RTX PRO 6000 vs H100: more memory or more bandwidth?

The RTX PRO 6000 vs H100 comparison is unusual: the newer card costs less and holds more, while the older one has more than twice the memory bandwidth. NVIDIA's RTX PRO 6000 Blackwell Server Edition carries 96 GB of GDDR7 at about 1.6 TB/s. The H100 SXM carries 80 GB of HBM3 at 3.35 TB/s and connects to its neighbors with NVLink. We rent the RTX PRO 6000 at $1,390 a month and the H100 at $2,190.

RTX PRO 6000 vs H100: the short answer

For inference on a single GPU, especially 70B-class models in FP8 or 4-bit, the RTX PRO 6000 is usually the better buy: more room for weights and KV cache for $800 a month less. For training, full fine-tuning and anything split across several GPUs, choose the H100. Its HBM3, NVLink and Hopper design were built for that work, and the RTX PRO 6000 has no NVLink.

Memory: what 96 GB buys for 70B-class inference

A 70B model takes roughly 70 GB in FP8 and 35 GB in a 4-bit format, so both fit on both cards. The difference is what remains for the KV cache, which grows with every token of context and every concurrent request.

With grouped-query attention (80 layers and 8 KV heads, as in Llama 3 70B), an FP8 KV cache takes about 0.16 MB per token. After FP8 weights, the H100's 10 GB holds roughly 60,000 tokens of context across all requests, and runtime overhead claims part of that. The RTX PRO 6000's 26 GB holds roughly 160,000: enough for one full 128k-token context or many shorter conversations. With 4-bit weights both cards have room, and the gap matters less.

For a 70B model in BF16, neither card is enough alone. You need two NVLink-connected GPUs, for example in our 8-GPU HGX H100 server, or a single NVIDIA B200 with 180 GB. Our guide to the best GPU for LLM inference covers the wider range.

Bandwidth and compute: where the H100 pulls ahead

NVIDIA lists the H100's HBM3 at 3.35 TB/s and the RTX PRO 6000's GDDR7 at about 1.6 TB/s. Token generation streams the weights and KV cache from memory at every step, so when a model fits on both cards, the H100 reads it at about 2.1 times the rate. For latency-sensitive serving of a model that fits in 80 GB, that is a real advantage.

Vendor compute figures point the same way. NVIDIA lists the H100 at 1,979 TFLOPS of dense FP8, 3,958 with sparsity, and quotes 2 PFLOPS of FP8 with sparsity for the RTX PRO 6000. On the sparse figures, the H100 is about twice as high. Large-batch serving and prefill on long prompts lean on compute, so this affects throughput as well as latency.

The RTX PRO 6000 answers with FP4. Blackwell supports FP4 in its tensor cores, and NVIDIA quotes 4 PFLOPS of FP4 with sparsity for the card, about the same as the H100's sparse FP8 figure. Hopper's lowest-precision floating-point format is FP8; on the H100, 4-bit formats such as AWQ or GPTQ reduce memory use, but the math runs at higher precision. If your model holds its accuracy in FP4, much of the compute gap closes on paper. Our FP8 and FP4 explainer covers the accuracy side.

NVLink, multi-GPU scaling and training

This is the H100's home ground. In our HGX H100 server, each GPU has 900 GB/s of NVLink 4 bandwidth through NVLink switches. Tensor parallelism, FSDP and other sharded training methods exchange gradients, parameters and activations constantly, and that traffic stays on NVLink. The RTX PRO 6000 has no NVLink: GPUs in our 8× RTX PRO 6000 server communicate over PCIe Gen5, at a fraction of that bandwidth.

Training memory adds up quickly. Full fine-tuning with Adam needs roughly 16–20 bytes per parameter before activations: about 112–140 GB for a 7B model and well over 1 TB for a 70B model. That is multi-GPU work, and it belongs on NVLink-connected GPUs such as the 8× H100. Adapter methods need far less. QLoRA on a 70B model, about 35 GB of 4-bit weights plus adapters and activations, fits on a single card of either type, and the RTX PRO 6000's extra 16 GB buys longer sequences or larger batches. Our fine-tuning page goes through the options.

Across servers the gap widens. The HGX H100 has 8× 400G ConnectX-7 network adapters, one per GPU; the 8× RTX PRO 6000 server has 2× 100 GbE. For multi-node work, see LLM training: the H100 platform is the one built for it.

Cost per month and value

A single RTX PRO 6000 is $1,390 a month, about $1.90 per GPU-hour; a single H100 is $2,190, about $3.00. Per gigabyte of GPU memory, that is about $14.48 against $27.38 a month: roughly half the price per GB on the RTX PRO 6000.

At server scale, the 8× RTX PRO 6000 costs $10,490 a month for 768 GB of GPU memory. The HGX H100 costs $16,900 for 640 GB, with NVLink and 3.2 Tb/s of networking. If you serve many independent model replicas, the RTX PRO 6000 server holds more for $6,410 a month less. If you train one model across all eight GPUs, the H100's interconnect is what you are paying for. Compare terms in the GPU cost calculator.

A worked example: serving a 70B model in FP8. On the 8× RTX PRO 6000, each GPU holds its own copy with about 26 GB left for KV cache, so you run eight independent replicas with no traffic between GPUs. On the HGX H100, one copy per GPU leaves only about 10 GB for KV cache, so you would more likely pair GPUs over NVLink and run four replicas, each with about 90 GB of headroom.

How to decide

1. One GPU, a 70B-class model in FP8 or 4-bit, many users or long contexts: RTX PRO 6000.
2. One GPU, a model that fits in 80 GB with headroom, lowest latency per request: H100.
3. Training or full fine-tuning across GPUs: 8× H100 with NVLink.
4. Many independent replicas, rendering or generative media: 8× RTX PRO 6000.

If you want both capacity and bandwidth on one GPU, look one step up: the H200 has 141 GB of HBM3e at 4.8 TB/s for $3,090 a month; see H100 vs H200.

Both run CUDA, and our Ubuntu images come with NVIDIA drivers, CUDA, Docker and the NVIDIA Container Toolkit preinstalled. Moving between them is a matter of sizing rather than porting, as long as your builds support Blackwell, which needs CUDA 12.8 or later.