Once you commit to hosting 70B class models, the choice stops being “what GPU” and becomes “what topology.” The two serious options on our dedicated GPU servers are a single RTX 6000 Pro with 96 GB or a pair of RTX 5090s running tensor parallel. They reach roughly the same VRAM envelope but behave very differently under load.
Contents
- What each topology actually looks like
- VRAM envelope and fragmentation
- Latency characteristics
- Throughput under batch load
- Cost and operational complexity
The Two Topologies
A single 6000 Pro gives you 96 GB of contiguous VRAM on one device. No sharding, no interconnect overhead, no tensor parallelism configuration. Two 5090s give you 64 GB total (32 + 32) and require you to split model layers across both cards, which means every forward pass crosses the PCIe bus between devices.
VRAM Envelope
A Llama 3 70B at INT4 needs roughly 38-42 GB for weights. At FP16 it wants 140 GB or more and will not fit cleanly on either setup without spillover. Where this gets interesting is the in-between:
| Model & Precision | Weights | 6000 Pro (96GB) | 2× 5090 (64GB) |
|---|---|---|---|
| Llama 3 70B INT4 | ~40 GB | Easy + huge KV cache | Fits, tight KV cache |
| Llama 3 70B INT8 | ~70 GB | Fits with room | Does not fit |
| Qwen 2.5 72B INT4 | ~42 GB | Easy | Fits tightly |
| Mixtral 8x22B INT4 | ~75 GB | Fits | Does not fit |
For full details on any of the above, see our Llama 3 70B VRAM requirements.
Latency
Single-card latency is noticeably lower because there is no cross-device synchronisation. On the 6000 Pro, a Llama 3 70B INT4 chat response starts within one to two seconds even under warm conditions. The dual-5090 setup adds 10-25% to time-to-first-token because tensor parallelism introduces all-reduce operations after every layer. If your product is a latency-sensitive chatbot, single-card wins.
Deploy 70B Models With Predictable Latency
We provision single-card 96GB servers with full root access and no shared tenancy surprises.
Browse GPU ServersThroughput Under Batch Load
The dual 5090 wins when you saturate the card with 16-64 concurrent requests. Two GPUs of compute, even with interconnect tax, process more tokens per second than one when batching kicks in. See vLLM continuous batching tuning for how to actually configure this. The crossover point is typically around 8 concurrent requests – below that the single card dominates, above that the pair pulls ahead.
Cost and Complexity
Two 5090s cost more than one 6000 Pro in most configurations and add operational complexity: NCCL tuning, tensor-parallel-aware serving stack, and double the failure surface. The 6000 Pro is boring in the best way – one device, one driver instance, one set of thermals.
If your workload is steady-state batch inference with high concurrency, the dual setup pays back. If it is mixed – some latency-sensitive chat, some batch – the 6000 Pro simplifies everything and our single 6000 Pro vs four 4060 Ti piece applies the same reasoning to smaller models.
See also our tensor vs pipeline parallelism guide if you are deciding which splitting strategy actually suits your model.
