pearl-ai/Llama-3.3-70B-Instruct-pearl

Pearl-certified variant of Llama-3.3-70B-Instruct, intended to run with the Pearl vLLM mining plugin.

• Project website: https://pearlresearch.ai
• Pearl repository: https://github.com/pearl-research-labs/pearl
• Miner docs: https://github.com/pearl-research-labs/pearl/tree/master/miner

Launch Benchmark

Original (Meta's) llama-3.3-70B-Instruct vs. our "two-for-one" Pearl-certified variant. Both executions were done with 4xH200 GPUs. We explore several parallelism techniques. TMADs, i.e., Tera MADs, is a metric counting number of Multiply-Add (MAD) operations. Useful MADs is the total number of MAD operations done anyway that are used for mining.

How To Use (Pearl vLLM Plugin)

This model is intended to be served through the Pearl miner stack, where vLLM inference is integrated with Pearl mining workflows.

Typical flow:

1. Run pearld with RPC enabled.
2. Start the Pearl miner/vLLM stack.
3. Serve this model through vLLM while Pearl gateway/miner components handle mining-side integration.

High-level prerequisites:

• Python 3.12
• uv
• CUDA + NVIDIA GPU (sm90 class, e.g. H100/H200, per project docs)
• Rust toolchain
• Running pearld node with RPC credentials

Docker Example

From the Pearl repository root:

docker buildx build -t vllm_miner . -f miner/vllm-miner/Dockerfile

docker run --rm -it --gpus all \
  -p 8000:8000 -p 8337:8337 -p 8339:8339 \
  -e PEARLD_RPC_URL=<PEARLD_URL> \
  -e PEARLD_RPC_USER=<RPC_USER> \
  -e PEARLD_RPC_PASSWORD=<RPC_PASSWORD> \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  --shm-size 8g \
  vllm_miner:latest \
  pearl-ai/Llama-3.3-70B-Instruct-pearl \
  --host 0.0.0.0 --port 8000 \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.9 \
  --enforce-eager