Rent a GPU to Run a Giant Open Model? What It Costs and When It's Worth It

TL;DR: Meta’s guidance for running its Muse Spark Open model in NVFP4 form is at least 700 GB of GPU memory, which Meta says means roughly eight H200 GPUs or four Blackwell Ultra GPUs. Renting eight H200s on Runpod costs about $42 an hour at its listed on-demand price, and when we looked at the Deploy a Pod screen on October 24 it offered at most seven H200s in a single Pod (987 GB, about $37 an hour), which still clears Meta’s minimum. A two-hour test session is under $100, but leaving it running for a month is over $30,000. By our rough math, a hosted API is cheaper unless you’re pushing more than about 20 to 23 million tokens an hour through the model, so renting mostly makes sense for short experiments, fine-tuning, or strict data-control needs. We haven’t deployed a Pod ourselves, and this guide contains a referral link, which we explain below.

A Quick Note on How This Was Put Together

This is a research guide, not a hands-on test: we created a Runpod account and looked at its Deploy a Pod screen, but we haven’t deployed a Pod or run Muse Spark Open. Prices come from Runpod’s pricing and documentation pages and that screen as we saw them on October 24, 2026, and the model requirements come from Meta’s launch materials, as summarized in our Muse Spark Open explainer. GPU rental prices change often, so check Runpod’s page before you budget. Calculations are ours and are labeled as estimates.

About the link in this guide: the Runpod link below is a referral link. Per Runpod’s referral program page, when someone signs up through a referral link and spends $10, both the new user and the referrer receive a random credit of $5 to $500, and the referrer earns 3% of the new user’s Pod spend (5% for Serverless) for the first six months. It doesn’t change the price you pay. We’d write the same cost breakdown without it, and one conclusion below is that most readers shouldn’t rent at all.

What You’d Actually Be Renting

Muse Spark Open is a very large model: about 816 billion parameters, with the compressed NVFP4 weights around 530 GB. Per Meta’s model card, you need at least 700 GB of GPU memory to run that version, because the weights aren’t the only thing taking up space. Long conversations add a memory cache on top, and one third-party estimate puts the cache for the full 1-million-token context at roughly 266 GB (unverified).

That’s why single consumer GPUs are out. An RTX 4090 has 24 GB; even an 80 GB H100 is far short. The question becomes whether to rent a multi-GPU machine by the hour instead of buying hardware. For the bigger picture on what Meta released, see our Muse Spark Open explainer.

What It Costs on Runpod

Runpod’s pricing page lists on-demand prices per GPU per hour. For the GPUs relevant to a model this size:

GPU	Memory each	Price per GPU per hour
H200	141 GB	$5.29
B200	180 GB	$7.99
H100 SXM	80 GB	$3.99
A100	80 GB	$1.79
RTX 5090	32 GB	$1.19
RTX 4090	24 GB	$0.89

Putting those together for Muse Spark Open, using Meta’s stated requirement:

Setup	Total GPU memory	Estimated cost per hour
8 × H200 (the configuration Meta cites)	1,128 GB	$42.32
7 × H200 (the most the Deploy a Pod screen offered on Oct. 24)	987 GB	$37.03
4 × B200 (our arithmetic, not a Meta recommendation)	720 GB	$31.96

The B200 line only just clears the 700 GB minimum, leaves almost no room for long-context cache, and B200 isn’t the same chip as the “Blackwell Ultra” Meta mentions, so treat it as a lower bound rather than a tested option. Availability matters as much as price. Runpod’s pricing and documentation pages don’t state a maximum number of GPUs per Pod, but the Deploy a Pod screen shows how many are free at that moment, and on October 24 it listed H200 SXM at up to 7 per Pod, B200 at up to 3, H100 SXM at up to 8, and the Blackwell Ultra-class B300 (288 GB, $8.99 an hour) at 1. Those counts change constantly, so an 8-GPU H200 Pod may or may not be available when you look. Seven H200s total 987 GB, comfortably above Meta’s 700 GB minimum though below the eight Meta mentions, and we don’t know how the model performs on seven. Eight H100s would total only 640 GB, which falls short. Runpod also sells multi-node Clusters, which its pricing page says scale up to 64 GPUs, for larger needs.

A few cost details from Runpod’s documentation:

• Billing is per second or finer (the docs say per second; the console showed “billed per millisecond”), with no charge for data in or out.
• Storage costs extra. Network volumes run $0.07 per GB per month under 1 TB, which for 530 GB of weights is about $37 a month. Volume disks cost $0.10 per GB per month while running and $0.20 while a Pod is stopped, so the same weights would be about $53 running or $106 stopped.
• You need credit up front. Runpod requires at least one hour’s worth of credit for your chosen configuration to start an on-demand Pod, so about $42 for eight H200s or $37 for seven.
• Balance hitting zero stops the Pod, and without a network volume your data can’t be recovered.
• There’s a default spending limit of $80 per hour across all resources, enough for the setups above but worth knowing about.

