Building out a small AI serving cluster on dedicated GPU hosting forces a real architectural choice. One RTX 6000 Pro with 96 GB gives you monolithic capacity. Four RTX 4060 Ti 16 GB cards give you 64 GB distributed across four independent devices. The total VRAM and the cost are in similar territory. Everything else diverges.
Outline
- What workload shapes fit each
- Capacity for large single models
- Parallelism across many small models
- Operational cost
- When to pick which
Workload Shape
A 6000 Pro is shaped for one big workload. 96 GB of contiguous memory hosts a 70B model with room to batch. Four 4060 Tis are shaped for four workloads – each card runs independently with its own 16 GB. Trying to host a 70B model across four 4060 Tis in tensor parallel is technically possible but the PCIe interconnect becomes the bottleneck and latency suffers.
One Big Model
| Model | 6000 Pro 96GB | 4× 4060 Ti |
|---|---|---|
| Llama 3 70B INT4 | Native fit | Tensor parallel with PCIe tax |
| Llama 3 70B INT8 | Fits | Does not fit (64 GB cap) |
| Qwen 2.5 72B INT4 | Comfortable | Tight, slow decode |
| Mixtral 8x22B INT4 | Fits | Does not fit |
The 6000 Pro wins outright when your use case is one very large model serving many users. See tensor vs pipeline parallelism for why multi-GPU is not free.
Many Small Models
If your product needs to serve four different models to four different customer segments (an LLM, an embedder, a reranker, and a vision model), four 4060 Tis are unbeatable. Each card is an independent serving unit. One model going down does not affect others. Memory is isolated per card. Scheduling is trivial. See our multi-model serving benchmark.
Monolith or Grid – We Build Both
One-card 96GB servers or multi-card racks with fixed monthly pricing from our UK datacenter.
Browse GPU ServersOperational Cost
Four 4060 Tis draw roughly 4 × 165 W = 660 W at peak; a 6000 Pro draws around 300 W. Power-wise the single card wins. Cooling complexity favours the single card too – one device, one thermal envelope, one monitoring target. Cabling and motherboard slot availability favour fewer cards. The grid approach has one operational upside: rolling maintenance. You can take one 4060 Ti offline for firmware or driver updates while the other three serve traffic. The 6000 Pro is an all-or-nothing target.
Which Topology Wins
Pick the 6000 Pro for a single large model with high concurrency (70B chat API, embedding-plus-rerank-plus-generation pipeline that benefits from shared memory). Pick four 4060 Tis when your workload is naturally sharded across multiple independent models or customer tenants, when you want physical isolation between workloads, or when you value rolling maintenance. The 6000 Pro is a better pure-performance box. The grid is a better multi-tenant operational box.
See also 6000 Pro vs dual 5090 for the middle-ground comparison.
