Tenstorrent Developer Extension - FAQ

Frequently Asked Questions - Your quick reference for common questions, troubleshooting, and tips from all 48 lessons.

Table of Contents

Getting Started

Q: Which lesson should I start with?

A: Start with Hardware Detection if you're brand new. The 48 lessons are organized into 9 categories:

🚀 Your First Inference (5 lessons)

  1. Hardware Detection → Verify Installation → Download Model → Interactive Chat → API Server

🏭 Serving Models (4 lessons) Production servers (tt-inference-server, vLLM) and generation (Image, Video)

🎓 Custom Training (8 lessons) ⭐ NEW! Fine-tune models or train from scratch - validated on hardware with both workflows working!

🎯 Applications (2 lessons) Coding Assistant, AnimateDiff Video Generation

👨‍🍳 Tenstorrent Cookbook (6 lessons) Game of Life, Audio, Mandelbrot, Image Filters, Particle Life + Overview

🔧 Compilers & Tools (2 lessons) TT-Forge, TT-XLA

🧠 CS Fundamentals (7 lessons) Computer Architecture, Memory, Parallelism, Networks, Synchronization, Abstraction, Complexity

🎓 Advanced Topics (5 lessons) tt-installer, Bounty Program, Explore Metalium, Koyeb Deployment (2)

Can I skip lessons? Yes! Categories are independent - jump to what interests you.

Q: Do I need to complete lessons in order?

A: Not strictly, but:

Quick start for experienced users:

  1. Run Hardware Detection (2 minutes - verify hardware)
  2. Skip to Production Inference with vLLM (production serving)
  3. Explore advanced topics (compilers, RISC-V, bounty program)

Q: What's the difference between the different tools?

A: Tenstorrent has several tools serving different purposes:

Tool Purpose When to Use Maturity
tt-metal Low-level framework Custom kernels, maximum control Stable
vLLM LLM serving Production LLM deployment Production
TT-Forge MLIR compiler PyTorch models (experimental) Beta
TT-XLA XLA compiler JAX/PyTorch (production) Production

Simple guide:

Remote Development & SSH

Q: Can I use this extension from my Mac/Windows laptop to access remote Tenstorrent hardware?

A: Yes! Use VSCode's Remote-SSH extension - the industry-standard solution for remote development.

This is the recommended approach for:

Why Remote-SSH is perfect for this:

Q: How do I set up Remote-SSH for Tenstorrent development?

A: Quick setup guide:

Step 1: Install Remote-SSH extension

  1. Open VSCode on your local machine (Mac/Windows)
  2. Open Extensions panel (Cmd+Shift+X or Ctrl+Shift+X)
  3. Search for "Remote - SSH"
  4. Install the official Microsoft extension

Step 2: Configure SSH connection

Add your Tenstorrent machine to SSH config:

# On your local machine, edit ~/.ssh/config
# (Cmd+Shift+P → "Remote-SSH: Open Configuration File")

Host tenstorrent-dev
  HostName 192.168.1.100        # Your hardware machine IP
  User ubuntu                   # Your username
  IdentityFile ~/.ssh/id_rsa    # Your SSH key
  ForwardAgent yes              # Optional: Forward SSH agent

Step 3: Connect to remote machine

  1. Cmd+Shift+P (or Ctrl+Shift+P) → "Remote-SSH: Connect to Host"
  2. Select "tenstorrent-dev"
  3. New VSCode window opens connected to remote machine

Step 4: Install Tenstorrent extension on remote

  1. In the remote VSCode window, go to Extensions
  2. Search for "Tenstorrent Developer Extension"
  3. Click "Install in SSH: tenstorrent-dev"

Step 5: Start using lessons!

Q: Do the lessons work through Remote-SSH?

A: Yes, perfectly! Remote-SSH makes everything transparent:

What works automatically:

Example workflow:

  1. Connect via Remote-SSH from your Mac
  2. Open Tenstorrent walkthrough (works like local)
  3. Run Hardware Detection → tt-smi runs on remote
  4. Download model → Saves to remote ~/models/
  5. Start vLLM server → Runs on remote, port auto-forwarded
  6. Test from local browser → http://localhost:8000 works!

No code changes needed - The extension doesn't know or care that you're remote!

Q: What about SSH without Remote-SSH extension?

