An interesting matchup on our dedicated hosting: the flagship RTX 6000 Pro against a pair of RTX 3090s. You get 96 GB versus 48 GB total, Blackwell versus Ampere, and one well-understood topology versus a multi-GPU setup that can move faster than you expect once tuned.
What’s Ahead
Specs
| Spec | RTX 6000 Pro | 2× RTX 3090 |
|---|---|---|
| VRAM total | 96 GB | 48 GB (24 + 24) |
| Bandwidth | ~1,800 GB/s | ~936 GB/s per card |
| FP8 support | Yes | No |
| Interconnect | N/A (single device) | PCIe 4.0 between cards |
| Power total | ~300 W | ~700 W combined |
Model Fit
Both setups comfortably host Llama 3 70B at INT4. Only the 6000 Pro holds 70B at INT8 or higher. The paired 3090s cap at ~48 GB total so a 70B at INT8 is out of reach. Where the 3090 pair shines is on models in the 30-40 GB class: two models loaded simultaneously on separate cards, or a 32B INT4 with room to spare on each. See Llama 70B INT4 VRAM.
Throughput
| Workload | 6000 Pro | 2× 3090 tensor-parallel |
|---|---|---|
| Llama 3 70B INT4, batch 1 | ~35 t/s | ~28 t/s |
| Llama 3 70B INT4, batch 16 | ~380 t/s aggregate | ~420 t/s aggregate |
| Two independent 13B models | Sequential on one card | Parallel, one per card |
| Qwen 2.5 32B INT8 | Easy fit, fast decode | Split across cards, slower per token |
At batch 1 the single 6000 Pro wins on latency. Under saturation the twin 3090s narrow and sometimes pass it because you effectively have two decoders running in parallel. Our data vs tensor parallel guide covers when each strategy wins.
Serve 70B Models Your Way
One big GPU or two smaller ones in the same server – we provision both topologies.
Browse GPU ServersCost per Token
Two 3090s typically cost less per month than a single 6000 Pro. On a fixed monthly price, if both setups reach your throughput target, the 3090 pair is cheaper. The catch is the ceiling – 3090s cannot reach models the 6000 Pro hosts comfortably, and FP8 kernels are unavailable. Break-even is typically around 30-40 concurrent users for 70B INT4; below that the 6000 Pro wins on latency, above that the pair wins on throughput-per-pound.
Which to Choose
If you are running a low-latency chat product – user sends message, expects response in two seconds – the single 6000 Pro is the right tool. If you are running background batch inference, bulk document summarisation, or serving many async API consumers, the twin 3090 setup is more cost-effective. For multi-model serving (two different models, two different workloads), two 3090s give you physical separation and avoid one model starving another.
Compare to 6000 Pro vs dual 5090 if you want modern silicon instead of Ampere, or one 6000 Pro vs four 4060 Ti for a denser grid-style topology.
