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Run LLM Locally

Gemma 3 27B IT on the RTX 4060 Ti 16 GB

Gemma 3 27B IT at Q4_K_M needs about 18.3 GB, more than the 16 GB of the RTX 4060 Ti 16 GB: part of the model runs from system RAM, which slows generation. At Q3_K_M it fits entirely.

Gemma 3 27B IT

Q4_K_M · 8K context

CPU/GPU offload

The model needs about 18.3 GB but only 15.5 GB of VRAM is usable: 2.8 GB must be loaded into system RAM. It runs, 85% on the GPU, but more slowly.

Your hardware
RTX 4060 Ti 16 GB · 64 GB RAM
Estimated memory
~18.3 GB estimate
VRAM (usable memory)
15.5 GB
GPU only
No
CPU/GPU offload
Required (2.8 GB)
Generation speed
4.5–8.5 tok/s estimate
Largest context in fast memory
None
Confidence: low

Based on: memory bandwidth (288 GB/s) and model size · system RAM bandwidth (~51 GB/s)

Memory breakdown

~18.3 GB (17.6 GB–19.1 GB) needed · estimate

2.8 GB must be offloaded to system RAM

  • Model weights15.4 GB
  • KV cache1 GB
  • Runtime overhead1 GB
  • Safety margin0.9 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (CUDA) · flash attention on · weights = real file size · sliding-window layers keep 1,024 tokens

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 4060 Ti 16 GB

Gemma 3 27B IT on RTX 4060 Ti 16 GB + 64 GB RAM · 8K context
Gemma 3 27B IT Q8_08K~30.2 GB · 1.2–2.5 tok/s estimatePossible but slow
Gemma 3 27B IT Q6_K8K~23.8 GB · 2–4 tok/s estimatePossible but slow
Gemma 3 27B IT Q5_K_M8K~21 GB · 3–5.5 tok/s estimateCPU/GPU offload
Gemma 3 27B IT Q4_K_M8K~18.3 GB · 4.5–8.5 tok/s estimateCPU/GPU offload
Gemma 3 27B IT IQ4_XS8K~16.6 GB · 7.5–13 tok/s estimateCPU/GPU offload
Gemma 3 27B IT Q3_K_M8K~15.3 GB · 15–20 tok/s estimateCompatible with limits

Alternatives

Hardware that runs Gemma 3 27B IT Q4_K_M entirely in memory

Prices and links follow the market you choose.

Radeon RX 7900 XT

AMD

CompatibleRuns Gemma 3 27B IT Q4_K_M entirely in memory

Price not available

Availability unknown

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AMD Ryzen AI Max+ 395

Unified memory PC

CompatibleRuns Gemma 3 27B IT Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

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

Frequently asked questions

Can the RTX 4060 Ti 16 GB run Gemma 3 27B IT?
The model needs about 18.3 GB but only 15.5 GB of VRAM is usable: 2.8 GB must be loaded into system RAM. It runs, 85% on the GPU, but more slowly.
Which quantization should I use on the RTX 4060 Ti 16 GB?
Q3_K_M is the highest-precision quantization that fits entirely in VRAM at 8K context. ~15.3 GB / 15.5 GB.
What is the largest context on the RTX 4060 Ti 16 GB?
At Q4_K_M, the model does not fit entirely in VRAM even at a short context.
How fast is Gemma 3 27B IT (RTX 4060 Ti 16 GB)?
We estimate 4.5–8.5 tok/s (estimate, confidence low). Based on: memory bandwidth (288 GB/s) and model size, system RAM bandwidth (~51 GB/s).