RTX PRO 6000 Blackwell vs A100

The RTX PRO 6000 is the better rental for anything that needs FP8, FP4 or more than 74 GiB, and the A100 for BF16 work that fits in 80 GB. The two cards trade places on almost every line of the spec sheet. The RTX PRO 6000 is a Blackwell card with 96 GB of GDDR7 and FP8 and FP4 Tensor Cores. The A100 is Ampere, with 80 GB of HBM2e, no FP8 at all, and faster memory: 2,039 GB/s on the SXM4 module, against 1,597 or 1,792 GB/s on the RTX PRO 6000 depending on its edition. On 2026-09-27 a 1x RTX PRO 6000 cost $2.39 an hour and a 1x A100 SXM4 $1.50.

RTX PRO 6000 and A100 specs side by side#

The RTX PRO 6000 leads on memory, Tensor Core math and FP32, and the A100 leads on bandwidth, FP64 and links between GPUs. The table uses the A100 SXM4, which QuantaCloud rents one at a time and in 8-GPU VMs, and notes the PCIe card where it differs.

The A100 figures come from NVIDIA's A100 datasheet, and the RTX PRO 6000's memory, bandwidth, FP32 and power from its Server Edition page and datasheet. The Tensor Core rows are the ones to read carefully. NVIDIA publishes dense rates for the RTX PRO 6000 only in its RTX PRO Blackwell architecture whitepaper, and only for the 600 W Workstation Edition and the 300 W Max-Q. The Server Edition page's round figures, 2 PFLOPS of FP8 and 1 PFLOP of BF16, match the whitepaper's sparse rates, and NVIDIA's workstation datasheets footnote the matching 4,000 AI TOPS as FP4 with sparsity, so none of them can be set against the A100's dense 312. The table uses the Workstation Edition's dense rates, the highest of the three editions. The Max-Q is rated at 438.9 dense BF16 TFLOPS and 877.9 FP8, and the Server Edition boosts to 2,430 MHz against the Workstation Edition's 2,617, which puts its dense rates about 7% below the table's (our calculation: 2,430 / 2,617 = 0.93).

In ratios (our calculations), the A100 SXM4 reads memory 1.14 to 1.28 times as fast (2,039 / 1,792 and 2,039 / 1,597). The RTX PRO 6000 has 1.4 to 1.6 times the A100's dense BF16 rate, from the Max-Q to the Workstation Edition (438.9 / 312 and 503.8 / 312), and on the Server Edition's figure 6.2 times its plain FP32 rate (120 / 19.5). The gap is widest for an FP8 checkpoint, which the A100 loads weight-only and multiplies at its BF16 rate: the RTX PRO 6000 multiplies the same file in FP8 at 2.8 to 3.2 times that rate, on the Max-Q's and the Workstation Edition's dense FP8 figures (877.9 / 312 and 1,007.6 / 312).

Hourly prices and the 1.59 bar#

The A100 is the cheaper hour by a wide margin: 37% less than the RTX PRO 6000 on 2026-09-27.

Prices checked 5 Oct 2026, 21:30 UTC

A 1x RTX PRO 6000 listed at $2.39 an hour that day, a 1x A100 SXM4 at $1.50 and the A100 PCIe at $1.475 per GPU-hour (today the console price, $1.49/GPU-hr and $1.48/GPU-hr). The RTX PRO 6000 costs less per job only when it finishes at least 1.59 times as fast as an A100 SXM4 (our calculation: 2.39 / 1.50 = 1.59). FP8 math clears that bar on every edition, at 2.8 to 3.2 times. BF16 math clears it only on the Workstation Edition, at 1.6 times: the Max-Q's 1.4 falls short, and so does a Server Edition at about 1.5 (our calculation: 503.8 x 0.93 / 312 = 1.50). Single-stream generation misses the bar on every edition, because there the A100 is the faster card. The RTX PRO 6000 came in 1, 2 and 4-GPU VMs in us-east-1 and us-midwest-4 that day, the A100 SXM4 in 1 and 8-GPU VMs and the A100 PCIe in 2 and 4-GPU VMs, all listed on the RTX PRO 6000 page and the A100 page. The pricing page covers how each hour is billed.

Rent the RTX PRO 6000 for FP8, FP4 and 74 to 88 GiB#

Memory near and past 74 GiB comes first. Qwen3-32B in BF16 with one 32k-token sequence needs 69.0 GiB, just inside the A100's 73.6 GiB working budget with 4.6 GiB to spare, and fits the RTX PRO 6000's 87.9 GiB with about 19 GiB to spare. 16-bit LoRA on a 30 to 34B model, 64 to 80 GB in Axolotl's table, is tight on the A100 and fits the RTX PRO 6000. The per-channel FP8 build of Llama-3.3-70B, a 72.7 GB checkpoint, shows both of this card's advantages at once. On an A100 it leaves about 5.9 GiB for KV cache, about 19,000 tokens, and runs weight-only. On the RTX PRO 6000 it leaves about 20.3 GiB, roughly 66,000 tokens of BF16 KV cache, and runs with FP8 math (our calculation: 87.95 - 67.68, at 320 KiB per token).

A busy FP8 or FP4 endpoint is the second case. Under load, serving time goes to batched matrix math, and with a per-channel FP8 checkpoint such as RedHatAI/Qwen3-32B-FP8-dynamic (34.3 GB) the RTX PRO 6000 does that math in FP8, at 2.8 to 3.2 times the dense rate the A100 gets from the same file, depending on the edition. NVFP4 checkpoints widen the gap: vLLM runs them natively on Blackwell and falls back to weight-only 4-bit kernels on the A100.