Example: a two-hour experiment on eight H200s is about $85 in compute ($74 on seven), plus a few dollars of storage, plus the time to download 530 GB of weights, during which the meter may already be running. Leaving it on 24/7 would be about $30,470 a month ($42.32 × 24 × 30).

Renting vs. Using a Hosted API

Several providers already host Muse Spark Open as an API, including Together AI, Fireworks, Baseten, and OpenRouter, per Meta. One third-party writeup captured an OpenRouter price of $1.25 per million input tokens and $4.25 per million output tokens on October 16. That’s a single secondary source and prices change, so check the provider’s page.

Here’s the break-even arithmetic, so you can plug in your own numbers. Assume your workload is four input tokens for every output token. The blended API price is (4 × $1.25 + $4.25) ÷ 5 = $1.85 per million tokens. Dividing the $42.32 hourly rental cost by $1.85 gives about 22.9 million tokens per hour, or roughly 6,350 tokens per second, sustained for every hour you pay for (about 20 million per hour with seven GPUs at $37.03). Unless your own setup really can keep a rented eight-GPU machine that busy, the API wins on price. We haven’t seen published throughput numbers for this model on seven or eight H200s, so whether you could even reach that rate is an open question. To model your own mix of input, output, and cached tokens, try our LLM API cost calculator.

When Renting Does Make Sense

• Short experiments. If you want to try the model on your own tasks for an afternoon without committing to an API provider, a few hours of rental is cheap relative to buying hardware.
• Fine-tuning or modifying the weights. The Muse Model License permits fine-tuning, and hosted APIs generally don’t let you change the model.
• Strict control over where data goes. Renting a machine you control isn’t the same as running on-premises, but some teams prefer it to sending prompts to a third-party API. Read Runpod’s security terms for your own requirements.
• Smaller models. For open models that fit in 24 to 80 GB, a single RTX 4090 ($0.89 an hour), RTX 5090 ($1.19), or A100 ($1.79) is a far more modest bill than the eight-GPU example, and this is where most people experimenting with local models actually land.

When You Should Skip It

• You just want to use the model. A hosted API costs nothing until you send requests and avoids setup, storage, and idle-time charges.
• You’d leave it running. An always-on rental costs thousands per month; the economics only work for short sessions or very high, steady use.
• You need a polished, supported setup. Multi-GPU inference setups for brand-new models can take effort to configure, and we can’t tell you from experience how smooth Runpod’s is.
• A cheaper laptop plus a cloud subscription already covers your needs. See our Surface Laptop Ultra explainer for how local-AI hardware compares with just paying for hosted models.

If You Decide to Try Runpod

You can create an account through our referral link: Runpod (referral link). Before starting a large job, check these things in the console:

1. Whether the multi-GPU configuration you need is available right now (the Deploy a Pod screen shows a “max” count per GPU type), and in which cloud tier (Runpod offers Secure Cloud and Community Cloud, with different reliability and pricing).
2. That you attach a network volume if you don’t want to re-download hundreds of gigabytes of weights, so a stopped Pod doesn’t lose them.
3. That you have a balance comfortably above the hourly cost, so the Pod doesn’t shut off mid-run.
4. How to stop the Pod and confirm billing has stopped when you finish.

FAQ

Can I run Muse Spark Open on a single rented GPU? Not the full model. Meta’s guidance for the NVFP4 version is at least 700 GB of GPU memory, and the largest single GPU on Runpod’s list has 180 GB. Smaller or heavily compressed builds exist but trade quality for memory.

How much does it cost to rent GPUs for Muse Spark Open? About $42 per hour for eight H200s at Runpod’s listed price, or about $37 for seven, which was the most the Deploy a Pod screen offered in one Pod on October 24. Storage is extra. Runpod bills by the second, so a short session costs proportionally less.

Is renting cheaper than using the API? Usually not, unless you keep the hardware busy. By our estimate, break-even is around 23 million tokens an hour at the OpenRouter price we found, which is a very high sustained rate.

Does the referral link cost me anything? No. It doesn’t change Runpod’s prices. Per Runpod’s referral page, a new user who spends $10 gets a random credit between $5 and $500, and credits expire after 90 days.

Have you used Runpod? Only to look around: we created an account and viewed the Deploy a Pod screen, but we haven’t deployed a Pod. This guide is based on Runpod’s pricing, documentation, and that screen, and we’ll update it if that changes.

Final Thoughts

Renting a GPU cluster is a reasonable way to try a giant open model for an afternoon, and an expensive way to use one day to day. Compare the hourly rental cost with hosted API prices before you commit, and keep sessions short.

Sources: Runpod pricing, Runpod referral and affiliate program, Runpod documentation: Pod pricing, Runpod documentation: Pods overview, Meta: Build with Muse Spark Open, Hugging Face model card, Digital Applied