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| Llama working at his workstation. This image was generated locally using Stable Diffusion on a 2011 desktop with an Nvidia RTX3060 12GB GPU |
Llama 3 is an 'AI model', ie a Large Language Deep Learning model comparable to Google Gemini 3.
Sean Zheng's excellent post details a very quick way of installing and running Llama3 from a local desktop. He had good results with an Intel i9 with 128GB RAM and an Nvidia RTX 4090 with 24GB VRAM. However, my desktop dates back to 2011 and is just a 3GHz AMD Phenom II with only 16GB DRAM and an Nvidia RTX 3060 GPU with 12GB VRAM. The hope is since the RTX3060 is not too far behind his RTX 4090, Llama3 can run or maybe hobble along in some fashion.
Sean's desktop runs Red Hat's RHEL9.3 but mine runs Ubuntu 22.04LTS. Both of us had already installed Nvidia graphics drivers as well as the CUDA Toolkit. In my case the driver is 560.35.03 and CUDA is 12.6. Sean's method was to run llama3 from a Docker image. This is a excellent sandbox for a beginner like me to try out Llama3, and not risk upsetting other large AI installs like Stable Diffusion or Keras.
Sean's post is mostly complete, the instructions are replicted here for convenience. First the system updates:
$sudo apt update
$sudo apt upgrade
We then need to update the ubuntu repository for docker:
$sudo apt install apt-transport-https ca-certificates curl software-properties-common
~$curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg
$echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
Then the actual Docker install:
$sudo apt update
$apt-cache policy docker-ce
$sudo apt install docker-ce
And I have a running docker daemon:
$sudo systemctl status docker
�.. docker.service - Docker Application Container Engine
Loaded: loaded (/lib/systemd/system/docker.service; enabled; vendor preset>
Active: active (running) since Thu 2024-09-26 11:05:47 +08; 22s ago
TriggeredBy: �.. docker.socket
Docs: https://docs.docker.com
Main PID: 56585 (dockerd)
Tasks: 10
Memory: 22.2M
CPU: 729ms
CGroup: /system.slice/docker.service
�..�..56585 /usr/bin/dockerd -H fd:// --containerd=/run/containerd/
con>
A quick test seems fine:
$docker run hello-world
Unable to find image 'hello-world:latest' locally
latest: Pulling from library/hello-world
c1ec31eb5944: Pull complete
Digest: sha256:91fb4b041da273d5a3273b6d587d62d518300a6ad268b28628f74997b93171b2
Status: Downloaded newer image for hello-world:latest
Hello from Docker!
This message shows that your installation appears to be working correctly.
Next I just use Docker to pull in ollama:
$docker run -d -v ollama:/root/.ollama -p 11434:1
1434 --name ollama ollama/ollama
Unable to find image 'ollama/ollama:latest' locally
latest: Pulling from ollama/ollama
Digest: sha256:e458178cf2c114a22e1fe954dd9a92c785d1be686578a6c073a60cf259875470
Status: Downloaded newer image for ollama/ollama:latest
c09a5a60d5aa9120175c52f7b13b59420564b126005f4e90da704851bbeb9308
A quick check shows everything seems OK:
$docker ps -a
CONTAINER ID IMAGE COMMAND CREATED STATUS
PORTS NAMES
c09a5a60d5aa ollama/ollama "/bin/ollama serve" 9 minutes ago Up 9 minute
s 0.0.0.0:11434->11434/tcp, :::11434->11434/tcp ollama
75beaa5bac23 hello-world "/hello" 2 hours ago Exited (0)
2 hours ago amazing_ptolemy
OK, now for the GPU version of Ollama. We first stop ollama:
$docker stop c09a5a60d5aa
c09a5a60d5aa
$docker rm c09a5a60d5aa
c09a5a60d5aa
Make the local directory for ollama:
$mkdir ~/ollama
Oops:
$docker run -it --rm --gpus=all -v /home/heong/ollama:/root/.ollama:z -p 11434:11434 --name ollama ollama/ollama
docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].
I think that means it cannot find the GPU. From here, I think I need the Nvidia Container Toolkit. The install guide is here.
Update the repository:
$curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
$ sudo apt-get update
Now the actual install:
sudo apt-get install -y nvidia-container-toolkit
The just restart Docker:
$ sudo systemctl restart docker
Now ollama runs:
$docker run -it --rm --gpus=all -v /home/heong/ollama:/root/.ollama:z -p 11434:11434 --name ollama ollama/ollama
2024/09/26 13:12:23 routes.go:1153: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PROXY: NO_PROXY: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://0.0.0.0:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE:5m0s OLLAMA_LLM_LIBRARY: OLLAMA_LOAD_TIMEOUT:5m0s OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:/root/.ollama/models OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:0 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://*] OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES: http_proxy: https_proxy: no_proxy:]"
But then it looked like it detected my GPU but refused to use it as my CPU does not have AVX or AVX2 instructions support:
time=2024-09-26T13:12:23.496Z level=WARN source=gpu.go:224 msg="CPU does not have minimum vector extensions, GPU inference disabled" required=avx detected="no vector extensions"
time=2024-09-26T13:12:23.496Z level=INFO source=types.go:107 msg="inference compute" id=0 library=cpu variant="no vector extensions" compute="" driver=0.0 name="" total="15.6 GiB" available="13.2 GiB"
Now that was a setback, but ollama runs. Let's see if it loads llama 3.
$docker exec -it ollama ollama pull llama3
For good measure lets pull in llama2:
$docker exec -it ollama ollama pull llama3
$docker exec -it ollama ollama list
NAME ID SIZE MODIFIED
llama3:latest 365c0bd3c000 4.7 GB 15 seconds ago
llama2:latest 78e26419b446 3.8 GB 24 hours ago
And indeed llama3 runs on a 2011 AMD CPU with just 16GB RAM:
$docker exec -it ollama ollama run llama3
>>> Send a message (/? for help)
>>> /?
Available Commands:
/set Set session variables
/show Show model information
/load <model> Load a session or model
/save <model> Save your current session
/clear Clear session context
/bye Exit
/?, /help Help for a command
/? shortcuts Help for keyboard shortcuts
Use """ to begin a multi-line message.
>>> /show info
Model
architecture llama
parameters 8.0B
context length 8192
embedding length 4096
quantization Q4_0
Parameters
num_keep 24
stop "<|start_header_id|>"
stop "<|end_header_id|>"
stop "<|eot_id|>"
License
META LLAMA 3 COMMUNITY LICENSE AGREEMENT
Meta Llama 3 Version Release Date: April 18, 2024
In response to the prompt
>>> How are you today?
The reply was:
I'm just an AI, I don't have feelings or emotions like humans do. However,
I am functioning properly and ready to assist with any questions or tasks
you may have! Is there something specific you'd like to talk about or ask
for help with?
It was excruciatingly slow, and nvtop show the gpu is indeed not used but ollama seems to be all there. So there you have it, Llama3 running on a 16GB AMD Phenom II with no GPU.
Happy Trails.
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