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Run LLM Locally

Gemma 3 4B IT on the RTX 5080

With 16 GB of VRAM, the RTX 5080 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 15.5 GB available, with 11.6 GB to spare.

Your hardware
RTX 5080 16 GB · 64 GB RAM
Estimated memory
~3.9 GB estimate
VRAM (usable memory)
15.5 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
178–288 tok/s estimate
Largest context in fast memory
128K tokens
Confidence: low

Based on: memory bandwidth (960 GB/s) and model size

Memory breakdown

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

Fits entirely in VRAM · 11.6 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 5080

Gemma 3 4B IT on RTX 5080 + 64 GB RAM · 8K context
Gemma 3 4B IT Q8_08K~5.4 GB · 128–192 tok/s estimateExcellent
Gemma 3 4B IT Q6_K8K~4.6 GB · 153–237 tok/s estimateExcellent
Gemma 3 4B IT Q5_K_M8K~4.2 GB · 165–261 tok/s estimateExcellent
Gemma 3 4B IT Q4_K_M8K~3.9 GB · 178–288 tok/s estimateExcellent
Gemma 3 4B IT IQ4_XS8K~3.7 GB · 189–309 tok/s estimateExcellent
Gemma 3 4B IT Q3_K_M8K~3.5 GB · 197–327 tok/s estimateExcellent

Alternatives

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

Other models that fit the RTX 5080

Where to buy a RTX 5080

Prices and links follow the market you choose.

GeForce RTX 5080

16 GB GDDR7 · 960 GB/s · 360 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 5080 run Gemma 3 4B IT?
The model needs about 3.9 GB and fits in the 15.5 GB available, with 11.6 GB to spare.
Which quantization should I use on the RTX 5080?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~5.4 GB / 15.5 GB.
What is the largest context on the RTX 5080?
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 5080)?
We estimate 178–288 tok/s (estimate, confidence low). Based on: memory bandwidth (960 GB/s) and model size.