RTX 5060 Ti 16GB for Llama 3 70B INT4 – Does It Fit?

A common question when evaluating the RTX 5060 Ti 16GB: can it run Llama 3 70B? The short answer: not really for production. On our dedicated hosting there are better cards for 70B. Here is what happens if you try – and what to do instead.

Contents

• VRAM math
• The tight-fit options
• CPU/disk offload
• Performance
• Better card choices

VRAM Math

No pure-GPU configuration fits Llama 3 70B on 16 GB with usable quality plus KV cache headroom.

Tight Options

People do attempt these, with mixed results:

• Very aggressive GGUF (Q2_K, IQ2_XS): weights ~18-20 GB. Quality noticeably degrades. Still does not fit 16 GB alongside KV cache.
• 2-bit SOTA quants via ExLlama: may fit at single-user context but quality impact is substantial.

For production you need to pick between these and something that actually fits.

CPU/Disk Offload

llama.cpp supports GPU+CPU layer offload. With -ngl 16 (16 of 80 layers on GPU) you can run a 70B Q4 model, shuttling most layers between CPU RAM and GPU per forward pass.

Expected throughput: 1-3 tokens/sec. See CPU offload strategy guide. Works for occasional batch jobs; not production viable.

Performance

Summary of Llama 3 70B attempts on 5060 Ti:

• IQ2_XS barely-fits pure GPU: single-user 2-4 t/s, quality degraded
• Q4 with CPU offload: 1-3 t/s, quality preserved but slow
• Practical production serving: not feasible

Better Options

For 70B models in production:

• RTX 5090 32GB – INT4 fits comfortably with 35+ t/s at batch 1
• RTX 6000 Pro 96GB – FP8 native, 40+ t/s, high concurrency
• Two 5060 Ti in tensor parallel: 32 GB aggregate, INT4 fits but interconnect slows decode

For 70B workloads, step up. The 5060 Ti 16GB shines at 7-14B class where it is genuinely the default. See Llama 3.3 70B on 6000 Pro.

See also: max model size, upgrade to 5090.