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Run LLM Locally

Qwen2.5 7B Instruct on the RTX 2080 Ti

With 11 GB of VRAM, the RTX 2080 Ti 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 fits entirely.

Qwen2.5 7B Instruct

Q4_K_M · 8K context

Excellent

The model needs about 5.9 GB and fits in the 10.7 GB available, with 4.8 GB to spare.

Your hardware
RTX 2080 Ti 11 GB · 64 GB RAM
Estimated memory
~5.9 GB estimate
VRAM (usable memory)
10.7 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
83–116 tok/s estimate
Largest context in fast memory
91K tokens
Confidence: low

Based on: memory bandwidth (616 GB/s) and model size

Memory breakdown

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

Fits entirely in VRAM · 4.8 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 2080 Ti

Qwen2.5 7B Instruct on RTX 2080 Ti + 64 GB RAM · 8K context
Qwen2.5 7B Instruct Q8_08K~9.2 GB · 52–70 tok/s estimateCompatible
Qwen2.5 7B Instruct Q6_K8K~7.4 GB · 65–89 tok/s estimateExcellent
Qwen2.5 7B Instruct Q5_K_M8K~6.6 GB · 73–101 tok/s estimateExcellent
Qwen2.5 7B Instruct Q4_K_M8K~5.9 GB · 83–116 tok/s estimateExcellent
Qwen2.5 7B Instruct IQ4_XS8K~5.5 GB · 91–127 tok/s estimateExcellent
Qwen2.5 7B Instruct Q3_K_M8K~5.1 GB · 98–139 tok/s estimateExcellent

Alternatives

Where to buy a RTX 2080 Ti

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GeForce RTX 2080 Ti

11 GB GDDR6 · 616 GB/s · 250 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 2080 Ti run Qwen2.5 7B Instruct?
The model needs about 5.9 GB and fits in the 10.7 GB available, with 4.8 GB to spare.
Which quantization should I use on the RTX 2080 Ti?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~9.2 GB / 10.7 GB.
What is the largest context on the RTX 2080 Ti?
At Q4_K_M, about 91K 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 2080 Ti)?
We estimate 83–116 tok/s (estimate, confidence low). Based on: memory bandwidth (616 GB/s) and model size.