RTX 5090 vs Dual RTX 5060 Ti 16GB for Local LLMs

Overview

An RTX 5090 versus a dual RTX 5060 Ti 16GB build comes down to this: both reach 32 GB of GDDR7 on paper, but the 5090 holds a model in one memory pool while two 5060 Tis hold half each, so the dual build fits 32 GB-class models only if the split, the context and the per-card overhead all land under 16 GB per card. NVIDIA lists the RTX 5090 at 32 GB and 575 W, and the RTX 5060 Ti at 16 GB (or 8 GB) and 180 W. Tokens per second for either option is unknown: no vendor page or project README read for this page publishes a named-machine figure. Prices: check current retailer price. Method: methodology.

5090 vs dual 5060 Ti: what do the spec pages say?

The last column is addition, not a vendor claim. Summed cores and TOPS do not tell you speed, because a single model run cannot use two cards as one. Memory bandwidth is not on either page, so which option reads weights faster is unknown here. The 5060 Ti page also lists an 8 GB version, so confirm the box says 16 GB: rtx-5060-ti-16gb-llm.

Does 32GB across two cards behave like 32GB on one?

No. Neither card has NVLink per the pages above, so the cards talk over PCIe. The llama.cpp multi-GPU guide describes the modes:

• Layer split (the default): “Each GPU holds a contiguous slice of layers”, the KV cache for a layer stays on the GPU that holds it, and it “can tolerate slow interconnect speeds”. It “requires many tokens to scale well”.
• Tensor split (experimental): splits weights and KV across GPUs, is “much more bottlenecked by the GPU interconnect speed”, needs flash attention and a non-quantized KV cache, and does not support every architecture (the guide lists some MoE and hybrid architectures, such as Grok, MPT and OLMoE, as failing).

The CLI README shows the switch: --split-mode takes none, layer, row or tensor, and --tensor-split sets proportions such as 3,1. So two 16 GB cards behave like 32 GB for capacity only when the model divides cleanly, and the speed effect of layer split on one stream is unknown here. Lane width and PSU detail: multi-gpu-pcie-and-psu; general background: multi-gpu-llm.

What fits on one 32 GB card versus 2 x 16 GB?

The formulas are from methodology: weights_GB = params x bits / 8 / 1e9, KV bytes = 2 x layers x kv_heads x head_dim x context x bytes_per_value, plus an assumed 1 to 2 GB overhead (an assumption, not a measurement). Inputs: Qwen2.5-32B has 32.5B parameters on its model card, and its config.json gives 64 layers, 8 KV heads, hidden size 5120 over 40 attention heads (head dimension 128) and bfloat16. Q4_K_M is 4.8944 bits per weight and Q8_0 is 8.5008 in the llama.cpp quantize README, where they are measured on Llama-3.1-8B, so applying them to Qwen2.5-32B is an estimate.

Weights: 32.5e9 x 4.8944 / 8 / 1e9 = 19.8835 GB. KV: 2 x 64 x 8 x 128 x 2 = 262,144 bytes per token, so 1.0737 GB at 4,096 tokens and 8.5899 GB at 32,768.

The per-card figure assumes an even split of weights and KV and that each card pays its own overhead; both are assumptions, and an uneven split or a bigger overhead tips it over 16 GB. At Q8_0 the weights alone are 34.5345 GB (8.5008 bits), over 32 GB on either build. Qwen2.5-72B (72.7B parameters, per the figure recorded on vram-requirements) at Q4_K_M is 72.7e9 x 4.8944 / 8 / 1e9 = 44.4779 GB of weights, over 32 GB on both. So the extra capacity of a 5090 over two 16 GB cards shows up as headroom at long context, not as a bigger class of model. Compare other sizes in vram-requirements and 32gb-vram-llm.

Is dual RTX 5060 Ti 16GB better than a single RTX 3090?