A: Not recommended. Manual SSH has major problems:

❌ File operations break - Extension reads/writes local filesystem, not remote ❌ Path mismatches - ~/models/ on Mac ≠ ~/models/ on remote ❌ Complex escaping - Terminal commands get mangled through SSH ❌ No port forwarding - Can't access servers on localhost ❌ Poor UX - Feels disconnected, hard to debug

Example of problems:

If you manually SSH in terminal:

# This command in lesson creates file on your MAC, not remote!
cat > ~/tt-scratchpad/script.py << 'EOF'
...
EOF

Then this fails because the file is on the wrong machine:

ssh user@remote python3 ~/tt-scratchpad/script.py

With Remote-SSH: Both operations happen on remote automatically.

Q: Can multiple people share the same remote hardware?

A: Yes, but with considerations:

Shared hardware works best with:

Limitations:

Best practice for teams:

# User 1
vllm ... --port 8001

# User 2
vllm ... --port 8002

# Each user accesses their own server
curl http://localhost:8001/...  # User 1
curl http://localhost:8002/...  # User 2

Q: What about Tenstorrent Cloud? Does Remote-SSH work?

A: Yes! Tenstorrent Cloud instances are perfect for Remote-SSH:

Typical setup:

  1. Get Tenstorrent Cloud instance (pre-configured with hardware)
  2. Receive SSH credentials
  3. Add to ~/.ssh/config on your laptop
  4. Connect via Remote-SSH
  5. Start developing!

Cloud benefits:

Example cloud SSH config:

Host tt-cloud
  HostName cloud.instance.tenstorrent.com
  User your-username
  IdentityFile ~/.ssh/tt-cloud-key
  ForwardAgent yes

Q: Are there performance considerations with Remote-SSH?

A: Remote-SSH is very efficient:

Fast operations (no noticeable latency):

What uses bandwidth:

Best practices:

Real-world experience:

Q: How do I disconnect from remote machine?

A: Several options:

Graceful disconnect:

From command palette:

Important: vLLM servers keep running after disconnect!

# Before disconnecting, you may want to:
docker ps                    # Note container IDs
docker stop <container-id>   # Stop servers

# Or leave them running and reconnect later

Reconnecting:

Hardware & Detection

Q: Can I try Tenstorrent development without hardware?

A: Yes! Use ttsim - Tenstorrent's full-system simulator.

What is ttsim:

Quick Start:

# Download simulator (replace vX.Y with latest version)
mkdir -p ~/sim
cd ~/sim
wget https://github.com/tenstorrent/ttsim/releases/latest/download/libttsim_wh.so

# Copy SOC descriptor
cp $TT_METAL_HOME/tt_metal/soc_descriptors/wormhole_b0_80_arch.yaml ~/sim/soc_descriptor.yaml

# Set environment variable
export TT_METAL_SIMULATOR=~/sim/libttsim_wh.so

# Run in slow dispatch mode (required for simulator)
export TT_METAL_SLOW_DISPATCH_MODE=1

# Test it works
cd $TT_METAL_HOME
./build/programming_examples/metal_example_add_2_integers_in_riscv

What you CAN do with ttsim:

What you CAN'T do (too slow):

Which lessons work with ttsim:

Resources:

Tip: Use ttsim for learning and kernel development, then move to real hardware for model inference and production workloads.

Q: Which hardware do I have?

A: Run this command:

tt-smi -s | grep -o '"board_type": "[^"]*"'

Output tells you:

Q: tt-smi says "No devices found" - what do I do?

A: Try these steps in order:

  1. Check PCIe detection:

    lspci | grep -i tenstorrent

    Should show: Processing accelerators: Tenstorrent Inc.

  2. Try with sudo:

    sudo tt-smi

    If this works, you have a permissions issue.

  3. Reset the device:

    tt-smi -r

  4. Full cleanup (if still failing):

    sudo pkill -9 -f tt-metal
sudo pkill -9 -f vllm
sudo rm -rf /dev/shm/tenstorrent* /dev/shm/tt_*
tt-smi -r

Still not working? Check the Hardware Detection lesson troubleshooting section for detailed steps.

Q: What's the difference between Wormhole and Blackhole?

A:

For production: Stick with Wormhole (N150/N300/T3K) - more models validated.

For experimentation: Blackhole offers newer features but check model compatibility.

Q: How do I know what my hardware can run?

A: Quick reference:

Hardware Max Model Size Max Context Multi-chip Best For
N150, P100 8B 64K No (TP=1) Development, prototyping
N300, P150 13B 128K Yes (TP=2) Medium models, multi-user
T3K 70B+ 128K Yes (TP=8) Large models, production

Q: What happens to running jobs and hardware utilization when a system suspends?

