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Run LLM Locally

Phi-4 14B on the RTX 4070

Phi-4 14B at Q4_K_M needs about 11.2 GB; the RTX 4070 has 12 GB: it fits with almost no margin at 8K context. At Q8_0 it does not fit in VRAM.

Phi-4 14B

Q4_K_M · 8K context

Compatible with limits

The model needs about 11.2 GB (up to 11.8 GB) for 11.6 GB available: it fits, but with almost no margin. Use a shorter context or a smaller quantization if loading fails.

Your hardware
RTX 4070 12 GB · 64 GB RAM
Estimated memory
~11.2 GB estimate
VRAM (usable memory)
11.6 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
36–48 tok/s estimate
Largest context in fast memory
9K tokens
Confidence: low

Based on: memory bandwidth (504 GB/s) and model size

Memory breakdown

~11.2 GB (10.8 GB–11.8 GB) needed · estimate

Fits entirely in VRAM · 0.4 GB left

  • Model weights8.4 GB
  • KV cache1.6 GB
  • Runtime overhead0.7 GB
  • Safety margin0.5 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 4070

Phi-4 14B on RTX 4070 + 64 GB RAM · 8K context
Phi-4 14B Q8_08K~17.6 GB · 3–6 tok/s estimateCPU/GPU offload
Phi-4 14B Q6_K8K~14.2 GB · 6.5–13 tok/s estimateCPU/GPU offload
Phi-4 14B Q5_K_M8K~12.8 GB · 12–21 tok/s estimateCPU/GPU offload
Phi-4 14B Q4_K_M8K~11.2 GB · 36–48 tok/s estimateCompatible with limits
Phi-4 14B IQ4_XS8K~10.2 GB · 40–54 tok/s estimateCompatible
Phi-4 14B Q3_K_M8K~9.6 GB · 43–58 tok/s estimateExcellent

Alternatives

Where to buy a RTX 4070

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GeForce RTX 4070

12 GB GDDR6X · 504 GB/s · 200 W

Compatible with limitsRuns Phi-4 14B Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RTX 4070 run Phi-4 14B?
The model needs about 11.2 GB (up to 11.8 GB) for 11.6 GB available: it fits, but with almost no margin. Use a shorter context or a smaller quantization if loading fails.
Which quantization should I use on the RTX 4070?
Q4_K_M is the highest-precision quantization that fits entirely in VRAM at 8K context. ~11.2 GB / 11.6 GB.
What is the largest context on the RTX 4070?
At Q4_K_M, about 9K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Phi-4 14B (RTX 4070)?
We estimate 36–48 tok/s (estimate, confidence low). Based on: memory bandwidth (504 GB/s) and model size.