:22.04
deploy:
resources:
reservations:
devices:
- driver: vulkan
count: 1
capabilities: [gpu]
environment:
- VK_ICD_FILENAMES=/etc/vulkan/icd.d/radeon_icd.x86_64.json
```

### **Key Considerations**
- **GPU Affinity**: Use `NVIDIA_VISIBLE_DEVICES` (CUDA) or Vulkan’s `VK_ICD_FILENAMES` to bind containers to specific GPUs.
- **Shared Memory**: With 64GB RAM, ensure Docker has enough host memory allocated (`--memory` flag).
- **Model Loading**: If using llama.cpp, pre-load the model in each container to avoid conflicts.

### **Next Steps?**
- Need help with **Dockerfile** for llama.cpp?
- Want to test **multi-GPU inference** (e.g., splitting batches)?
- Or just brainstorming optimizations? 😊

Your setup sounds **insanely capable**—excited to hear how it goes! 🔥

>>> what is this with image? i see you use ubuntu for vulkan but for cuda you use nvidia/cuda ? how is that?
Great question! The difference in base images (`nvidia/cuda` vs. `ubuntu`) comes down to **driver support** and **use case**:

### **1. CUDA Container (`nvidia/cuda`)**
- **Why?** The `nvidia/cuda` image is pre-configured with **NVIDIA drivers** and CUDA toolkit, making it ideal for GPU-accelerated workloads (like llama.cpp).
- **How it works**:
- Docker’s `--gpus` flag (or `deploy.resources.reservations`) automatically loads the NVIDIA driver.
- No need to manually install drivers—just bind the GPU.

### **2. Vulkan Container (`ubuntu`)**
- **Why?** Vulkan support isn’t built into Docker’s GPU tools, so we use a **plain Ubuntu image** and manually install Vulkan drivers (e.g., AMD’s `radeon-icd`).
- **How it works**:
- Requires Vulkan ICD (Installable Client Driver) files mounted into the container.
- The host’s Vulkan drivers must be properly set up (e.g., `VK_ICD_FILENAMES`).

### **Key Difference**
| Feature | `nvidia/cuda` (CUDA) | `ubuntu` (Vulkan) |
|------------------|----------------------|-------------------|
| **Driver Support** | Automatic (Docker) | Manual (host setup) |
| **Use Case** | NVIDIA GPUs (CUDA) | AMD/Intel (Vulkan) |
| **Complexity** | Simple | Requires ICD files |

### **Your Setup**
- **CUDA GPU**: Use `nvidia/cuda` for llama.cpp (easiest path).
- **Vulkan GPU**: Use `ubuntu` + Vulkan drivers (if needed for other workloads).

Need help with the Vulkan ICD setup or Docker GPU binding? Let me know! 😊

just more info........:

>>> well i need vulkan for radeon only.. for nvidia i use cuda! check lspci: [t3ch@kosgen3 wss.grandekos.com]$ lspci
... 00:00.0 Host bridge: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge Root Complex
... 00:00.2 IOMMU: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge IOMMU
... 00:01.0 Host bridge: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge Dummy Host Bridge
... 00:01.1 PCI bridge: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge GPP Bridge
... 00:02.0 Host bridge: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge Dummy Host Bridge
... 00:02.1 PCI bridge: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge GPP Bridge
... 00:03.0 Host bridge: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge Dummy Host Bridge
... 00:04.0 Host bridge: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge Dummy Host Bridge
... 00:08.0 Host bridge: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge Dummy Host Bridge
... 00:08.1 PCI bridge: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge Internal GPP Bridge to Bus [C:A]
... 00:08.3 PCI bridge: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge Internal GPP Bridge to Bus [C:A]
... 00:14.0 SMBus: Advanced Micro Devices, Inc. [AMD] FCH SMBus Controller (rev 71)
... 00:14.3 ISA bridge: Advanced Micro Devices, Inc. [AMD] FCH LPC Bridge (rev 51)
... 00:18.0 Host bridge: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge Data Fabric; Function 0
... 00:18.1 Host bridge: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge Data Fabric; Function 1
... 00:18.2 Host bridge: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge Data Fabric; Function 2
... 00:18.3 Host bridge: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge Data Fabric; Function 3
