What quantization should I use for Llama-3.2-1B-Instruct on a NVIDIA GeForce RTX 4070 Ti SUPER?

Q8_0 is the highest-quality quant that grades B or better on this hardware with llama.cpp at 8K context; the per-quant table on this page grades all 4 available quants.

Can an NVIDIA GeForce RTX 4070 Ti SUPER run Llama-3.2-1B-Instruct?

Yes. Llama-3.2-1B-Instruct needs 2.01GB at Q8_0 with llama.cpp and 8K context; the NVIDIA GeForce RTX 4070 Ti SUPER has 16GB of VRAM (15.5GB usable) — grade A, with decode speed in the interactive tier.

NVIDIA GeForce RTX 4070 Ti SUPER · Llama-3.2-1B-Instruct · Q8_0

✅ Fits — grade A

Interactive

weights 1.32 GBKV cache 0.27 GBoverhead 0.42 GBpool 15.5 GBneeds 2.01 GB

assumes llama.cpp · batch 1 · 8K context · fp16 KV cache

Also runs with: Ollama (one-line install) · LM Studio (GUI) · KoboldCpp (GUI)

$799 MSRP · as of 2026-07-08

Also good for gaming — 4K-class (estimated from FP32 compute, not a game benchmark).

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Adjust assumptions — runtime, context, GPU count →

Every quant, graded

NVIDIA GeForce RTX 4070 Ti SUPER × Llama-3.2-1B-Instruct, llama.cpp, 8K context.

Quant	File size	Fit	Speed tier
Q8_0	1.3 GB	Fits · A	interactive
Q6_K	1.0 GB	Fits · A	interactive
Q5_K_M	0.9 GB	Fits · A	interactive
Q4_K_M	0.8 GB	Fits · A	interactive

Llama-3.2-1B-Instruct on similar hardware

More models on the NVIDIA GeForce RTX 4070 Ti SUPER

Verdict computed from published specs and measured GGUF file sizes — see how the math works.