A: When the system goes into suspend, all running jobs on Tenstorrent hardware are interrupted and effectively terminated, and hardware utilization drops to zero. On resume, the driver re-initializes the device (similar to a reset), so any workloads must be restarted. In normal cases you don't need a full reboot; if the device doesn't come back cleanly, run tt-smi -r (reset) or reboot the host.

Q: After a Linux kernel update, my Tenstorrent device is not detected or tt-inference-server reports a pre-release driver version.

A: The kernel module driver (tt-kmd) must be compiled specifically for each kernel version. This normally happens automatically via DKMS when a new kernel is installed, but can silently fail if there are orphaned DKMS entries left behind from old driver versions.

Quick fix:

sudo dkms install tenstorrent/$(dkms status tenstorrent | grep -o '[0-9]\+\.[0-9]\+\.[0-9]\+' | sort -V | tail -1) -k $(uname -r)
sudo modprobe tenstorrent
modinfo tenstorrent | grep version   # Confirm correct version loaded

If dkms install fails with "Could not locate dkms.conf", you have orphaned entries from old driver versions that are blocking the auto-build. Clean them up:

# Identify broken entries (any version that prints an error instead of a status line)
dkms status

# Manually remove each broken version
sudo rm -rf /var/lib/dkms/tenstorrent/<broken-version>

# Then retry the install above

Why this happens: When old tt-kmd versions are superseded, their DKMS source directories are sometimes removed without de-registering them from the DKMS registry. These orphaned entries cause dkms autoinstall to abort with an error before it reaches the valid driver version, so the new kernel boots without the module.

After cleanup, kernel upgrades will rebuild the module automatically — no manual intervention needed on future upgrades.

Installation & Setup

Q: How do I verify tt-metal is working?

A: Run this quick test:

python3 -c "import ttnn; print('✓ tt-metal ready')"

If it fails:

Q: Which Python version do I need?

A:

Check your version:

python3 --version

Q: Where should models be installed?

A: Standard locations:

Both formats needed for some lessons:

Q: How much disk space do I need?

A: Plan for:

Minimum for this extension: 100GB free space

Models & Downloads

Q: Which model should I download first?

A: Llama-3.1-8B-Instruct - covered in Download Model.

Why this model:

Download command:

hf download meta-llama/Llama-3.1-8B-Instruct \
  --local-dir ~/models/Llama-3.1-8B-Instruct

Q: How do I handle HuggingFace authentication?

A: Three options:

Option 1: Environment variable (recommended for scripts)

export HF_TOKEN=your_token_from_huggingface
hf download meta-llama/Llama-3.1-8B-Instruct --local-dir ~/models/Llama-3.1-8B-Instruct

Option 2: Interactive login (recommended for manual use)

hf auth login
# Paste your token when prompted

Option 3: In code

from huggingface_hub import login
login(token="your_token_from_huggingface")

Get a token: https://huggingface.co/settings/tokens

Q: Download failed with "repository not found" - why?

A: Gated models require access request:

  1. Go to model page on HuggingFace
  2. Click "Request access" button
  3. Wait for approval (usually instant for Llama)
  4. Ensure you're authenticated (see question above)

For Llama models: Must accept Meta's license agreement.

Q: Can I use models from other sources?

A: Yes, but:

Recommendation: Stick with HuggingFace - most compatible.

Inference & Serving

Q: Which inference method should I use?

A: Depends on your goal:

Method Lesson Best For Speed (after load)
One-shot demo Download Model Testing, verification 2-5 min per query
Interactive chat Interactive Chat Learning, prototyping 1-3 sec per query
Flask API API Server Simple custom APIs 1-3 sec per query
vLLM Production Inference Production serving 1-3 sec per query

Quick guide:

Q: Why does first load take 2-5 minutes?

A: Model initialization involves:

  1. Loading weights from disk (~16GB for Llama-8B)
  2. Converting to TT-Metal format
  3. Distributing to hardware cores
  4. JIT compilation of kernels

This is normal and only happens once.

Subsequent queries are fast (1-3 seconds) because model stays in memory.

Q: Can I run multiple models simultaneously?

A: On same hardware: No (one model at a time per device)

Workarounds:

Q: What does "context length" mean and why does it matter?

A:

Hardware limits:

Exceeding context?

RuntimeError: Input sequence length exceeds maximum

Solutions:

Q: Getting PyTorch dataclass errors with vLLM - how do I fix them?

