Short answer: no. And here’s why that’s actually fine. The RTX 4060 Ti has 16GB of VRAM. Llama 70B at the lowest usable quantization (Q2_K) needs about 25-28GB. The math simply doesn’t work on a single 16GB card.
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You own an RTX 4060 Ti (8GB or 16GB) and you’re wondering if you can run Llama 70B. Or you’re considering buying one and want to know its limits before spending money.
Why 16GB isn’t enough for 70B
| Quantization | Llama 70B model size | KV cache (4K ctx) | Total VRAM | Fits 16GB? |
|---|---|---|---|---|
| q2_K | 26GB | ~1GB | ~27GB | No |
| q3_K_M | 34GB | ~1GB | ~35GB | No |
| q4_K_M | 43GB | ~1GB | ~44GB | No |
| fp16 | 141GB | ~1GB | ~142GB | No |
Even at Q2_K — the most aggressive quantization where quality degrades significantly — you still need 26GB. The RTX 4060 Ti maxes out at 16GB. There’s no quantization level that makes this work without CPU offloading.
VRAM capacity memory bandwidth Specs are manufacturer figures. Bar lengths are scaled independently per metric.
What about CPU offloading?
Technically, you can offload layers to system RAM. Ollama and llama.cpp support this. But here’s what happens:
- Speed drops to 2-5 tok/s for offloaded layers (vs 35 tok/s fully on GPU)
- A 50/50 GPU/CPU split gives you ~5-8 tok/s — painfully slow for chat
- System RAM needs to be at least 32GB, ideally 64GB
- The experience is barely usable for interactive conversation
In our experience, CPU offloading for 70B models is only acceptable for batch processing where you don’t mind waiting.
What you SHOULD run on the RTX 4060 Ti
The 16GB version is excellent for models it can actually fit:
| Model | Quantization | VRAM used | Speed | Experience |
|---|---|---|---|---|
| Llama 3 8B | Q8_0 | ~9GB | ~35 tok/s | Excellent |
| Mistral 7B | Q8_0 | ~8GB | ~35 tok/s | Excellent |
| Llama 2 13B | Q4_K_M | ~8GB | ~20 tok/s | Good |
| Qwen 32B | Q3_K_M | ~15GB | ~12 tok/s | Tight but works |
The RTX 4060 Ti 16GB is genuinely great for 7B-13B models and can stretch to 34B with aggressive quantization. That’s its lane — and it’s a good lane.
Which GPU should you buy for 70B?
If you specifically need to run Llama 70B, here are your real options:
- Single card, tight budget? → Used RTX 3090 ($800) — still can’t run 70B alone but gets you 24GB
- Single card, good quality? → RTX 5090 ($4,900) — 32GB tops out at q3_K_S (31GB); q3_K_M is 34GB and will not fit. Expect degraded quality either way
- Good quality, any budget? → Dual RTX 3090 ($1,640 total) — 48GB combined handles Q4_K_M
- Skip the hassle? → Cloud GPU via RunPod. See our VRAM guide for the full breakdown.
Common mistakes to avoid
- Buying a 16GB card specifically for 70B models. It won’t work. If 70B is your goal, start at 24GB minimum.
- Assuming CPU offloading is “fine.” It’s functional but miserable for interactive use. 5 tok/s feels like watching paint dry.
- Ignoring better-fitting models. A well-prompted Qwen 32B at Q4 on your 16GB card often beats a barely-running 70B at Q2 on a larger GPU.
Final verdict
| Question | Answer |
|---|---|
| Can RTX 4060 Ti run 70B? | No — not enough VRAM |
| Best 70B-capable GPU? | Dual RTX 3090 or RTX 5090 |
| Best use for RTX 4060 Ti? | 7B-13B models, stretching to 34B |
The RTX 4060 Ti is a great GPU — just not for 70B models. Use it for what it’s good at (7B-13B at high quality) and save 70B for when you have 24GB+ of VRAM.
Common questions about the RTX 4060 Ti and 70B models
What GPU do you need to run Llama 70B?
Plan for 24GB of VRAM at an absolute minimum and realistically 48GB for decent quality. A single RTX 5090 (32GB) can run 70B with degraded quality, while dual RTX 3090s (roughly $1,640 used, 48GB combined) handle Q4_K_M and remain the usual budget path. If you only need 70B occasionally, cloud GPU rental avoids the hardware spend entirely.
Would three RTX 4060 Ti 16GB cards run Llama 70B at Q4?
On paper, three 16GB cards give you 48GB combined, which is enough VRAM for Q4_K_M. In practice, multi-GPU splitting adds communication overhead, and dual RTX 3090s reach the same 48GB with fewer cards, fewer PCIe slots, and a simpler build — which is why they remain the recommended budget route for 70B rather than stacking three mid-range cards.
Can the RTX 4060 Ti run Llama 70B with CPU offloading?
Technically yes, but it’s barely usable. Offloading layers to system RAM drops speed to roughly 2-5 tok/s, and even a 50/50 split only manages roughly 5-8 tok/s — painfully slow for interactive chat. You’d also need at least 32GB of system RAM. Offloading is only acceptable for batch processing where you don’t mind waiting.
Is the 8GB RTX 4060 Ti good for local LLMs in 2026?
The 8GB version is far more limited than the 16GB model. It handles 7B-class models like Mistral 7B at mid-level quantization, but it cannot comfortably fit the higher-quality Q8 versions or the 13B-34B models the 16GB card runs well. For local LLM work, the 16GB variant is worth the extra cost; the 8GB card is entry-level only.