Overview

Python → Petaflops in 15 seconds. Flow procures GPUs through Mithril, spins InfiniBand-connected instances, and runs your workloads—zero friction, no hassle.

Background

There's a paradox in GPU infrastructure today: Massive GPU capacity sits idle, even as AI teams wait in queues—starved for compute. Mithril, the AI-compute omnicloud, dynamically allocates GPU resources from a global pool (spanning Mithril's first-party resources and 3rd-party partner cloud capacity) using efficient two-sided auctions, maximizing surplus and reducing costs. Mithril seamlessly supports both reserved-in-advance and just-in-time workloads—maximizing utilization, ensuring availability, and significantly reducing costs.

Infrastructure mode

flow instance create -i 8xh100 -N 20
╭─ Instance Configuration ────────────────────────────────╮
│                                                         │
│  Name           multinode-run                           │
│  Command        sleep infinity                          │
│  Image          nvidia/cuda:12.1.0-runtime-ubuntu22.04  │
│  Working Dir    /workspace                              │
│  Instance Type  8xh100                                  │
│  Num Instances  20                                      │
│  Max price      $12.29/hr                               │
│                                                         │
╰─────────────────────────────────────────────────────────╯

flow instance list
╭─────────────────────────────── ❊ Flow ────────────────────────────────╮
│                                                                       │
│     #     Status     Instance               GPU       Owner     Age   │
│     1   ● running    multinode-run        8×H100·80G  alex       0m   │
│     2   ● running    interactive-77c31e   A100·80G    noam       5h   │
│     3   ○ cancelled  dev-a100-test        A100·80G    alex       1d   │
│                                                                       │
╰───────────────────────────────────────────────────────────────────────╯

Research mode (in early preview)

flow submit "python train.py" # -i 8xh100
⠋ Bidding for best‑price GPU node (8×H100) with $12.29/h100-hr limit_price…
✓ Launching on NVIDIA H100-80GB for $1/h100-hr

Why choose Flow

Status quo GPU provisioning involves quotas, complex setups, and queue delays, even as GPUs sit idle elsewhere or in recovery processes. Flow addresses this:

Dynamic Market Allocation – Efficient two-sided auctions ensure you pay the lowest market-driven prices rather than inflated rates.

Simplified Batch Execution – An intuitive interface designed for cost-effective, high-performance batch workloads without complex infrastructure management.

Provision from 1 to thousands of GPUs for long-term reservations, short-term "micro-reservations" (minutes to weeks), or spot/on-demand needs—all interconnected via InfiniBand. High-performance persistent storage and built-in Docker support further streamline workloads, ensuring rapid data access and reproducibility.

Why Flow + Mithril?

Pillar
Outcome
How

Iteration Velocity and Ease

Fresh containers in seconds; from idea to training or serving instantly.

flow dev for DevBox or flow run to programmatically launch tasks

Best price-performance via market-based pricing

Preemptible secure jobs for $1/h100-hr

Blind two-sided second-price auction; client-side bid capping

Availability and Elasticity

GPUs always available, self-serve; no haggling, no calls.

Uncapped spot + overflow capacity from partner clouds

Abstraction and Simplification

InfiniBand VMs, CUDA drivers, auto-managed healing buffer—all pre-arranged.

Mithril virtualization and base images preconfigured + Mithril capacity management.

"The tremendous demand for AI compute and the large fraction of idle time makes sharing a perfect solution, and Mithril's innovative market is the right approach." — Paul Milgrom, Nobel Laureate (Auction Theory and Mechanism Design)

Pricing & Auctions

How Flow leverages Mithril's Second-Price Auction:

You express your limit price (or leverage flow defaults); GPUs provision instantly at the fair market clearing rate.

Your Bid's Limit Price
Current Spot Price
You Pay

$3.00

$1.00

$1.00

$3.00

$3.50 (spike)

No allocation

  • Your billing price = highest losing bid.

  • Limit price protects from surprises.

  • Resell unused reservations into the auction to recoup costs.

Full Auction Mechanics →

Key Concepts to Get Started

Auctions & Limit Prices

Flow uses Mithril spot instances via second-price auctions. See auction mechanics.

Core Workflows

Infrastructure mode

  • flow instance create -i 8xh100 -N 20 → spin up a 20-node GPU cluster in seconds

  • flow volume create -s 10000 -i file → provision 10 TB of persistent, high-speed storage

  • flow ssh instance -- nvidia-smi → run across all nodes in parallel

In early preview

Research mode

  • flow dev → interactive loops in seconds.

  • flow submit → reproducible batch jobs.

  • Python API → easy pipelines and orchestration.

Examples

# Launch a batch job on discounted H100s
flow submit "python train.py" -i 8xh100

# Frictionlessly leverage an existing SLURM script
flow submit job.slurm

# Serverless‑style decorator
@app.function(gpu="a100")

Ideal Use Cases

  • Rapid Experimentation – Quick iterations for research sprints.

  • Instant Elasticity – Scale rapidly from one to thousands of GPUs.

  • Collaborative Research – Shared dev environments with per-task cost controls.

Flow is not yet ideal for: always‑on ≤100 ms inference, strictly on‑prem regulated data, or models that fit on laptop or consumer-grade GPUs.

Architecture (30‑s view)

Your intent ⟶ Flow Execution Layer ⟶ Global GPU Fabric

Flow SDK abstracts complex GPU auctions, InfiniBand clusters, and multi-cloud management into a single seamless and unified developer interface.

Under the Hood (Advanced)

  • Bid Caps – Protect budgets automatically.

  • Self-Healing – Spot nodes dynamically migrate tasks.

  • Docker/Conda – Pre-built images or dynamic install.

  • Multi-cloud Ready – Mithril (with Oracle, Nebius integrations internal to Mithril), and more coming

  • SLURM Compatible – Run #SBATCH scripts directly.

Key Features Summary

  • Distributed Training – Multi-node InfiniBand clusters auto-configured

  • Code Upload – Automatic with .flowignore (or .gitignore fallback)

  • Live Debugging – SSH into running instances (flow ssh)

  • Cost Protection – Built-in max_price_per_hour safeguards

  • Jupyter Integration – Connect notebooks to GPU instances

Repository: https://github.com/mithrilcompute/flow

Further Reading

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