NVIDIA A100 vs Dual RTX 3090 for Local LLMs in 2026

Two used RTX 3090s give you 48GB of VRAM for around $1,600. A used A100 80GB gives you one contiguous 80GB pool for closer to $10,000. For a home or small-office inference box, the dual 3090 wins on price and desktop practicality. The A100 earns its premium only under a narrow set of conditions, and this page names them instead of hand-waving “the A100 is faster.”

TL;DR verdict

Pick two RTX 3090s if you are building a local LLM rig at home or in a small office, you want the most VRAM per dollar, and your models fit in 48GB (which covers everything up to a 70B at 4-bit). This is the right call for most people reading this.

Pick a single A100 80GB if you need one contiguous 80GB pool that two cards cannot give you, you are running the box 24/7 as production infrastructure, you want ECC memory and MIG partitioning, and you already have a server chassis with the airflow to cool a passive card.

Skip the A100 40GB entirely. It costs more than two 3090s and gives you less usable VRAM.

The real question is not speed, it is fit

Most comparisons open with TFLOPS charts. That is the wrong starting point for a buying decision. Three practical constraints decide this before raw throughput ever matters.

Cooling and form factor. The A100 is passively cooled. It has no fans and expects the wall of high-pressure air a rack server pushes through it. Put it in a normal desktop case and it throttles or overheats fast. Two 3090s are actively cooled consumer cards that live happily in a big tower with good intake. If you do not own a server chassis, that fact alone can end the discussion.

VRAM pool shape. The A100 80GB is one pool. Two 3090s are two 24GB pools that you stitch together with tensor parallelism. A model that needs more than 24GB has to be sharded across both cards, which works well but adds a little overhead and some setup. A model that needs a single 60GB or 70GB allocation fits on the A100 and does not fit cleanly on the pair.

Cost per GB. This is the number that decides it for most buyers, and the A100 loses it badly. More on that below.

Spec comparison

Figures are for the PCIe variants, the ones you actually buy secondhand. Prices are used-market as of mid-2026 and move fast in the current memory shortage, so treat them as a snapshot, not a quote.

Spec	2x RTX 3090	A100 40GB PCIe	A100 80GB PCIe
Usable VRAM	48GB (2x 24 GDDR6X)	40GB HBM2	80GB HBM2e
VRAM pool	Split across 2 cards	Single pool	Single pool
Memory bandwidth	936 GB/s per card	1,555 GB/s	1,935 GB/s
FP16 tensor (dense)	~71 TFLOPS per card	~312 TFLOPS	~312 TFLOPS
Cooling	Active (fans)	Passive (server)	Passive (server)
Board power	350W per card (700W)	250W	300W
ECC memory	No	Yes	Yes
MIG partitioning	No	Yes	Yes
NVLink	Yes (3-slot bridge)	Yes	Yes
Architecture	Ampere (GA102)	Ampere (GA100)	Ampere (GA100)
Used price (mid-2026)	~$1,400 to $1,800 pair	~$4,000 to $6,000	~$8,000 to $12,000

Both sides are Ampere, so you get the same CUDA feature level, the same driver stack, and full support in vLLM, llama.cpp, TGI, and Ollama. Neither generation does FP8. This is not old versus new silicon. It is consumer packaging versus data-center packaging of the same era.

VRAM: 48GB split vs 80GB unified

For inference, VRAM capacity sets the ceiling on what you can run. Here is what each config holds at common quantization levels.

Model	Precision	Approx VRAM	2x 3090 (48GB)	A100 80GB
Llama 8B	FP16	~16GB	Fits (1 card)	Fits
Qwen 32B	Q4_K_M	~20GB	Fits (1 card)	Fits
Llama 70B	Q4_K_M	~40GB	Fits (sharded)	Fits
Mixtral 8x22B	Q4_K_M	~80GB	No	Fits (tight)
Llama 70B	FP16	~140GB	No	No

The split matters at the edges. A 70B at 4-bit fits on both, but on the pair it runs across two cards with tensor parallelism, so you pay a small sync tax and you set up the sharding once. Anything that needs a single allocation above 48GB (a large mixture-of-experts model at higher precision, or a 70B with a very long context window) fits the 80GB A100 and does not fit the pair at all.

If your workload is a stack of 7B to 34B models, the split never bites and the 48GB is pure win. If you specifically need to hold one very large model with lots of context in a single pool, that is the A100’s home turf.

