Use-case landing page

Best cloud GPU for fine-tuning

Fine-tuning usually starts at 80GB-class GPUs, and the current best live balance is A100 PCIE on Vast.ai at $1.00/hr.

80GB+ VRAM On-demand pricing Fine-tuning budget Live provider snapshots

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Best current balance

$1.00/hr

Vast.ai · A100 PCIE

Cheapest 80GB+ row

$1.00/hr

A100 PCIE

Highest memory tracked

192GB

B200 on RunPod

Provider coverage

Fresh 80GB+ rows updated Jul 31, 2026

Search guide

Best cloud GPU for fine-tuning

Fine-tuning buyers rarely search for a raw SKU first. They usually want the cheapest card that still gives them enough memory headroom to train, checkpoint, and recover from longer-context runs.

This page narrows the market to 80GB+ inventory, ranks the lowest-cost current rows, and highlights where newer architectures are close enough in price to justify skipping the absolute budget pick.

Cheapest provider right now

Fine-tuning recommendation summary

Our best current balance for fine-tuning is A100 PCIE on Vast.ai at $1.00/hr, because it clears the 80GB bar while keeping hourly spend controlled. The cheapest qualifying row is A100 PCIE on Vast.ai at $1.00/hr. The highest-memory tracked option is B200 on RunPod with 192GB at $5.89/hr.

Methodology and freshness

How this guide is computed

We filter the live market to on-demand rows with at least 80GB of VRAM, then compare the cheapest entries against the highest-memory alternatives so the recommendation balances price with operational headroom.

FAQ

Best cloud GPU for fine-tuning FAQ

What is the best cloud GPU for fine-tuning right now?

Our best current balance for fine-tuning is A100 PCIE on Vast.ai at $1.00/hr, because it clears the 80GB bar while keeping hourly spend controlled.

Why does this guide focus on 80GB-class GPUs?

That is the practical starting point for many adapter-heavy and full fine-tuning jobs once you account for weights, optimizer state, activations, and room for longer sequences.

Should I choose the cheapest GPU or the newest architecture for fine-tuning?

The cheapest qualifying row is A100 PCIE on Vast.ai at $1.00/hr. The highest-memory tracked option is B200 on RunPod with 192GB at $5.89/hr. If the newer or larger-memory option is close in price, it usually buys back operational headroom more cleanly than squeezing onto the absolute cheapest card.

How fresh is the fine-tuning price data?

This page is recalculated from the latest on-demand rows. The freshest qualifying row is from Jul 31, 2026, and collectors run daily.

More GPU workload guides

These follow-up guides target adjacent high-intent searches so buyers can move from a single query into the next pricing question without bouncing back to search.

What to do with this page

Best cloud GPU for fine-tuning at a glance

Use these recommendation cards to separate the current budget floor from the higher-headroom or broader-catalog alternatives that matter for this decision.

Best overall

A100 PCIE

Our best current balance for fine-tuning is A100 PCIE on Vast.ai at $1.00/hr, because it clears the 80GB bar while keeping hourly spend controlled.

Budget baseline

A100 PCIE

The cheapest qualifying row is A100 PCIE on Vast.ai at $1.00/hr.

More headroom

B200

The highest-memory tracked option is B200 on RunPod with 192GB at $5.89/hr.

Live pricing inputs

Current fine-tuning-friendly GPU rows

These rows all clear the 80GB memory floor and are ranked by current on-demand median price.

Updated Jul 31, 2026

GPU / target	Provider	Type	Hourly	Monthly	Why it fits
A100 PCIE Mid-Range	Vast.ai Provider site	on-demand	$1.00/hr	$732/mo	80GB Ampere memory envelope for fine-tuning workloads.
A100 SXM4 High Performance	Vast.ai Provider site	on-demand	$1.06/hr	$771/mo	80GB Ampere memory envelope for fine-tuning workloads.
A100 PCIE Mid-Range	RunPod Provider site	on-demand	$1.39/hr	$1,015/mo	80GB Ampere memory envelope for fine-tuning workloads.
A100 SXM4 High Performance	RunPod Provider site	on-demand	$1.49/hr	$1,088/mo	80GB Ampere memory envelope for fine-tuning workloads.
H100 PCIE High Performance	Vast.ai Provider site	on-demand	$1.93/hr	$1,411/mo	80GB Hopper memory envelope for fine-tuning workloads.
A100 SXM4 High Performance	Lambda Provider site	on-demand	$1.99/hr	$1,453/mo	80GB Ampere memory envelope for fine-tuning workloads.
H100 SXM Flagship	Vast.ai Provider site	on-demand	$2.20/hr	$1,607/mo	80GB Hopper memory envelope for fine-tuning workloads.
H200 Flagship	Lambda Provider site	on-demand	$2.29/hr	$1,672/mo	141GB Hopper memory envelope for fine-tuning workloads.
MI300X Flagship	RunPod Provider site	on-demand	$2.39/hr	$1,745/mo	192GB AMD CDNA 3 memory envelope for fine-tuning workloads.
H100 NVL High Performance	Vast.ai Provider site	on-demand	$2.76/hr	$2,013/mo	94GB Hopper memory envelope for fine-tuning workloads.

Agent-assisted setup

Hand this runbook to Claude Code or Codex

Open a terminal in the repository where you want the deployment files, start claude or codex, then paste this prompt. It asks the agent to verify sources and stop before it creates billable infrastructure.

Claude Code quickstart ↗ Codex docs ↗

Deployment prompt

Use the infrastructure or model context on this page to create a reproducible open-model deployment. Use this guide as the starting context: https://www.getflops.ai/guides/best-cloud-gpu-for-fine-tuning. Read the linked model card and provider documentation before choosing hardware or runtime settings. Open every linked primary source and flag any mismatch instead of guessing. Create a deployment folder containing README.md, .env.example with no secrets, a pinned start script or infrastructure manifest, and smoke-test.sh. Make the endpoint OpenAI-compatible where the runtime supports it. Run local/static validation, estimate the billable resources, and stop before provisioning paid infrastructure until I approve.

Guardrails included No secrets in files · verify primary docs · approval before spend