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Run LLM Locally

Qwen2.5 7B Instruct on the RTX 5060 Ti 8 GB

With 8 GB of VRAM, the RTX 5060 Ti 8 GB runs Qwen2.5 7B Instruct at Q4_K_M entirely on the GPU with an 8K context (about 5.9 GB needed). At Q8_0 it does not fit in VRAM.

Qwen2.5 7B Instruct

Q4_K_M · 8K context

Excellent

The model needs about 5.9 GB and fits in the 7.7 GB available, with 1.8 GB to spare.

Your hardware
RTX 5060 Ti 8 GB · 64 GB RAM
Estimated memory
~5.9 GB estimate
VRAM (usable memory)
7.7 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
63–86 tok/s estimate
Largest context in fast memory
38K tokens
Confidence: low

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

Memory breakdown

~5.9 GB (5.5 GB–6.3 GB) needed · estimate

Fits entirely in VRAM · 1.8 GB left

  • Model weights4.4 GB
  • KV cache0.4 GB
  • Runtime overhead0.8 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

Qwen2.5 7B Instruct on RTX 5060 Ti 8 GB + 64 GB RAM · 8K context
Qwen2.5 7B Instruct Q8_08K~9.2 GB · 10–19 tok/s estimateCPU/GPU offload
Qwen2.5 7B Instruct Q6_K8K~7.4 GB · 49–66 tok/s estimateCompatible with limits
Qwen2.5 7B Instruct Q5_K_M8K~6.6 GB · 55–75 tok/s estimateCompatible
Qwen2.5 7B Instruct Q4_K_M8K~5.9 GB · 63–86 tok/s estimateExcellent
Qwen2.5 7B Instruct IQ4_XS8K~5.5 GB · 69–94 tok/s estimateExcellent
Qwen2.5 7B Instruct Q3_K_M8K~5.1 GB · 75–103 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 Qwen2.5 7B 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 Qwen2.5 7B Instruct?
The model needs about 5.9 GB and fits in the 7.7 GB available, with 1.8 GB to spare.
Which quantization should I use on the RTX 5060 Ti 8 GB?
Q6_K is the highest-precision quantization that fits entirely in VRAM at 8K context. ~7.4 GB / 7.7 GB.
What is the largest context on the RTX 5060 Ti 8 GB?
At Q4_K_M, about 38K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Qwen2.5 7B Instruct (RTX 5060 Ti 8 GB)?
We estimate 63–86 tok/s (estimate, confidence low). Based on: memory bandwidth (448 GB/s) and model size.