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Run LLM Locally

Llama 3.3 70B Instruct on the RTX A6000

With 48 GB of VRAM, the RTX A6000 runs Llama 3.3 70B Instruct at Q4_K_M entirely on the GPU with an 8K context (about 45.1 GB needed). At Q8_0 it does not fit in VRAM.

Llama 3.3 70B Instruct

Q4_K_M · 8K context

Compatible

The model needs about 45.1 GB and fits in the 46.6 GB available (1.5 GB left).

Your hardware
RTX A6000 48 GB · 64 GB RAM
Estimated memory
~45.1 GB estimate
VRAM (usable memory)
46.6 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
13–17 tok/s estimate
Largest context in fast memory
12K tokens
Confidence: low

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

Memory breakdown

~45.1 GB (43.9 GB–46.3 GB) needed · estimate

Fits entirely in VRAM · 1.5 GB left

  • Model weights39.6 GB
  • KV cache2.5 GB
  • Runtime overhead0.8 GB
  • Safety margin2.1 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 A6000

Llama 3.3 70B Instruct on RTX A6000 + 64 GB RAM · 8K context
Llama 3.3 70B Instruct Q8_08K~76.8 GB · 0.6–1.2 tok/s estimateNot recommended
Llama 3.3 70B Instruct Q6_K8K~60.1 GB · 1.3–2.5 tok/s estimatePossible but slow
Llama 3.3 70B Instruct Q5_K_M8K~52.3 GB · 2.5–5 tok/s estimateCPU/GPU offload
Llama 3.3 70B Instruct Q4_K_M8K~45.1 GB · 13–17 tok/s estimateCompatible
Llama 3.3 70B Instruct IQ4_XS8K~40.6 GB · 14–19 tok/s estimateCompatible
Llama 3.3 70B Instruct Q3_K_M8K~37 GB · 16–21 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

CompatibleRuns Llama 3.3 70B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RTX A6000 run Llama 3.3 70B Instruct?
The model needs about 45.1 GB and fits in the 46.6 GB available (1.5 GB left).
Which quantization should I use on the RTX A6000?
Q4_K_M is the highest-precision quantization that fits entirely in VRAM at 8K context. ~45.1 GB / 46.6 GB.
What is the largest context on the RTX A6000?
At Q4_K_M, about 12K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Llama 3.3 70B Instruct (RTX A6000)?
We estimate 13–17 tok/s (estimate, confidence low). Based on: memory bandwidth (768 GB/s) and model size.