RTX PRO 6000 vs A100 for AI work
The A100 was NVIDIA's main data-centre GPU from 2020 and is still widely rented. The RTX PRO 6000 Blackwell is five years newer, with more memory and the low-precision formats that modern inference relies on.
| If | Choose | Why |
|---|---|---|
| Your code is built and tested on A100s, your models fit in 80 GB, or you need NVLink between two cards | A100, a median of $1.88 per GPU-hour on demand across 48 providers | Faster HBM2e memory and a mature, well-known platform |
| You serve or fine-tune current LLMs and want FP8 or FP4 and more memory per card | RTX PRO 6000, rent our 8-GPU server from $1.70 per GPU-hour | 96 GB per card and native FP8 and FP4 |
The differences that matter for AI
| A100 80GB (PCIe / SXM) | RTX PRO 6000 Server Edition | |
|---|---|---|
| Architecture | Ampere (2020) | Blackwell (2025) |
| GPU memory | 80 GB HBM2e | 96 GB GDDR7 with ECC |
| Memory bandwidth | 1,935 / 2,039 GB/s | 1,597 GB/s |
| Low-precision formats | FP16, BF16, INT8 | adds FP8 and FP4 |
| GPU-to-GPU link | NVLink, 600 GB/s | PCIe Gen 5 |
| MIG instances | up to 7 | up to 4 |
| Maximum power | 300 W / 400 W | up to 600 W, configurable |
Formats: FP8 and FP4 change what fits
The A100 runs models in FP16 or BF16, or in INT8. NVIDIA does not list FP8 or FP4 for it. The RTX PRO 6000 runs both natively. A 70B model needs about 140 GB in FP16, about 70 GB in FP8 and about 35 GB in FP4.
In FP16 the same 70B model needs at least two A100s or two RTX PRO 6000 cards. In FP8 it fits on one RTX PRO 6000 with room for the context cache, while on one A100 80GB almost nothing is left for the cache. Most current inference engines, such as vLLM and SGLang, can serve FP8 and FP4 models, which is where the newer card gains the most.
Memory speed: the A100 is faster per GPU
The A100's HBM2e memory moves 1,935 to 2,039 GB/s, more than the 1,597 GB/s of the RTX PRO 6000 Server Edition. When both cards run the same model in the same format, a single conversation can generate tokens faster on the A100.
The balance changes when the RTX PRO 6000 runs the model in FP8 or FP4 and the A100 cannot. Smaller weights mean less data to read for every token, and the extra 16 GB per card leave more room for long contexts and larger batches.
Several GPUs: NVLink favours the A100
A100 cards can be linked with NVLink at 600 GB/s, so a model split across two or more of them exchanges data quickly. The RTX PRO 6000 talks to its neighbours over PCIe Gen 5. For inference we therefore recommend running each model on one, two or four RTX PRO 6000 cards and adding copies, instead of splitting one model across all eight. The same logic is explained in our RTX PRO 6000 vs H100 comparison.
Renting either one
A100 capacity is easy to find by the hour; the price tracker getdeploying listed it from $0.54 per GPU-hour, with a median of $1.88 across 48 providers on 8 October 2026. Our dedicated GPU server is a whole machine with eight RTX PRO 6000 Server Edition cards and 768 GB of GPU memory, rented by the month from $1.70 per GPU-hour with power, cooling and internet included. Published monthly offers for this card in Europe are compared in RTX PRO 6000 servers in Europe.
Questions
Is the A100 still a good choice in 2026? For code that is already tuned for it and for models that run well in FP16 or BF16, yes. For new inference work that can use FP8 or FP4, a Blackwell card gets more out of the same memory.
Can the RTX PRO 6000 be split like an A100 with MIG? Yes, into up to four isolated instances per card. The A100 supports up to seven smaller ones.
Does the RTX PRO 6000 have NVLink? NVIDIA does not list NVLink for the Server Edition; the cards communicate over PCIe Gen 5.
Sources
- NVIDIA: A100 Tensor Core GPU, specifications for the 80 GB PCIe and SXM models.
- NVIDIA: RTX PRO 6000 Blackwell Server Edition.
- getdeploying: A100 rental prices, updated 8 October 2026.