Best GPU for Local LLMs: Fitting 30B Models in 16GB or 24GB VRAM
A practical guide to running 30B local AI models on 16GB and 24GB GPUs, comparing RTX 3090, dual 5060 Ti, AMD and Intel cards.
Why does VRAM matter more than anything else for local AI?
VRAM is the single biggest bottleneck for running large language models on consumer hardware. A model has to fit entirely inside the graphics card’s memory to run at usable speed, and a popular 30 billion parameter model quantized to 4-bit typically takes up somewhere around 15 to 18GB on its own, before you add room for context (the conversation history the model has to hold in memory while it works). On a 16GB card, that leaves almost no headroom, and things that should be fast start stuttering or failing outright. On a 24GB card, the same model fits with space to spare. That gap, between “tight” and “comfortable,” is why 24GB has become the number everyone building with local models keeps coming back to.
TL;DR
- 24GB of VRAM is the practical threshold for running quantized 30B-parameter models comfortably, while 16GB cards leave little to no room for context.
- 4-bit quantization shrinks a 30B model to roughly 15-18GB, which is why the extra 6-8GB on a 24GB card makes a real difference in stability and usable context length.
- The RTX 3090 remains the most popular budget pick for 24GB because it combines that memory size with about 936GB/s of bandwidth, even though it’s secondhand, power hungry, and runs hot.
- Running two RTX 5060 Ti (16GB) cards together gets you 32GB of pooled memory, but splitting a model across two GPUs over a narrower memory bus costs some speed.
- AMD’s Radeon AI Pro R9700 and Intel’s Arc Pro B70 both ship with 32GB and launched well under $1,500, undercutting Nvidia on memory-per-dollar, but both ask users to work outside the CUDA ecosystem.
- Nvidia’s CUDA software stack is still the easiest path for running local models, which is a major reason Nvidia has little competitive pressure to put out an affordable 24GB consumer card.
- An rumored RTX 5080 variant with 24GB reportedly got cancelled, reportedly over the cost of denser memory chips, leaving a gap between 16GB gaming cards and 24GB workstation cards priced for businesses.
How much VRAM do you actually need to run a 30B model?
The rule of thumb is simple: figure out the model’s quantized size, then add a buffer for context. A 30B-parameter model at 4-bit quantization lands around 15 to 18GB depending on the exact quantization format and architecture. That number alone can already brush up against the ceiling of a 16GB card. Once you factor in the key-value cache needed to hold conversation history, a 16GB card gets squeezed fast, especially with longer prompts or chat sessions.
A 24GB card gives you breathing room. The model loads, there’s space left for context, and you’re not fighting the GPU for every extra token of memory. This is why 24GB keeps surfacing as the de facto line between “it technically runs” and “it runs well” for anyone serious about local inference on a single GPU.
Smaller models (7B to 13B class) are far more forgiving and run fine on 12 to 16GB cards. The 24GB conversation specifically matters for people trying to run the larger, more capable 30B-class open models that have become popular for coding and general reasoning tasks.
What are the actual GPU options at 16GB and 24GB?
For 24GB specifically, the most commonly used option today is a secondhand RTX 3090. It offers 24GB of VRAM and about 936GB/s of memory bandwidth, numbers that still hold up well against newer cards. The tradeoffs are real: it’s an older card, it draws a lot of power, it runs hot, and buying used always carries some risk around wear and warranty.
For people who’d rather buy new, one option is running two RTX 5060 Ti cards (16GB each) in the same machine, for a combined 32GB. These launched around $429 each and draw about 180W per card. The catch is that splitting a single model across two GPUs over a narrower memory bus introduces overhead, so you trade some raw speed for the extra capacity.
On the professional side, Nvidia’s RTX Pro 4000 offers 24GB of GDDR7 in a single-slot design, but it’s priced and positioned for business and workstation buyers rather than hobbyists or students.
Outside Nvidia, two newer entrants are worth watching:
- AMD’s Radeon AI Pro R9700, offering 32GB, launched at around $1,300.
- Intel’s Arc Pro B70, also offering 32GB, with a suggested starting price around $949.
