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Run LLM Locally

Llama 3.2 3B Instruct on the RTX 5060 Ti 8 GB

With 8 GB of VRAM, the RTX 5060 Ti 8 GB runs Llama 3.2 3B Instruct at Q4_K_M entirely on the GPU with an 8K context (about 3.8 GB needed). At Q8_0 it fits entirely.

Llama 3.2 3B Instruct

Q4_K_M · 8K context

Excellent

The model needs about 3.8 GB and fits in the 7.7 GB available, with 3.9 GB to spare.

Your hardware
RTX 5060 Ti 8 GB · 64 GB RAM
Estimated memory
~3.8 GB estimate
VRAM (usable memory)
7.7 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
113–162 tok/s estimate
Largest context in fast memory
42K tokens
Confidence: low

Based on: memory bandwidth (448 GB/s) and model size

Memory breakdown

~3.8 GB (3.5 GB–4.2 GB) needed · estimate

Fits entirely in VRAM · 3.9 GB left

  • Model weights1.9 GB
  • KV cache0.9 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 5060 Ti 8 GB

Llama 3.2 3B Instruct on RTX 5060 Ti 8 GB + 64 GB RAM · 8K context
Llama 3.2 3B Instruct Q8_08K~5.1 GB · 77–107 tok/s estimateExcellent
Llama 3.2 3B Instruct Q6_K8K~4.4 GB · 94–132 tok/s estimateExcellent
Llama 3.2 3B Instruct Q5_K_M8K~4.1 GB · 103–146 tok/s estimateExcellent
Llama 3.2 3B Instruct Q4_K_M8K~3.8 GB · 113–162 tok/s estimateExcellent
Llama 3.2 3B Instruct IQ4_XS8K~3.6 GB · 120–174 tok/s estimateExcellent
Llama 3.2 3B Instruct Q3_K_M8K~3.5 GB · 126–184 tok/s estimateExcellent

Alternatives

Where to buy a RTX 5060 Ti 8 GB

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GeForce RTX 5060 Ti 8 GB

8 GB GDDR7 · 448 GB/s · 180 W

ExcellentRuns Llama 3.2 3B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RTX 5060 Ti 8 GB run Llama 3.2 3B Instruct?
The model needs about 3.8 GB and fits in the 7.7 GB available, with 3.9 GB to spare.
Which quantization should I use on the RTX 5060 Ti 8 GB?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~5.1 GB / 7.7 GB.
What is the largest context on the RTX 5060 Ti 8 GB?
At Q4_K_M, about 42K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Llama 3.2 3B Instruct (RTX 5060 Ti 8 GB)?
We estimate 113–162 tok/s (estimate, confidence low). Based on: memory bandwidth (448 GB/s) and model size.