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Run LLM Locally

Gemma 3 4B IT on the RTX 4060

With 8 GB of VRAM, the RTX 4060 runs Gemma 3 4B IT at Q4_K_M entirely on the GPU with an 8K context (about 3.9 GB needed). At Q8_0 it fits entirely.

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Gemma 3 4B IT

Q4_K_M · 8K context

Excellent

The model needs about 3.9 GB and fits in the 7.7 GB available, with 3.7 GB to spare.

Your hardware
RTX 4060 8 GB · 64 GB RAM
Estimated memory
~3.9 GB estimate
VRAM (usable memory)
7.7 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
68–95 tok/s estimate
Largest context in fast memory
128K tokens
Confidence: low

Based on: memory bandwidth (272 GB/s) and model size

Memory breakdown

~3.9 GB (3.5 GB–4.4 GB) needed · estimate

Fits entirely in VRAM · 3.7 GB left

  • Model weights2.3 GB
  • KV cache0.3 GB
  • Runtime overhead1 GB
  • Safety margin0.3 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

Other quantizations on the RTX 4060

Gemma 3 4B IT on RTX 4060 + 64 GB RAM · 8K context
Gemma 3 4B IT Q8_08K~5.4 GB · 45–60 tok/s estimateExcellent
Gemma 3 4B IT Q6_K8K~4.6 GB · 56–76 tok/s estimateExcellent
Gemma 3 4B IT Q5_K_M8K~4.2 GB · 62–85 tok/s estimateExcellent
Gemma 3 4B IT Q4_K_M8K~3.9 GB · 68–95 tok/s estimateExcellent
Gemma 3 4B IT IQ4_XS8K~3.7 GB · 74–103 tok/s estimateExcellent
Gemma 3 4B IT Q3_K_M8K~3.5 GB · 78–110 tok/s estimateExcellent

Alternatives

GPUs that run Gemma 3 4B IT Q4_K_M entirely in VRAM

Other models that fit the RTX 4060

Where to buy a RTX 4060

Prices and links follow the market you choose.

GeForce RTX 4060

8 GB GDDR6 · 272 GB/s · 115 W

ExcellentRuns Gemma 3 4B IT 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 4060 run Gemma 3 4B IT?
The model needs about 3.9 GB and fits in the 7.7 GB available, with 3.7 GB to spare.
Which quantization should I use on the RTX 4060?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~5.4 GB / 7.7 GB.
What is the largest context on the RTX 4060?
At Q4_K_M, about 128K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Gemma 3 4B IT (RTX 4060)?
We estimate 68–95 tok/s (estimate, confidence low). Based on: memory bandwidth (272 GB/s) and model size.