A: This error (TypeError: must be called with a dataclass type or instance) is caused by PyTorch version mismatches.

Error looks like:

TypeError: must be called with a dataclass type or instance
# ... torch/_inductor/runtime/hints.py errors

Root cause: vLLM on Tenstorrent hardware requires PyTorch 2.5.0+cpu specifically. Other versions (2.4.x, 2.7.x) cause compatibility issues.

Solution: Recreate your vLLM environment

bash ~/tt-scratchpad/setup-vllm-env.sh

This automated script:

Verify your environment:

source ~/activate-vllm-env.sh
python3 -c "import torch; print('PyTorch version:', torch.__version__)"
# Should print: PyTorch version: 2.5.0+cpu

Why the specific version? TT-Metal hardware drivers are built against PyTorch 2.5.0+cpu APIs. Other versions have incompatible dataclass implementations.

Custom Training

Q: Can I train models on Tenstorrent hardware?

A: Yes! The extension now includes 8 complete Custom Training lessons (CT1-CT8) that are fully validated on hardware.

What's working:

Recommended version: tt-metal v0.66.0-rc7 (fully tested)

Q: What hardware do I need for training?

A: Training requirements depend on model size:

N150 (Wormhole single-chip):

N300+ (Wormhole dual-chip or higher):

Recommendation: Start with N150 and NanoGPT to learn the workflow!

Q: What's the difference between fine-tuning and training from scratch?

A:

Fine-tuning (CT4):

Training from Scratch (CT8):

Which should I start with? CT8 (from-scratch) - it's faster on N150 with NanoGPT and teaches fundamentals!

Q: What tt-metal version do I need for training?

A: Training requires v0.66.0-rc5 or later

Why:

Check your version:

cd $TT_METAL_HOME && git describe --tags

See CT4 and CT8 lessons for complete setup instructions!

Compilers & Tools

Q: What's the difference between TT-Forge and TT-XLA?

A:

Feature TT-Forge TT-XLA
Status Experimental Production-ready
Multi-chip Single only Yes (TP/DP)
Frameworks PyTorch, ONNX JAX, PyTorch/XLA
Model support Limited (169 validated) Broader
Installation Complex (build from source) Simple (pip)

When to use TT-Forge:

When to use TT-XLA:

Q: Why did my model fail to compile in TT-Forge?

A: TT-Forge is experimental. Common reasons:

  1. Unsupported operators

    • Not all PyTorch ops implemented
    • Check tt-forge-models for validated examples

  2. Model architecture

    • Very new architectures may not work
    • Dynamic shapes not supported
    • Control flow limited

  3. Environment variable pollution (most common!)

    unset TT_METAL_HOME
unset TT_METAL_VERSION
# Then try again

Recommendation: Start with MobileNetV2 (Image Classification with TT-Forge default) - known to work.

Q: How do I know if my model is supported?

A:

For TT-Forge:

For vLLM:

For TT-XLA:

Troubleshooting

Q: Command failed with "ImportError: undefined symbol"

A: This is almost always environment variable pollution.

Fix:

unset TT_METAL_HOME
unset TT_METAL_VERSION
# Retry your command

Make permanent: Add to ~/.bashrc:

# Prevent TT-Metal environment pollution
unset TT_METAL_HOME
unset TT_METAL_VERSION

Why this happens: Different versions of libraries loaded due to environment variables overriding build paths.

Q: vLLM server won't start - what do I check?

A: Systematic debugging:

1. Check environment variables:

echo $TT_METAL_HOME    # Should be ~/tt-metal
echo $MESH_DEVICE      # Should match your hardware (N150, etc.)
echo $PYTHONPATH       # Should include $TT_METAL_HOME

2. Verify model path:

ls ~/models/Llama-3.1-8B-Instruct/config.json

3. Check for other processes:

ps aux | grep -E "tt-metal|vllm"
# Kill if needed:
# pkill -9 -f vllm

4. Verify vLLM installation:

source ~/tt-vllm-venv/bin/activate
python3 -c "import vllm; print(vllm.__version__)"

5. Check device availability:

tt-smi
# Should show your device

Q: "Out of memory" errors - what can I do?

A: Several strategies:

1. Reduce context length:

# Instead of:
--max-model-len 65536

# Try:
--max-model-len 32768

2. Reduce batch size:

# Instead of:
--max-num-seqs 32

# Try:
--max-num-seqs 16

3. Use smaller model:

4. Clear device state:

sudo pkill -9 -f tt-metal
sudo rm -rf /dev/shm/tenstorrent* /dev/shm/tt_*
tt-smi -r

Q: Build failed - where do I look?

