Principle:Bigscience workshop Petals Server Configuration

Knowledge Sources	Petals Petals: Collaborative Inference and Fine-tuning of Large Models
Domains	Distributed_Computing, Infrastructure, Resource_Management
Last Updated	2026-02-09 14:00 GMT

Overview

The process of configuring a Petals server by determining GPU resources, estimating throughput, resolving data types, and establishing DHT connectivity before serving transformer blocks.

Description

Server Configuration encompasses the initialization phase where a Petals server determines its operational parameters:

Resource estimation:

• Block count: If not specified, automatically calculated from available GPU memory by estimating per-block memory requirements (weights + KV cache)
• Throughput: Benchmarked by running sample inference/forward passes and measuring network speed (via speedtest-cli)
• Data type: Resolved from model config (float16/bfloat16) based on hardware capabilities
• Quantization: NF4 is default on CUDA for memory efficiency; INT8 available for better quality

Network setup:

• DHT connection: Connects to the hivemind Kademlia DHT using initial peers
• Reachability check: Validates that the server is reachable from the network
• Port configuration: Sets up P2P listening endpoints

KV cache sizing:

• Calculates attn_cache_bytes based on model architecture (MQA vs MHA)
• Default: 16384 tokens for Multi-Query Attention, 4096 for standard MHA

Usage

This principle is applied automatically during Server.__init__. Server operators can override auto-detected values via CLI flags for fine-grained control. Understanding this process helps operators optimize their server contribution.

Theoretical Basis

Memory budget calculation:

# Abstract resource estimation
block_memory = get_block_size(config, dtype, quant_type)  # Per-block weight memory
cache_per_token = 2 * num_layers * hidden_size * dtype_size  # KV cache per token
total_cache = cache_per_token * attn_cache_tokens

available_memory = torch.cuda.get_device_properties(0).total_mem
usable_memory = available_memory * 0.9  # Safety margin

if num_blocks is None:
    num_blocks = (usable_memory - total_cache) // block_memory

Throughput estimation:

• Inference RPS: Single-token forward pass throughput
• Forward RPS: Multi-token forward pass throughput
• Network RPS: Hidden state transfer rate (bandwidth / hidden_state_size)
• Effective throughput: min(compute_rps, network_rps)

Related Pages

Implemented By

• Implementation:Bigscience_workshop_Petals_Server_Init