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Run LLM Locally

Qwen2.5 Coder 32B Instruct on the RTX 6000 Ada

With 48 GB of VRAM, the RTX 6000 Ada runs Qwen2.5 Coder 32B Instruct at Q4_K_M entirely on the GPU with an 8K context (about 22.4 GB needed). At Q8_0 it fits entirely.

Qwen2.5 Coder 32B Instruct

Q4_K_M · 8K context

Excellent

The model needs about 22.4 GB and fits in the 46.6 GB available, with 24.2 GB to spare.

Your hardware
RTX 6000 Ada 48 GB · 64 GB RAM
Estimated memory
~22.4 GB estimate
VRAM (usable memory)
46.6 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
31–43 tok/s estimate
Largest context in fast memory
100K tokens
Confidence: low

Based on: memory bandwidth (960 GB/s) and model size

Memory breakdown

~22.4 GB (21.6 GB–23.2 GB) needed · estimate

Fits entirely in VRAM · 24.2 GB left

  • Model weights18.5 GB
  • KV cache2 GB
  • Runtime overhead0.8 GB
  • Safety margin1.1 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 6000 Ada

Qwen2.5 Coder 32B Instruct on RTX 6000 Ada + 64 GB RAM · 8K context
Qwen2.5 Coder 32B Instruct Q8_08K~37 GB · 19–26 tok/s estimateExcellent
Qwen2.5 Coder 32B Instruct Q6_K8K~29.3 GB · 24–33 tok/s estimateExcellent
Qwen2.5 Coder 32B Instruct Q5_K_M8K~25.7 GB · 27–37 tok/s estimateExcellent
Qwen2.5 Coder 32B Instruct Q4_K_M8K~22.4 GB · 31–43 tok/s estimateExcellent
Qwen2.5 Coder 32B Instruct IQ4_XS8K~20.3 GB · 34–47 tok/s estimateExcellent
Qwen2.5 Coder 32B Instruct Q3_K_M8K~18.5 GB · 37–52 tok/s estimateExcellent

Alternatives

Where to buy a RTX 6000 Ada

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RTX 6000 Ada Generation

48 GB GDDR6 ECC · 960 GB/s · 300 W

ExcellentRuns Qwen2.5 Coder 32B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RTX 6000 Ada run Qwen2.5 Coder 32B Instruct?
The model needs about 22.4 GB and fits in the 46.6 GB available, with 24.2 GB to spare.
Which quantization should I use on the RTX 6000 Ada?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~37 GB / 46.6 GB.
What is the largest context on the RTX 6000 Ada?
At Q4_K_M, about 100K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Qwen2.5 Coder 32B Instruct (RTX 6000 Ada)?
We estimate 31–43 tok/s (estimate, confidence low). Based on: memory bandwidth (960 GB/s) and model size.