On capacity, 32 GB beats 24 GB: NVIDIA lists the RTX 3090 at 24 GB GDDR6X, 384-bit, 350 W, 750 W required system power, PCIe Gen 4. The same 32B Q4_K_M case needs 21.9572 to 22.9572 GB at 4,096 context, which fits 24 GB with about 1 GB spare, but 29.4734 GB or more at 32,768 context does not. Speed and price: unknown and check current retailer price. The 3090 is the only card here that NVIDIA lists with NVLink support (“SLI-Ready”); the dual 3090 route is on dual-rtx-3090-llm.

2x 5070 Ti or 4x 5060 Ti: do the same rules apply?

Yes. The RTX 5070 Ti is listed at 16 GB GDDR7, 256-bit, 8960 CUDA cores, 1406 AI TOPS, 300 W, 750 W required system power, and no NVLink, so two are 32 GB across two pools at 2 x 300 = 600 W of card power. Four 5060 Ti are 64 GB across four pools at 4 x 180 = 720 W, which needs four slots and lanes; whether a given board supplies them is a board-manual question (unknown here). Weights spread out, but each added card adds PCIe traffic, power and heat, and no source read gives the speed result.

Which should you buy?

Choose the 5090 if you want one pool, a 575 W card, and the simplest runtime setup. Choose two 5060 Ti if 180 W cards (NVIDIA lists 600 W required system power for one) suit your case and you can live with layer split. The sources do not state why more builders do not go dual; the documented costs are no NVLink, a split model, experimental tensor mode, two power inputs and two slots. Mixed pairs are on mixed-gpus-for-llm; the 5090 on its own is on rtx-5090-llm.

Questions

5090 vs dual 5060 16G: why isn’t everyone going dual?

No primary source says. What the sources do document: no NVLink on either card, a model split across two memory pools, experimental tensor split with limits on architectures and KV type, and two power inputs and slots instead of one. Speed difference is unknown.

What would you do: 2x 5060 Ti, 2x 5070 Ti or a 5090?

Decide by total card power and pools. Two 5060 Ti is 360 W, two 5070 Ti is 600 W and one 5090 is 575 W, all by NVIDIA’s total graphics power figures, and all three reach 32 GB. The 5090 is the only one that is a single pool. Prices and speed: unknown.

Is dual RTX 5060 Ti 16GB better than a single RTX 3090?

It has more memory (32 GB against 24 GB) and Gen 5 against Gen 4, per NVIDIA. Qwen2.5-32B Q4_K_M at 4,096 context needs 21.9572 to 22.9572 GB, which fits a 3090 narrowly. Speed is unknown.

Does 32GB across two cards behave like 32GB on one?

For capacity, partly. With the default layer split each card holds a slice of layers and its KV, so a model must divide into two parts that each fit 16 GB with overhead. Speed scaling is unknown.

Sources read on 2026-10-13

• NVIDIA RTX 5090: 32 GB GDDR7, 512-bit, 21,760 cores, 3,352 AI TOPS, 575 W, 1,000 W required, connectors, PCIe Gen 5, NVLink no.
• NVIDIA RTX 5060 family: 5060 Ti 16 GB / 8 GB GDDR7, 128-bit, 4608 cores, 759 AI TOPS, 180 W, 600 W required, connector, PCIe Gen 5, NVLink no.
• NVIDIA RTX 5070 family: 5070 Ti specs above.
• NVIDIA RTX 3090: 24 GB GDDR6X, 384-bit, 350 W, 750 W, PCIe Gen 4, NVLink supported.
• llama.cpp multi-GPU guide: layer and tensor split behavior and limits.
• llama.cpp CLI README: --split-mode, --tensor-split.
• llama.cpp quantize README: bits per weight (Q4_K_M 4.8944, Q8_0 8.5008, measured on Llama-3.1-8B; also recorded on vram-requirements).
• Qwen2.5-32B card and config.json: 32.5B parameters, 64 layers, 8 KV heads, hidden 5120, 40 heads, bfloat16.

Related

• rtx-5060-ti-16gb-llm and rtx-5090-llm cover each card.
• multi-gpu-llm and multi-gpu-pcie-and-psu cover splitting, lanes and power.
• dual-rtx-3090-llm is the used-card route.
• 32gb-vram-llm and vram-requirements cover what fits.
• mixed-gpus-for-llm and methodology.