I have $800 for local LLMs, should I buy a GPU or rent?

Rent, specifically on RunPod. $800 is 100+ hours on a B300 with 288GB of VRAM, one of the fastest single-GPU setups in existence, versus a mid-range consumer card that gates half the interesting models. Buy a card once you know exactly what you run daily; rent while you're figuring that out.

Why do egress and bandwidth charges matter so much?

Because the first act of any cloud GPU session is downloading a model, and modern models are huge. Vast.ai charges for that bandwidth, so on large models the real hourly cost climbs well past the sticker price. RunPod and Modal don't charge egress, which is a big part of why they win for large-model work.

Do I need maximum VRAM for local LLMs?

No. That's the most common misconception. Text and code generation hit a point of diminishing returns on VRAM; the extreme VRAM tiers pay off for image generation, video generation and 3D work, which are far more VRAM-hungry. Past a B300's 288GB, more VRAM for pure LLM inference is rarely worth it on a single GPU.

What's the biggest mistake people make speccing an AI setup?

Treating VRAM as the only number. It's the combination of processing power and VRAM that matters. You can build a budget rig where a CPU addresses a ton of memory, but single-digit tokens per second isn't usable if you want AI as a real tool with agent workflows. A model that fits but crawls is barely better than a model that doesn't fit.

When does owning beat renting?

When you have a fixed daily workload that fits your card's VRAM, care about data privacy, or already own the silicon. The crossover is moving though: providers are discounting rentals to build market share while consumer AI subscriptions restrict usage, so right now the incentives strongly favor developers who rent. That balance will shift as compute gets commoditized. Blackwell is the start of that.

Cloud GPU rentals · we benched 51 GPUs across all three · Updated July 2026

RunPod vs Vast.ai vs Modal: The Real Differences

Every AI benchmark on this site came off rented cloud GPUs, and I used all three of the big providers to do it: Vast.ai, RunPod and Modal. They all have their pros and cons, and the differences aren't the ones the pricing pages advertise. This is the practical version: what each one actually costs once you account for moving models around, and which one fits which job.

Providers used for our bench

Vast.ai, RunPod, Modal

GPUs measured across them

RTX 2060 to B300

~$7/hr

B300 with 288GB VRAM

the fastest single-GPU rental

Egress on RunPod & Modal

Vast.ai charges for bandwidth

Vast.ai, biggest marketplace, watch the bandwidth bill. Vast.ai looks like the cheapest sticker price, but it charges for egress and bandwidth, and if you're renting cloud GPUs, the first thing you do is download a model off Hugging Face that might be enormous. That bandwidth cost gets baked into what you actually pay, so on large models Vast.ai quietly becomes the most expensive of the three. Where it shines: consumer and workstation cards. Vast.ai has by far the widest selection of consumer-grade silicon, the cards with lower VRAM where you're running smaller models anyway, so the download tax doesn't bite. If you want to test on the exact card you're thinking of buying, Vast.ai probably has it.

RunPod: the best all-in-one, and the one I use most. RunPod won't list as many GPUs as Vast.ai, but it's the best overall package: no egress charges, a solid ecosystem, and the least friction from 'I have an idea' to 'the model is running.' For everyday AI work, inference, fine-tuning experiments, benchmarking, it's the default. One thing worth knowing about the current market: rental pricing is genuinely favorable right now. Think about what an hour costs you versus what the hardware costs them. The providers are effectively discounting compute to build a customer base, at the same time the AI subscription services are tightening token limits. Right now the incentives favor the developer who rents.

Modal, built for the really big jobs. Modal is datacenter GPUs only and it prices higher because it's more centralized, as a general-purpose rental it's the expensive option, though the free starting credits take the edge off trying it. But it has one killer capability for very large models: you can have a cheap CPU container download the entire model first, then hand off to the GPU container. On RunPod you're usually eating GPU-hour costs while a few hundred gigabytes download. When the model is huge, the GLM-class frontier models that need multiple B300s, that architecture is the difference between reasonable and painful. That's Modal's lane: the workloads too big for everything else.

The rent-vs-buy crossover, honestly. Here's the math that changed how I think about it: a B300 with 288GB of VRAM rents for about $7/hour. An $800 budget, the price of a mid-range consumer card, buys you 100+ hours on one of the fastest single-GPU setups that exists. That's enough to run 370B-parameter models, or set up multi-model consensus pipelines to cut hallucinations. Gaming compute got commoditized years ago: a 20, 30, or 40-series card games just fine, and going 40-series to 50-series is 110 FPS to 130 FPS, not the old 30-to-60 jump. AI compute is still scarce: limited VRAM, limited power. That's also why bolting more VRAM onto an older card wouldn't fix it, a 30-series card doesn't have the compute to run a big model at usable speed even if the model fit. If you're eyeing Blackwell, you're buying for AI whether you admit it or not: and at the workstation end ($50k+ of RTX PRO 6000s), the honest comparison is thousands of hours of rented B300 time for the same money. The one rule: have an objective before you rent serious horsepower. A B300 at $7/hour rewards a plan and punishes wandering.

Our verdict

Use-case routing, based on actually using all three: testing or running smaller models on consumer/workstation cards → Vast.ai (widest selection, and the bandwidth charge doesn't hurt on small models). Everyday AI work and most rentals → RunPod (best all-in-one, no egress, the one we use most). Massive models where the download alone is a project → Modal (CPU container downloads, GPU container runs, you don't pay GPU rates to wait on Hugging Face). And if your budget is under about $1,000 and your goal is local LLMs today: rent first, buy after you know your workload.

FAQ

I have $800 for local LLMs, should I buy a GPU or rent?: Rent, specifically on RunPod. $800 is 100+ hours on a B300 with 288GB of VRAM, one of the fastest single-GPU setups in existence, versus a mid-range consumer card that gates half the interesting models. Buy a card once you know exactly what you run daily; rent while you're figuring that out.
Why do egress and bandwidth charges matter so much?: Because the first act of any cloud GPU session is downloading a model, and modern models are huge. Vast.ai charges for that bandwidth, so on large models the real hourly cost climbs well past the sticker price. RunPod and Modal don't charge egress, which is a big part of why they win for large-model work.
Do I need maximum VRAM for local LLMs?: No. That's the most common misconception. Text and code generation hit a point of diminishing returns on VRAM; the extreme VRAM tiers pay off for image generation, video generation and 3D work, which are far more VRAM-hungry. Past a B300's 288GB, more VRAM for pure LLM inference is rarely worth it on a single GPU.
What's the biggest mistake people make speccing an AI setup?: Treating VRAM as the only number. It's the combination of processing power and VRAM that matters. You can build a budget rig where a CPU addresses a ton of memory, but single-digit tokens per second isn't usable if you want AI as a real tool with agent workflows. A model that fits but crawls is barely better than a model that doesn't fit.
When does owning beat renting?: When you have a fixed daily workload that fits your card's VRAM, care about data privacy, or already own the silicon. The crossover is moving though: providers are discounting rentals to build market share while consumer AI subscriptions restrict usage, so right now the incentives strongly favor developers who rent. That balance will shift as compute gets commoditized. Blackwell is the start of that.