RTX 5060 Ti 16GB for Local LLMs: Is It Good Enough?

Overview

The RTX 5060 Ti 16GB is a workable local LLM card for models up to about 14B parameters at 4-bit, but only the 16 GB version: NVIDIA lists the RTX 5060 Ti with two memory sizes, “16 GB / 8 GB GDDR7”, so check the memory size on the box before you buy. A 14B Q4_K_M model with a 4,096-token context lands near 10.8 to 11.8 GB by the formula below, which fits. A 32B Q4_K_M model lands near 22 GB, which does not. Tokens per second: unknown, because NVIDIA publishes none for this card and this page does not repeat third-party numbers.

Which RTX 5060 Ti has 16GB, and does an 8GB version exist?

Yes, an 8 GB version exists on NVIDIA’s own page. The RTX 5060 family page lists the 5060 Ti “Standard Memory Config” as “16 GB / 8 GB GDDR7” on a 128-bit memory interface, with 4608 CUDA cores, 180 W total graphics power and PCI Express Gen 5. The page gives one spec column for both sizes. Whether the two sizes differ in any other spec: unknown from that page. Memory bandwidth is not stated there, so it is unknown here.

For local LLMs the memory size is the whole decision. Every fit result below changes between 8 GB and 16 GB, so treat a listing that just says “RTX 5060 Ti” as unverified until it names the size.

Is the RTX 5060 Ti 16GB good for LLMs?

It is good for 7B to 14B models at 4-bit and for 7B at 8-bit, and not enough for 32B. Method: weights_GB = params x bits / 8 / 1e9, plus KV cache, plus a labelled 1 to 2 GB overhead assumption, as defined on methodology. Bits per weight are llama.cpp’s Q4_K_M 4.8944 and Q8_0 8.5008 from the quantize README. Parameter counts, layers and KV heads come from the Qwen2.5 model cards (7B, 14B, 32B) and each config.json; head dimension is hidden_size / query heads = 128 for all three, and the dtype is bfloat16 (2 bytes). KV bytes = 2 x layers x kv_heads x 128 x context x 2. All figures are decimal GB.

Longer context changes the answer. At 32,768 tokens, 14B Q4_K_M needs 8.9935 + 6.4425 = 15.4359 GB before overhead, or 16.44 to 17.44 GB with it, which no longer fits 16 GB. 7B Q4_K_M at 32,768 tokens is 4.6558 + 1.8790 = 6.5348 GB, or 7.53 to 8.53 GB with overhead: borderline on 8 GB. These are weights and KV only; the runtime’s own allocation behavior is not measured here. A 16 GB card marked “16 GB” is treated as 16.0 decimal GB; the usable amount is unknown. See vram-requirements for the formula by model size.

If a model does not fit, partial offload to system RAM works but is slower; speeds are unknown here.

RTX 5060 Ti 16GB or RX 9060 XT 16GB for local LLMs?

On paper they are the same memory class: both have 16 GB on a 128-bit interface. The AMD RX 9060 XT page lists “Max Memory Size 16 GB”, GDDR6, 128-bit, memory bandwidth “Up to 320 GB/s”, 32 compute units, typical board power 160W and a minimum PSU recommendation of 450W. It shows 16 GB as the maximum size; whether an 8 GB 9060 XT exists is not stated on that page, so it is unknown here.

The two cards fit the same models, because fit depends on capacity. They differ in software: NVIDIA cards use CUDA, and the AMD card depends on the AMD support of whatever runtime you choose; see amd-gpu-for-local-llm. Which is faster for a given model: unknown, as neither vendor publishes LLM throughput for these cards. Check current retailer price for both.

What about the RTX 4060 Ti 16GB?

It has the same 16 GB capacity, so the fit table above applies. NVIDIA’s RTX 4060 family page lists the 4060 Ti as “16 GB GDDR6 or 8 GB GDDR6” on a 128-bit interface with 4352 CUDA cores, and bandwidth is not stated there either. The same 8 GB trap applies. The 5060 Ti lists GDDR7 and 4608 CUDA cores; what that is worth for LLM speed is unknown.

What about two RTX 5060 Ti 16GB cards?

Two cards give 32 GB of total memory on paper, but no primary source read for this page says how a given runtime splits a model across them, so scaling is unknown here. Weights would still have to be split and each card needs its own power and slot. The comparison against a single larger card is on rtx-5090-vs-dual-rtx-5060-ti. For a wider view of 16 GB options, including cards above this one, see 16gb-vram-llm, budget-gpu-for-local-llm and gpu-vram-list.

Questions

Is the RTX 5060 Ti 16GB good for LLMs?

Yes for 7B and 14B models at 4-bit with a short context: 14B Q4_K_M at 4,096 tokens is estimated at 10.80 to 11.80 GB. No for 32B at 4-bit, which needs about 21.96 to 22.96 GB. Speed is unknown.

Does an 8GB RTX 5060 Ti exist?

Yes. NVIDIA’s RTX 5060 family page lists “16 GB / 8 GB GDDR7” for the 5060 Ti. Confirm the memory size before buying.

RTX 5060 Ti 16GB or RX 9060 XT 16GB for local LLMs?

Both list 16 GB on a 128-bit interface, so they fit the same models by capacity. The 9060 XT page states up to 320 GB/s; NVIDIA states no bandwidth for the 5060 Ti. Speed difference: unknown.

Can the RTX 5060 Ti 16GB run a 32B model?

Not at Q4_K_M in VRAM alone. Qwen2.5-32B weights at Q4_K_M are 19.8835 GB, and with KV cache and overhead the total is 21.96 to 22.96 GB, above 16 GB.

Is the RTX 4060 Ti 16GB the same for LLMs?

Capacity-wise yes: NVIDIA lists it with 16 GB GDDR6 or 8 GB GDDR6. The fit table applies. Speed comparison with the 5060 Ti: unknown.

Sources read on 2026-10-13

• NVIDIA RTX 5060 family: 16 GB / 8 GB GDDR7, 128-bit, 4608 CUDA cores, 180 W, power connector, PCIe Gen 5; bandwidth not stated.
• AMD Radeon RX 9060 XT: max 16 GB, GDDR6, 128-bit, up to 320 GB/s, 32 compute units, 160W, 450W PSU, 1x8-Pin. (WebFetch returned HTTP 503; page body fetched with curl.)
• NVIDIA RTX 4060 family: 4060 Ti 16 GB or 8 GB GDDR6, 128-bit, 4352 CUDA cores.
• llama.cpp quantize README: Q4_K_M 4.8944 and Q8_0 8.5008 bits per weight.
• Qwen2.5 7B, 14B, 32B model cards and config.json: parameters (7.61B, 14.7B, 32.5B), layers (28, 48, 64), KV heads (4, 8, 8), hidden size, bfloat16.

Related

• 16gb-vram-llm
• rtx-5090-vs-dual-rtx-5060-ti
• budget-gpu-for-local-llm
• best-gpu-for-local-llm
• amd-gpu-for-local-llm
• gpu-vram-list
• vram-requirements
• methodology