Skip to content
Run LLM Locally

Phi-4 14B on the RTX 2080 Ti

Phi-4 14B at Q4_K_M needs about 11.2 GB, more than the 11 GB of the RTX 2080 Ti: part of the model runs from system RAM, which slows generation. At Q3_K_M it fits entirely.

Phi-4 14B

Q4_K_M · 8K context

CPU/GPU offload

The model needs about 11.2 GB but only 10.7 GB of VRAM is usable: 0.6 GB must be loaded into system RAM. It runs, 95% on the GPU, but more slowly.

Your hardware
RTX 2080 Ti 11 GB · 64 GB RAM
Estimated memory
~11.2 GB estimate
VRAM (usable memory)
10.7 GB
GPU only
No
CPU/GPU offload
Required (0.6 GB)
Generation speed
19–33 tok/s estimate
Largest context in fast memory
5K tokens
Confidence: low

Based on: memory bandwidth (616 GB/s) and model size · system RAM bandwidth (~51 GB/s)

Memory breakdown

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

0.6 GB must be offloaded to system RAM

  • 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
  • split CPU/GPU runs vary a lot with the CPU and PCIe link
  • system RAM speed unknown (dual-channel DDR4-3200 assumed)

Other quantizations on the RTX 2080 Ti

Phi-4 14B on RTX 2080 Ti + 64 GB RAM · 8K context
Phi-4 14B Q8_08K~17.6 GB · 3–5.5 tok/s estimateCPU/GPU offload
Phi-4 14B Q6_K8K~14.2 GB · 5.5–10 tok/s estimateCPU/GPU offload
Phi-4 14B Q5_K_M8K~12.8 GB · 8–15 tok/s estimateCPU/GPU offload
Phi-4 14B Q4_K_M8K~11.2 GB · 19–33 tok/s estimateCPU/GPU offload
Phi-4 14B IQ4_XS8K~10.2 GB · 48–66 tok/s estimateCompatible with limits
Phi-4 14B Q3_K_M8K~9.6 GB · 51–70 tok/s estimateCompatible

Alternatives

Hardware that runs Phi-4 14B Q4_K_M entirely in memory

Prices and links follow the market you choose.

GeForce RTX 4060 Ti 16 GB

NVIDIA

ExcellentRuns Phi-4 14B Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

Radeon RX 7600 XT

AMD

ExcellentRuns Phi-4 14B Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

MacBook Pro 14 M3 Pro

Apple Silicon

CompatibleRuns Phi-4 14B Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

AMD Ryzen AI Max+ 395

Unified memory PC

ExcellentRuns Phi-4 14B 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 2080 Ti run Phi-4 14B?
The model needs about 11.2 GB but only 10.7 GB of VRAM is usable: 0.6 GB must be loaded into system RAM. It runs, 95% on the GPU, but more slowly.
Which quantization should I use on the RTX 2080 Ti?
IQ4_XS is the highest-precision quantization that fits entirely in VRAM at 8K context. ~10.2 GB / 10.7 GB.
What is the largest context on the RTX 2080 Ti?
At Q4_K_M, about 5K 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 2080 Ti)?
We estimate 19–33 tok/s (estimate, confidence low). Based on: memory bandwidth (616 GB/s) and model size, system RAM bandwidth (~51 GB/s).