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Run LLM Locally

Qwen3 30B-A3B on the RTX A6000

With 48 GB of VRAM, the RTX A6000 runs Qwen3 30B-A3B at Q4_K_M entirely on the GPU with an 8K context (about 19.7 GB needed). At Q8_0 it fits entirely.

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Qwen3 30B-A3B

Q4_K_M · 8K context

Excellent

The model needs about 19.7 GB and fits in the 46.6 GB available, with 26.8 GB to spare.

Your hardware
RTX A6000 48 GB · 64 GB RAM
Estimated memory
~19.7 GB estimate
VRAM (usable memory)
46.6 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
109–176 tok/s estimate
Largest context in fast memory
128K tokens
Confidence: low

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

Memory breakdown

~19.7 GB (19.1 GB–20.5 GB) needed · estimate

Fits entirely in VRAM · 26.8 GB left

  • Model weights17.3 GB
  • KV cache0.8 GB
  • Runtime overhead0.8 GB
  • Safety margin0.9 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
  • mixture-of-experts: speed depends on expert routing

Other quantizations on the RTX A6000

Qwen3 30B-A3B on RTX A6000 + 64 GB RAM · 8K context
Qwen3 30B-A3B Q8_08K~33.4 GB · 82–125 tok/s estimateExcellent
Qwen3 30B-A3B Q6_K8K~26.1 GB · 94–148 tok/s estimateExcellent
Qwen3 30B-A3B Q5_K_M8K~22.8 GB · 101–161 tok/s estimateExcellent
Qwen3 30B-A3B Q4_K_M8K~19.7 GB · 109–176 tok/s estimateExcellent
Qwen3 30B-A3B IQ4_XS8K~17.7 GB · 115–188 tok/s estimateExcellent
Qwen3 30B-A3B Q3_K_M8K~15.4 GB · 122–204 tok/s estimateExcellent

Alternatives

GPUs that run Qwen3 30B-A3B Q4_K_M entirely in VRAM

Other models that fit the RTX A6000

Where to buy a RTX A6000

Prices and links follow the market you choose.

RTX A6000

48 GB GDDR6 ECC · 768 GB/s · 300 W

ExcellentRuns Qwen3 30B-A3B Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RTX A6000 run Qwen3 30B-A3B?
The model needs about 19.7 GB and fits in the 46.6 GB available, with 26.8 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. ~33.4 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 Qwen3 30B-A3B (RTX A6000)?
We estimate 109–176 tok/s (estimate, confidence low). Based on: memory bandwidth (768 GB/s) and model size.