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Run LLM Locally

Llama 3.1 8B Instruct on the RTX 5070

With 12 GB of VRAM, the RTX 5070 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 fits entirely.

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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 11.6 GB available, with 5 GB to spare.

Your hardware
RTX 5070 12 GB · 64 GB RAM
Estimated memory
~6.7 GB estimate
VRAM (usable memory)
11.6 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
80–113 tok/s estimate
Largest context in fast memory
45K tokens
Confidence: low

Based on: memory bandwidth (672 GB/s) and model size

Memory breakdown

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

Fits entirely in VRAM · 5 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 5070

Llama 3.1 8B Instruct on RTX 5070 + 64 GB RAM · 8K context
Llama 3.1 8B Instruct Q8_08K~10.2 GB · 52–70 tok/s estimateCompatible
Llama 3.1 8B Instruct Q6_K8K~8.3 GB · 64–88 tok/s estimateExcellent
Llama 3.1 8B Instruct Q5_K_M8K~7.5 GB · 71–99 tok/s estimateExcellent
Llama 3.1 8B Instruct Q4_K_M8K~6.7 GB · 80–113 tok/s estimateExcellent
Llama 3.1 8B Instruct IQ4_XS8K~6.2 GB · 87–123 tok/s estimateExcellent
Llama 3.1 8B Instruct Q3_K_M8K~5.8 GB · 94–133 tok/s estimateExcellent

Alternatives

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

Other models that fit the RTX 5070

Where to buy a RTX 5070

Prices and links follow the market you choose.

GeForce RTX 5070

12 GB GDDR7 · 672 GB/s · 250 W

ExcellentRuns Llama 3.1 8B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

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Frequently asked questions

Can the RTX 5070 run Llama 3.1 8B Instruct?
The model needs about 6.7 GB and fits in the 11.6 GB available, with 5 GB to spare.
Which quantization should I use on the RTX 5070?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~10.2 GB / 11.6 GB.
What is the largest context on the RTX 5070?
At Q4_K_M, about 45K 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 5070)?
We estimate 80–113 tok/s (estimate, confidence low). Based on: memory bandwidth (672 GB/s) and model size.