A:

tt-metal build issues:

cd ~/tt-metal
./build_metal.sh 2>&1 | tee build.log
# Check build.log for errors

Common build failures:

TT-Forge build issues:

Q: TTNN import errors or symbol undefined errors in cloud environments - how do I fix them?

A: After rolling back or updating tt-metal versions, TTNN bindings may become incompatible.

Symptoms:

Common Cause: Rolling back or updating tt-metal versions (for example, to match specific vLLM compatibility) can break TTNN bindings.

Solution - Clean Rebuild to Known-Good Version:

  1. Note your original working commit:

    cd ~/tt-metal
git log --oneline | head -5
# Save the commit hash that was working

  2. Checkout the known-good version:

    cd ~/tt-metal
git checkout 5143b856eb  # Replace with your working commit
git submodule update --init --recursive

  3. Complete clean rebuild:

    cd ~/tt-metal
# Clean all build artifacts
rm -rf build build_Release

# Reinstall dependencies
sudo ./install_dependencies.sh

# Rebuild from scratch
./build_metal.sh

  4. Test TTNN:

    source ~/tt-metal/python_env/bin/activate
export LD_LIBRARY_PATH=/opt/openmpi-v5.0.7-ulfm/lib:$LD_LIBRARY_PATH
export PYTHONPATH=~/tt-metal:$PYTHONPATH
python3 -m ttnn.examples.usage.run_op_on_device

Important Notes:

Known-Good Commit (as of Dec 2024):

Q: Getting OpenMPI errors - how do I fix them?

A: OpenMPI library path errors are common and easy to fix.

Symptoms:

Fix:

export LD_LIBRARY_PATH=/opt/openmpi-v5.0.7-ulfm/lib:$LD_LIBRARY_PATH

Make permanent: Add to ~/.bashrc:

# OpenMPI library path for Tenstorrent
export LD_LIBRARY_PATH=/opt/openmpi-v5.0.7-ulfm/lib:$LD_LIBRARY_PATH

Then reload:

source ~/.bashrc

Why this happens: The OpenMPI library installation isn't in the system's default library search path, so you need to explicitly tell the dynamic linker where to find it.

Alternative OpenMPI paths: If the above doesn't work, try:

# Find your OpenMPI installation
find /opt -name "libmpi.so*" 2>/dev/null

# Use the directory containing the .so files
export LD_LIBRARY_PATH=/path/to/openmpi/lib:$LD_LIBRARY_PATH

Q: Downloads are slow or failing

A:

Slow downloads:

Failing downloads:

  1. Check internet connection
  2. Verify HF authentication (see authentication question above)
  3. Check disk space: df -h ~
  4. Try resuming:
    hf download meta-llama/Llama-3.1-8B-Instruct \
  --local-dir ~/models/Llama-3.1-8B-Instruct \
  --resume-download

Performance & Optimization

Q: How can I speed up inference?

A:

After first load (model in memory):

For batch processing:

For lower latency:

Q: What are good vLLM server parameters?

A: Recommended by hardware:

N150 (single chip):

--max-model-len 65536   # Full 64K context
--max-num-seqs 16       # Moderate batching
--block-size 64         # Standard

N300 (dual chip):

--max-model-len 131072  # Full 128K context
--max-num-seqs 32       # Higher batching
--block-size 64
--tensor-parallel-size 2  # Use both chips

T3K (8 chips):

--max-model-len 131072
--max-num-seqs 64       # High batching
--block-size 64
--tensor-parallel-size 8  # Use all chips

Conservative (if OOM errors):

Q: How do I monitor performance?

A:

Token generation speed:

# In vLLM output, look for:
"Generated 150 tokens in 2.5 seconds (60 tokens/sec)"

Server metrics:

# vLLM exposes Prometheus metrics:
curl http://localhost:8000/metrics

System monitoring:

# GPU-like monitoring for TT:
watch -n 1 tt-smi

Load testing:

# Install hey:
go install github.com/rakyll/hey@latest

# Test throughput:
hey -n 100 -c 10 -m POST \
  -H "Content-Type: application/json" \
  -d '{"model": "...", "messages": [...]}' \
  http://localhost:8000/v1/chat/completions

Q: How can I visualize my hardware usage?