Both beat Nvidia’s equivalent cards on memory-per-dollar at launch. Both are also workstation-class cards rather than mainstream gaming GPUs, and both ask buyers to step outside the CUDA ecosystem that most local-AI tooling is built around.
Is AMD or Intel a realistic alternative to Nvidia right now?
On paper, yes, in terms of memory. AMD’s ROCm and Intel’s software stacks have matured significantly, and both companies now offer cards with more VRAM than Nvidia’s comparably priced consumer options. If raw VRAM-per-dollar is your only metric, the R9700 and the Arc Pro B70 both look like better deals than anything Nvidia currently sells at a similar price for 24GB-plus of memory.
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The practical issue is software. Most local LLM tooling, from inference engines to fine-tuning frameworks, was built first and optimized hardest for CUDA. ROCm and Intel’s equivalents have closed a lot of that gap, but “closed a lot of the gap” still means more friction: less out-of-the-box compatibility, smaller community troubleshooting threads, and more cases where you’re the one debugging a driver issue instead of finding a existing fix. For people comfortable with some tinkering, that’s a fair tradeoff for more memory at a lower price. For people who want things to just work, Nvidia and CUDA remain the lower-friction choice, even at a worse price per gigabyte.
Why is there no affordable 24GB Nvidia card right now?
This is the gap that’s frustrated a lot of people trying to build local AI setups on a budget. Nvidia’s current consumer lineup tops out at 16GB for mainstream gaming cards (like the RTX 5080), while its 24GB options (like the RTX Pro 4000) are priced and marketed for business use. There’s reportedly no card sitting in between, affordable and 24GB, aimed at individual builders.
Reports circulated that Nvidia was preparing a 24GB variant of the RTX 5080, which would have filled exactly that gap. Those reports also suggest the card was cancelled, reportedly over cost: the denser 3GB memory chips needed for a 24GB config on that board are said to cost roughly three times as much as the 2GB chips used in the standard 16GB version. None of this has been confirmed by Nvidia either way.
A second, more structural explanation is worth considering regardless of whether the cancellation report is accurate: dense memory chips are in high demand, and Nvidia already uses them in its higher-margin workstation and enterprise cards. There’s less incentive to route scarce, expensive memory into a budget gaming card when it can go into a product sold at a much higher price. Whether or not this specific card comes back, the underlying dynamic, limited memory supply flowing toward the most profitable products first, is likely to keep shaping what’s available at the consumer level.
FAQ
How much VRAM do I need to run a 30B parameter local model?
Plan for at least 20 to 24GB. A 4-bit quantized 30B model typically takes up 15 to 18GB on its own, and you need additional room for context, so a 24GB card gives a comfortable margin while a 16GB card is a tight fit.
Is a used RTX 3090 still a good choice for local AI in 2024-2025?
Yes, it’s one of the most commonly recommended options because it offers 24GB of VRAM and about 936GB/s of bandwidth at a used-market price well below newer 24GB workstation cards. The downsides are higher power draw, heat, and the usual risks of buying used hardware.
Can I combine two smaller GPUs instead of buying one big one?
Yes. Running two 16GB cards, such as a pair of RTX 5060 Ti cards, gives you 32GB of combined memory. The tradeoff is that splitting a model across two GPUs over a narrower memory bus adds overhead and reduces speed compared to a single GPU with equivalent total memory.
Are AMD and Intel GPUs viable for running local LLMs?
They’re increasingly viable in terms of hardware, with AMD’s Radeon AI Pro R9700 and Intel’s Arc Pro B70 both offering 32GB at competitive prices. The limiting factor is software maturity: CUDA still has the broadest tooling and community support, so AMD’s ROCm and Intel’s stack require more troubleshooting and patience.
Why doesn’t Nvidia sell an affordable 24GB consumer GPU?
Currently its mainstream gaming cards top out at 16GB, while 24GB cards are positioned as professional/workstation products priced accordingly. Reports suggest a 24GB variant of the RTX 5080 was planned and then cancelled over the higher cost of denser memory chips, though Nvidia hasn’t confirmed this.
