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Run LLM Locally

Qwen2.5 7B Instruct on the RTX 5060

With 8 GB of VRAM, the RTX 5060 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 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

Qwen2.5 7B Instruct on RTX 5060 + 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

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GeForce RTX 5060

8 GB GDDR7 · 448 GB/s · 145 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 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?
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?
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)?
We estimate 63–86 tok/s (estimate, confidence low). Based on: memory bandwidth (448 GB/s) and model size.