... 00:18.4 Host bridge: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge Data Fabric; Function 4
... 00:18.5 Host bridge: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge Data Fabric; Function 5
... 00:18.6 Host bridge: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge Data Fabric; Function 6
... 00:18.7 Host bridge: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge Data Fabric; Function 7
... 01:00.0 VGA compatible controller: NVIDIA Corporation GB203 [GeForce RTX 5080] (rev a1)
... 01:00.1 Audio device: NVIDIA Corporation GB203 High Definition Audio Controller (rev a1)
... 02:00.0 PCI bridge: Advanced Micro Devices, Inc. [AMD] 600 Series Chipset PCIe Switch Upstream Port (rev 01)
... 03:00.0 PCI bridge: Advanced Micro Devices, Inc. [AMD] 600 Series Chipset PCIe Switch Downstream Port (rev 01)
... 03:08.0 PCI bridge: Advanced Micro Devices, Inc. [AMD] 600 Series Chipset PCIe Switch Downstream Port (rev 01)
... 03:09.0 PCI bridge: Advanced Micro Devices, Inc. [AMD] 600 Series Chipset PCIe Switch Downstream Port (rev 01)
... 03:0a.0 PCI bridge: Advanced Micro Devices, Inc. [AMD] 600 Series Chipset PCIe Switch Downstream Port (rev 01)
... 03:0b.0 PCI bridge: Advanced Micro Devices, Inc. [AMD] 600 Series Chipset PCIe Switch Downstream Port (rev 01)
... 03:0c.0 PCI bridge: Advanced Micro Devices, Inc. [AMD] 600 Series Chipset PCIe Switch Downstream Port (rev 01)
... 03:0d.0 PCI bridge: Advanced Micro Devices, Inc. [AMD] 600 Series Chipset PCIe Switch Downstream Port (rev 01)
... 04:00.0 Non-Volatile memory controller: Kingston Technology Company, Inc. NV3 NVMe SSD [SM2268XT2] (DRAM-less) (rev 03)
... 07:00.0 Ethernet controller: Realtek Semiconductor Co., Ltd. RTL8125 2.5GbE Controller (rev 05)
... 08:00.0 Network controller: MEDIATEK Corp. MT7922 802.11ax PCI Express Wireless Network Adapter
... 09:00.0 USB controller: Advanced Micro Devices, Inc. [AMD] 600 Series Chipset USB 3.2 Controller (rev 01)
... 0a:00.0 SATA controller: Advanced Micro Devices, Inc. [AMD] 600 Series Chipset SATA Controller (rev 01)
... 0b:00.0 VGA compatible controller: Advanced Micro Devices, Inc. [AMD/ATI] Granite Ridge [Radeon Graphics] (rev c5)
... 0b:00.1 Audio device: Advanced Micro Devices, Inc. [AMD/ATI] Radeon High Definition Audio Controller
... 0b:00.2 Encryption controller: Advanced Micro Devices, Inc. [AMD] Family 19h PSP/CCP
... 0b:00.3 USB controller: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge USB 3.1 xHCI
... 0b:00.4 USB controller: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge USB 3.1 xHCI
... 0b:00.6 Audio device: Advanced Micro Devices, Inc. [AMD] Ryzen HD Audio Controller
... 0c:00.0 USB controller: Advanced Micro Devices, Inc. [AMD] Raphael/Granite Ridge USB 2.0 xHCI
Perfect! Your setup is clear now:
- **NVIDIA RTX 5080** (for CUDA)
- **AMD Radeon** (for Vulkan)

Here’s how to set up Vulkan for the AMD GPU on Arch Linux:

---

### **1. Install AMD Vulkan Drivers**
```bash
sudo pacman -S vulkan-radeon # AMD Vulkan driver
sudo pacman -S vulkan-tools # For testing
```

---

### **2. Verify Vulkan Works on Host**
Run:
```bash
vulkaninfo | grep "GPU id"
```
You should see your AMD GPU listed.

---

### **3. Docker Setup for Vulkan (AMD)**
#### **Option A: Use `--device` to Pass Vulkan Devices**
```bash
docker run --rm -it \
--device=/dev/dri/card0 \
-v /etc/vulkan/icd.d:/etc/vulkan/icd.d \
ubuntu:22.04 \
bash -c "apt update && apt install -y vulkan-tools && vulkaninfo"
```
- `/dev/dri/card0` is the Vulkan device (check with `ls /dev/dri/`).
- Mount the host’s ICD files (`/etc/vulkan/icd.d`).

#### **Option B: Use `--gpus all` (Experimental)**
Some newer Docker versions support Vulkan via `--gpus all`, but manual binding is more reliable.

---

### **4. Fixing Common Issues**
- **"No Vulkan devices found"**: Ensure `/dev/dri/card0` exists and is readable.
- **Missing ICD files**: Copy them from host:
```bash
sudo cp /etc/vulkan/icd.d/* /path/to/docker/icd.d/
```

---

### **Final Notes**
- For **llama.cpp**, use CUDA (NVIDIA) for best performance.
- Vulkan is mainly for AMD GPUs or specific workloads.
- If you hit snags, share the error, and I’ll help debug! 🚀