A: Four layers of observability, from quick to deep:

1. tt-smi — live telemetry

tt-smi          # interactive TUI with chip temp, DRAM usage, firmware version
tt-smi -s       # structured JSON snapshot (scriptable)
watch -n 1 tt-smi -s | python3 -c "import sys,json; d=json.load(sys.stdin); print(d)"

2. tt-toplike — htop-style real-time view

An htop-inspired Rust TUI that shows per-chip utilization, process list, temperature, and power draw in real time. Install via pip install tt-toplike or the system package.

tt-toplike

3. ttnn-visualizer — model execution analysis

A web-based tool that loads a tt-metal performance trace and renders interactive graphs: operation timelines, memory usage over time, tensor shapes, buffer allocation maps, and the full operation flow graph. Run after a profiled inference pass to understand exactly where time is spent.

4. tensix-viz — chip topology education

An interactive JavaScript canvas visualizer showing the actual Tensix grid layout — which cores are compute vs DRAM vs ETH — and animating what different workload types look like. Useful for building the mental model before you profile.

⬡ Tensix Grid Visualizer Blackhole (P100/P150/P300c)

Community & Support

Q: Where can I get help?

A:

Official channels:

When asking for help, include:

  1. Hardware type (N150/N300/T3K/P100)
  2. Error message (full text)
  3. Command you ran
  4. Output of tt-smi
  5. Which lesson you're on

Q: How do I report a bug?

A:

Before reporting:

  1. Search existing issues on GitHub
  2. Verify hardware works (tt-smi)
  3. Try reset (tt-smi -r)
  4. Check you're on latest tt-metal/vLLM

When reporting, include:

Hardware: N150
OS: Ubuntu 22.04
tt-metal version: [git rev-parse HEAD output]
vLLM version: [pip show vllm]
Error: [paste full error]
Steps to reproduce: [numbered list]

Good issue = faster fix!

Q: Can I contribute?

A: Yes! Several ways:

1. Bounty Program

2. Documentation

3. Code contributions

Start here:

Q: Is this production-ready?

A: Depends on component:

Production-ready (✅):

Experimental (⚠️):

Recommendation:

Quick Reference

Essential Commands

# Hardware
tt-smi                                    # Check hardware
tt-smi -s                                # Structured output
tt-smi -r                                # Reset device

# Model info
ls ~/models/                            # List installed models
du -sh ~/models/*                       # Check model sizes

# Environment
python3 -c "import ttnn; print('✓')"   # Test tt-metal
hf --version                            # Check HF CLI

# vLLM
source ~/tt-vllm-venv/bin/activate      # Activate venv
curl http://localhost:8000/health       # Check server
curl http://localhost:8000/metrics      # Get metrics

# Cleanup
sudo pkill -9 -f "tt-metal|vllm"       # Kill processes
sudo rm -rf /dev/shm/tt_*              # Clear shared memory
tt-smi -r                               # Reset hardware

Quick Diagnostic

Run this to check your setup:

#!/bin/bash
echo "=== Tenstorrent Diagnostic ==="
echo ""
echo "Hardware:"
tt-smi -s 2>&1 | grep -o '"board_type": "[^"]*"' || echo "❌ No hardware detected"
echo ""
echo "tt-metal:"
python3 -c "import ttnn; print('✓ Working')" 2>&1 || echo "❌ Not working"
echo ""
echo "Models:"
ls ~/models/ 2>/dev/null | head -3 || echo "❌ No models found"
echo ""
echo "Disk space:"
df -h ~ | grep -v Filesystem
echo ""
echo "Python:"
python3 --version

Advanced Learning Resources

Q: Where can I learn about low-level RISC-V programming on Tenstorrent hardware?

A: Check out the CS Fundamentals series - Module 1 covers RISC-V & Computer Architecture!

Each Tensix core contains five RISC-V processors (RV32IM ISA):

With 176 Tensix cores on Wormhole, that's 880 RISC-V cores you can program directly!

What Module 1 includes:

Topics covered across 7 CS Fundamentals modules:

Access the series:

View the full guide:

Perfect for:

Still Have Questions?

Check:

  1. Specific lesson troubleshooting sections
  2. CLAUDE.md for detailed technical info
  3. Discord #help channel

Remember: Most issues are:

When in doubt:

tt-smi -r
sudo rm -rf /dev/shm/tt_*
# Then retry

Last updated: May 2026 Extension version: 0.0.438

Found an error in this FAQ? Please report it on GitHub or Discord!