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Run LLM Locally

Qwen3 14B on the RTX 6000 Ada

With 48 GB of VRAM, the RTX 6000 Ada runs Qwen3 14B at Q4_K_M entirely on the GPU with an 8K context (about 11 GB needed). At Q8_0 it fits entirely.

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Qwen3 14B

Q4_K_M · 8K context

Excellent

The model needs about 11 GB and fits in the 46.6 GB available, with 35.6 GB to spare.

Your hardware
RTX 6000 Ada 48 GB · 64 GB RAM
Estimated memory
~11 GB estimate
VRAM (usable memory)
46.6 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
65–91 tok/s estimate
Largest context in fast memory
128K tokens
Confidence: low

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

Memory breakdown

~11 GB (10.5 GB–11.5 GB) needed · estimate

Fits entirely in VRAM · 35.6 GB left

  • Model weights8.4 GB
  • KV cache1.3 GB
  • Runtime overhead0.8 GB
  • Safety margin0.5 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

Qwen3 14B on RTX 6000 Ada + 64 GB RAM · 8K context
Qwen3 14B Q8_08K~17.5 GB · 41–55 tok/s estimateExcellent
Qwen3 14B Q6_K8K~14 GB · 51–70 tok/s estimateExcellent
Qwen3 14B Q5_K_M8K~12.5 GB · 57–79 tok/s estimateExcellent
Qwen3 14B Q4_K_M8K~11 GB · 65–91 tok/s estimateExcellent
Qwen3 14B IQ4_XS8K~10.1 GB · 70–99 tok/s estimateExcellent
Qwen3 14B Q3_K_M8K~9.3 GB · 76–108 tok/s estimateExcellent

Alternatives

GPUs that run Qwen3 14B Q4_K_M entirely in VRAM

Other models that fit the RTX 6000 Ada

Where to buy a RTX 6000 Ada

Prices and links follow the market you choose.

RTX 6000 Ada Generation

48 GB GDDR6 ECC · 960 GB/s · 300 W

ExcellentRuns Qwen3 14B Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

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No current offer imported for this market: the link opens a marketplace search.

Frequently asked questions

Can the RTX 6000 Ada run Qwen3 14B?
The model needs about 11 GB and fits in the 46.6 GB available, with 35.6 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. ~17.5 GB / 46.6 GB.
What is the largest context on the RTX 6000 Ada?
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 Qwen3 14B (RTX 6000 Ada)?
We estimate 65–91 tok/s (estimate, confidence low). Based on: memory bandwidth (960 GB/s) and model size.