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Run LLM Locally

Llama 3.2 1B Instruct on the RTX 4080 SUPER

With 16 GB of VRAM, the RTX 4080 SUPER runs Llama 3.2 1B Instruct at Q4_K_M entirely on the GPU with an 8K context (about 2 GB needed). At Q8_0 it fits entirely.

Llama 3.2 1B Instruct

Q4_K_M · 8K context

Excellent

The model needs about 2 GB and fits in the 15.5 GB available, with 13.5 GB to spare.

Your hardware
RTX 4080 SUPER 16 GB · 64 GB RAM
Estimated memory
~2 GB estimate
VRAM (usable memory)
15.5 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
382–619 tok/s estimate
Largest context in fast memory
128K tokens
Confidence: low

Based on: memory bandwidth (736 GB/s) and model size

Memory breakdown

~2 GB (1.7 GB–2.3 GB) needed · estimate

Fits entirely in VRAM · 13.5 GB left

  • Model weights0.8 GB
  • KV cache0.3 GB
  • Runtime overhead0.7 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 4080 SUPER

Llama 3.2 1B Instruct on RTX 4080 SUPER + 64 GB RAM · 8K context
Llama 3.2 1B Instruct Q8_08K~2.5 GB · 284–431 tok/s estimateExcellent
Llama 3.2 1B Instruct Q6_K8K~2.2 GB · 334–524 tok/s estimateExcellent
Llama 3.2 1B Instruct Q5_K_M8K~2.1 GB · 357–569 tok/s estimateExcellent
Llama 3.2 1B Instruct Q4_K_M8K~2 GB · 382–619 tok/s estimateExcellent
Llama 3.2 1B Instruct IQ4_XS8K~2 GB · 400–655 tok/s estimateExcellent
Llama 3.2 1B Instruct Q3_K_M8K~1.9 GB · 415–687 tok/s estimateExcellent

Alternatives

Where to buy a RTX 4080 SUPER

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GeForce RTX 4080 SUPER

16 GB GDDR6X · 736 GB/s · 320 W

ExcellentRuns Llama 3.2 1B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RTX 4080 SUPER run Llama 3.2 1B Instruct?
The model needs about 2 GB and fits in the 15.5 GB available, with 13.5 GB to spare.
Which quantization should I use on the RTX 4080 SUPER?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~2.5 GB / 15.5 GB.
What is the largest context on the RTX 4080 SUPER?
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 Llama 3.2 1B Instruct (RTX 4080 SUPER)?
We estimate 382–619 tok/s (estimate, confidence low). Based on: memory bandwidth (736 GB/s) and model size.