Bandwidth beats TFLOPS for inference

Token generation is memory-bandwidth bound, not compute bound. Every token forces a read of the model weights out of VRAM, so how fast you can move bytes matters more than how many FLOPS the chip can do.

The A100 80GB moves 1,935 GB/s from a single pool. A 3090 moves 936 GB/s per card. When a model fits on one 3090, the A100 is roughly twice as fast at pushing weights, and that shows up in tokens per second. When a model is sharded across two 3090s with tensor parallelism, the cards work in parallel and effective bandwidth climbs, though inter-GPU communication eats some of the gain. An NVLink bridge (about 112 GB/s between the two 3090s) softens that penalty and is worth the small extra cost for multi-GPU inference.

The honest summary: on a single fitting model the A100 is clearly faster, on a sharded 70B the gap narrows to something most home users would not feel in a chat or coding loop. You are not choosing between fast and slow. You are choosing between fast and fast-enough at very different prices.

Cost per GB of VRAM

Strip away everything else and price the thing people actually buy: VRAM.

Config	Usable VRAM	Used price (mid-2026)	Cost per GB
2x RTX 3090	48GB	~$1,600	~$33
A100 40GB	40GB	~$5,000	~$125
A100 80GB	80GB	~$10,000	~$125

The dual 3090 is roughly four times cheaper per gigabyte. That is the whole argument in one row. For a personal or small-team rig where the goal is “run big open models without a cloud bill,” the 3090 pair is the efficient spend and it is not close.

When the A100 actually wins

The A100 is not overpriced. It is priced for a job the 3090 cannot do. That job looks like this:

1. You need one contiguous 80GB pool. A model or a context length that requires a single allocation above 48GB fits the A100 and cannot be split cleanly across two cards.
2. You run it 24/7 as production infrastructure. ECC memory catches bit flips that a consumer card silently passes through. For a service that has to stay correct for months, that reliability is the point.
3. You want to partition the GPU. MIG lets you slice one A100 into isolated instances, so several small models or several users each get a guaranteed share. Consumer 3090s cannot do this.
4. You already run a server chassis. If the passive card drops into airflow you already have, its cooling stops being a problem and becomes a non-issue.

If none of those four apply to you, you are paying data-center prices for features you will not use.

The middle option nobody mentions

There is a card that sits between these two and gets left out of the argument: the RTX 6000 Ada (or the older RTX A6000). It gives you 48GB in a single pool, it is actively cooled so it lives in a workstation, and it draws about 300W from one slot. You get the A100’s single-pool convenience and desktop-friendly cooling without needing a server.

The catch is price. A used A6000 runs several thousand dollars and a 6000 Ada more still, so it costs well above the 3090 pair while giving the same 48GB. You are paying for one-pool simplicity, lower power, and workstation drivers. For most home builders the two 3090s are still the better value, but if you want 48GB in one card without a server, this is the honest third answer.

Verdict by use-case

Your situation	Pick	Why
Home LLM rig, models up to 70B Q4	2x RTX 3090	Best VRAM per dollar, fits a desktop
Tight budget, want the most VRAM you can	2x RTX 3090	~$33 per GB, nothing beats it
24/7 production inference service	A100 80GB	ECC, reliability, single pool
Need one model above 48GB in a single pool	A100 80GB	The pair cannot hold it
Want 48GB in one card, no server	RTX 6000 Ada / A6000	Single pool, active cooling, workstation-friendly
Only ever running 7B to 34B models	2x RTX 3090 (or one)	You never hit the split, 48GB is plenty
No server chassis, considering an A100	2x RTX 3090	The passive card will cook in a desktop case

Related reading

• Local-LLM hardware buying hub: full GPU, VRAM, and deployment-tier breakdown
• RTX 5090 AI workstation build: the single-card build path
• Mac Studio M4 Ultra: unified-memory alternative for large models
• RTX 4060 Ti budget build: the entry point if 3090 pricing is out of reach

For a home local-LLM box, two RTX 3090s are the default and the A100 is the exception. Buy the A100 only when a single 80GB pool, ECC, MIG, or a passive server card is a real requirement, not a nice-to-have. For everyone else, the pair gives you 48GB of CUDA VRAM at roughly $33 per gigabyte, and that is the number the whole decision turns on.