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Llama 3.1 8B Instruct on the RTX 3080

With 10 GB of VRAM, the RTX 3080 runs Llama 3.1 8B Instruct at Q4_K_M entirely on the GPU with an 8K context (about 6.7 GB needed). At Q8_0 it does not fit in VRAM.

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Llama 3.1 8B Instruct

Q4_K_M · 8K context

Excellent

The model needs about 6.7 GB and fits in the 9.7 GB available, with 3 GB to spare.

Your hardware
RTX 3080 10 GB · 64 GB RAM
Estimated memory
~6.7 GB estimate
VRAM (usable memory)
9.7 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
89–126 tok/s estimate
Largest context in fast memory
30K tokens
Confidence: low

Based on: memory bandwidth (760 GB/s) and model size

Memory breakdown

~6.7 GB (6.2 GB–7.1 GB) needed · estimate

Fits entirely in VRAM · 3 GB left

  • Model weights4.6 GB
  • KV cache1 GB
  • Runtime overhead0.8 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 3080

Llama 3.1 8B Instruct on RTX 3080 + 64 GB RAM · 8K context
Llama 3.1 8B Instruct Q8_08K~10.2 GB · 22–40 tok/s estimateCPU/GPU offload
Llama 3.1 8B Instruct Q6_K8K~8.3 GB · 71–99 tok/s estimateCompatible
Llama 3.1 8B Instruct Q5_K_M8K~7.5 GB · 79–111 tok/s estimateExcellent
Llama 3.1 8B Instruct Q4_K_M8K~6.7 GB · 89–126 tok/s estimateExcellent
Llama 3.1 8B Instruct IQ4_XS8K~6.2 GB · 96–137 tok/s estimateExcellent
Llama 3.1 8B Instruct Q3_K_M8K~5.8 GB · 103–149 tok/s estimateExcellent

Alternatives

GPUs that run Llama 3.1 8B Instruct Q4_K_M entirely in VRAM

Other models that fit the RTX 3080

Where to buy a RTX 3080

Prices and links follow the market you choose.

GeForce RTX 3080 (10 GB)

10 GB GDDR6X · 760 GB/s · 320 W

ExcellentRuns Llama 3.1 8B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

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

Frequently asked questions

Can the RTX 3080 run Llama 3.1 8B Instruct?
The model needs about 6.7 GB and fits in the 9.7 GB available, with 3 GB to spare.
Which quantization should I use on the RTX 3080?
Q6_K is the highest-precision quantization that fits entirely in VRAM at 8K context. ~8.3 GB / 9.7 GB.
What is the largest context on the RTX 3080?
At Q4_K_M, about 30K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Llama 3.1 8B Instruct (RTX 3080)?
We estimate 89–126 tok/s (estimate, confidence low). Based on: memory bandwidth (760 GB/s) and model size.