Image and video generation is the third. ComfyUI computes in FP8 only on compute capability 8.9 and newer, and in NVFP4 only on 10 and newer with a cu130 PyTorch build, so on the A100 the FP8 files save memory but the math runs in 16-bit. The RTX PRO 6000 holds FLUX.2 [dev]'s fp8 transformer and fp8 text encoder together, 35.5 GB and 18.0 GB, with FP8 math, and computes FLUX.1 [dev] in FP4 from BFL's 9.2 GB NVFP4 file. It also has four NVENC video encoders, where NVIDIA's H100 whitepaper notes that the A100 has none, and for code that does not run on Tensor Cores, 6.2 times the A100's FP32 rate on the Server Edition's figure. FLUX in ComfyUI covers the files and their licences.

Rent the A100 for BF16 inference, eight GPUs and FP64#

BF16 generation that fits in 80 GB is where the A100 wins outright. NVIDIA describes token-by-token generation as memory-bound, and the A100 SXM4 reads memory 1.14 to 1.28 times as fast as the RTX PRO 6000 for 37% less per hour, so it is both the faster and the cheaper stream. The AWQ build of Llama-3.3-70B (39.8 GB) leaves about 36.6 GiB for KV cache on an A100, roughly 120,000 tokens. gpt-oss-120b fits with room for 2.8 full-length sequences, and vLLM runs it on the A100 through its Marlin MXFP4 kernels. For one user, a RAG pipeline or a queue of long generations, that is enough memory.

Eight GPUs in one VM is the A100's second case. On 2026-09-27 the 8x A100 SXM4 VM had 640 GB of GPU memory for $11.92 an hour, while the largest RTX PRO 6000 VM had four cards and 384 GB for $9.53, with no NVLink between them. QuantaCloud's API flags the A100 SXM4 GPUs as NVLink-connected. On the HGX A100 board that link runs at 600 GB/s, against 64 GB/s for PCIe Gen4, and it decides how fast a sharded fine-tune exchanges gradients. A full fine-tune of Qwen3-8B, 131 GB of model states, fits either VM with ZeRO-3: 16.4 GB per GPU across eight A100s or 32.8 GB across four RTX PRO 6000s, plus activations (our calculation). The A100s exchange those gradients over NVLink if nvidia-smi topo -m confirms the links, while the RTX PRO 6000s always go over PCIe.

Double precision and older software are the third. The A100 runs FP64 at 9.7 TFLOPS, 19.5 on Tensor Cores, and NVIDIA lists no FP64 figure for the RTX PRO 6000. Every current PyTorch build includes Ampere kernels, while Blackwell needs builds for CUDA 12.8 or newer, so a container that runs on the A100 today may need rebuilding before it runs on the RTX PRO 6000. The FAQ below has the check.

What fits in 80 GB and in 96 GB#

Two things separate these columns: 14.3 GiB of working budget and the math each card uses once the model is loaded. The budgets are 73.6 GiB on the A100 and 87.9 GiB on the RTX PRO 6000, at vLLM's default of reserving 92% of GPU memory (our calculation from the 81,920 and 97,887 MiB that nvidia-smi reports).

The KV cache figures assume BF16 at 320 KiB per token for Llama-3.3-70B, before activations. The VRAM guide walks through the arithmetic for other models, and the fine-tuning VRAM guide covers LoRA and QLoRA.

FAQ#

Does the RTX PRO 6000's edition change the verdict?

For BF16 math, yes. At 1.59 times the A100's hourly price, the RTX PRO 6000 needs 1.59 times its speed to cost less per job, and only the Workstation Edition's dense BF16 rate gets there, at 1.6 times. The Max-Q's gives 1.4 and a Server Edition's about 1.5. For FP8 math the edition does not change the answer, and for single-stream generation the A100 leads on every edition: 1.14 times the bandwidth of the Workstation and Max-Q editions, 1.28 times the Server Edition's. The catalog lists the card only as RTX PRO 6000 Blackwell, and nvidia-smi -q on the VM names the edition.

Which is better for multi-GPU training?

The 8x A100 SXM4, once its links check out. NVIDIA lists NVLink as not supported on the RTX PRO 6000, so the GPUs in a 2x or 4x VM talk over PCIe, and for serving across them vLLM recommends pipeline parallelism over tensor parallelism. QuantaCloud's API flags the A100 SXM4 GPUs in the 8x VM as NVLink-connected, and NV entries between them in nvidia-smi topo -m confirm it.

Will my A100 containers run on the RTX PRO 6000?

Only if they were built for CUDA 12.8 or newer. PyTorch builds for CUDA 12.6 and older have no Blackwell kernels and fail with "no kernel image is available for execution on the device". Rebuild on a cu128, cu129 or cu130 PyTorch that matches the driver (cu130 needs driver 580 or newer), then check that torch.cuda.get_arch_list() includes sm_120. The RTX PRO 6000 page has the driver table.

What happens to my checkpoints when I stop?

They are deleted with the VM's local disk, which on 2026-09-27 was 5,000 GB on the 8x A100 SXM4 and 2,900 GB on the 4x RTX PRO 6000. Stopping terminates the instance, and there are no volumes or snapshots, so on a long fine-tune copy checkpoints off as you go. The pricing page explains how the unused seconds of the current hour are refunded.

My rule for this pair is to settle the precision before the size. If the model runs in BF16 and fits in about 74 GiB with its KV cache and batch, rent the A100: it reads memory faster and cost 37% less per hour on 2026-09-27. If the job needs FP8 or FP4 math, 74 to 88 GiB, or ComfyUI's FP8 and NVFP4 paths, rent the RTX PRO 6000. For eight GPUs training together, start on the 8x A100 SXM4 and check its links. RTX PRO 6000 vs H100 covers the Hopper card at a similar hourly price, A100 vs H200 covers the A100's upgrade path, and L40S vs A100 covers the 48 GB card with FP8.