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Run LLM Locally

Mistral Nemo 12B Instruct on the RTX 3080 Ti

With 12 GB of VRAM, the RTX 3080 Ti runs Mistral Nemo 12B Instruct at Q4_K_M entirely on the GPU with an 8K context (about 9.4 GB needed). At Q8_0 it does not fit in VRAM.

Mistral Nemo 12B Instruct

Q4_K_M · 8K context

Excellent

The model needs about 9.4 GB and fits in the 11.6 GB available, with 2.2 GB to spare.

Your hardware
RTX 3080 Ti 12 GB · 64 GB RAM
Estimated memory
~9.4 GB estimate
VRAM (usable memory)
11.6 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
72–102 tok/s estimate
Largest context in fast memory
21K tokens
Confidence: low

Based on: memory bandwidth (912 GB/s) and model size

Memory breakdown

~9.4 GB (9 GB–10 GB) needed · estimate

Fits entirely in VRAM · 2.2 GB left

  • Model weights7 GB
  • KV cache1.3 GB
  • Runtime overhead0.8 GB
  • Safety margin0.4 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 3080 Ti

Mistral Nemo 12B Instruct on RTX 3080 Ti + 64 GB RAM · 8K context
Mistral Nemo 12B Instruct Q8_08K~14.9 GB · 6–11 tok/s estimateCPU/GPU offload
Mistral Nemo 12B Instruct Q6_K8K~12 GB · 28–50 tok/s estimateCPU/GPU offload
Mistral Nemo 12B Instruct Q5_K_M8K~10.7 GB · 64–89 tok/s estimateCompatible
Mistral Nemo 12B Instruct Q4_K_M8K~9.4 GB · 72–102 tok/s estimateExcellent
Mistral Nemo 12B Instruct IQ4_XS8K~8.7 GB · 77–111 tok/s estimateExcellent
Mistral Nemo 12B Instruct Q3_K_M8K~8.1 GB · 83–121 tok/s estimateExcellent

Alternatives

Where to buy a RTX 3080 Ti

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GeForce RTX 3080 Ti

12 GB GDDR6X · 912 GB/s · 350 W

ExcellentRuns Mistral Nemo 12B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RTX 3080 Ti run Mistral Nemo 12B Instruct?
The model needs about 9.4 GB and fits in the 11.6 GB available, with 2.2 GB to spare.
Which quantization should I use on the RTX 3080 Ti?
Q5_K_M is the highest-precision quantization that fits entirely in VRAM at 8K context. ~10.7 GB / 11.6 GB.
What is the largest context on the RTX 3080 Ti?
At Q4_K_M, about 21K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Mistral Nemo 12B Instruct (RTX 3080 Ti)?
We estimate 72–102 tok/s (estimate, confidence low). Based on: memory bandwidth (912 GB/s) and model size.