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Run LLM Locally

Qwen2.5 7B Instruct on the RTX A6000

With 48 GB of VRAM, the RTX A6000 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 46.6 GB available, with 40.7 GB to spare.

Your hardware
RTX A6000 48 GB · 64 GB RAM
Estimated memory
~5.9 GB estimate
VRAM (usable memory)
46.6 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
100–142 tok/s estimate
Largest context in fast memory
128K tokens
Confidence: low

Based on: memory bandwidth (768 GB/s) and model size

Memory breakdown

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

Fits entirely in VRAM · 40.7 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 A6000

Qwen2.5 7B Instruct on RTX A6000 + 64 GB RAM · 8K context
Qwen2.5 7B Instruct Q8_08K~9.2 GB · 64–87 tok/s estimateExcellent
Qwen2.5 7B Instruct Q6_K8K~7.4 GB · 79–110 tok/s estimateExcellent
Qwen2.5 7B Instruct Q5_K_M8K~6.6 GB · 89–124 tok/s estimateExcellent
Qwen2.5 7B Instruct Q4_K_M8K~5.9 GB · 100–142 tok/s estimateExcellent
Qwen2.5 7B Instruct IQ4_XS8K~5.5 GB · 109–156 tok/s estimateExcellent
Qwen2.5 7B Instruct Q3_K_M8K~5.1 GB · 118–170 tok/s estimateExcellent

Alternatives

Where to buy a RTX A6000

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RTX A6000

48 GB GDDR6 ECC · 768 GB/s · 300 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 A6000 run Qwen2.5 7B Instruct?
The model needs about 5.9 GB and fits in the 46.6 GB available, with 40.7 GB to spare.
Which quantization should I use on the RTX A6000?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~9.2 GB / 46.6 GB.
What is the largest context on the RTX A6000?
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 Qwen2.5 7B Instruct (RTX A6000)?
We estimate 100–142 tok/s (estimate, confidence low). Based on: memory bandwidth (768 GB/s) and model size.