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Run LLM Locally

Llama 3.2 1B Instruct on the RTX 4060 Ti 16 GB

With 16 GB of VRAM, the RTX 4060 Ti 16 GB runs Llama 3.2 1B Instruct at Q4_K_M entirely on the GPU with an 8K context (about 2 GB needed). At Q8_0 it fits entirely.

Llama 3.2 1B Instruct

Q4_K_M · 8K context

Excellent

The model needs about 2 GB and fits in the 15.5 GB available, with 13.5 GB to spare.

Your hardware
RTX 4060 Ti 16 GB · 64 GB RAM
Estimated memory
~2 GB estimate
VRAM (usable memory)
15.5 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
193–275 tok/s estimate
Largest context in fast memory
128K tokens
Confidence: low

Based on: memory bandwidth (288 GB/s) and model size

Memory breakdown

~2 GB (1.7 GB–2.3 GB) needed · estimate

Fits entirely in VRAM · 13.5 GB left

  • Model weights0.8 GB
  • KV cache0.3 GB
  • Runtime overhead0.7 GB
  • Safety margin0.3 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (CUDA) · flash attention on · weights = real file size

EstimateData updated 24 days agoSource: Run LLM Locally engine, Hugging Face config.json + model card

What makes this estimate less reliable

  • no benchmark for this hardware and model yet

Other quantizations on the RTX 4060 Ti 16 GB

Llama 3.2 1B Instruct on RTX 4060 Ti 16 GB + 64 GB RAM · 8K context
Llama 3.2 1B Instruct Q8_08K~2.5 GB · 133–184 tok/s estimateExcellent
Llama 3.2 1B Instruct Q6_K8K~2.2 GB · 163–228 tok/s estimateExcellent
Llama 3.2 1B Instruct Q5_K_M8K~2.1 GB · 177–250 tok/s estimateExcellent
Llama 3.2 1B Instruct Q4_K_M8K~2 GB · 193–275 tok/s estimateExcellent
Llama 3.2 1B Instruct IQ4_XS8K~2 GB · 204–294 tok/s estimateExcellent
Llama 3.2 1B Instruct Q3_K_M8K~1.9 GB · 214–311 tok/s estimateExcellent

Alternatives

Where to buy a RTX 4060 Ti 16 GB

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GeForce RTX 4060 Ti 16 GB

16 GB GDDR6 · 288 GB/s · 165 W

ExcellentRuns Llama 3.2 1B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RTX 4060 Ti 16 GB run Llama 3.2 1B Instruct?
The model needs about 2 GB and fits in the 15.5 GB available, with 13.5 GB to spare.
Which quantization should I use on the RTX 4060 Ti 16 GB?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~2.5 GB / 15.5 GB.
What is the largest context on the RTX 4060 Ti 16 GB?
At Q4_K_M, about 128K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Llama 3.2 1B Instruct (RTX 4060 Ti 16 GB)?
We estimate 193–275 tok/s (estimate, confidence low). Based on: memory bandwidth (288 GB/s) and model size.