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Run LLM Locally

Gemma 3 27B IT on the RTX 6000 Ada

With 48 GB of VRAM, the RTX 6000 Ada runs Gemma 3 27B IT at Q4_K_M entirely on the GPU with an 8K context (about 18.3 GB needed). At Q8_0 it fits entirely.

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Gemma 3 27B IT

Q4_K_M · 8K context

Excellent

The model needs about 18.3 GB and fits in the 46.6 GB available, with 28.2 GB to spare.

Your hardware
RTX 6000 Ada 48 GB · 64 GB RAM
Estimated memory
~18.3 GB estimate
VRAM (usable memory)
46.6 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
37–52 tok/s estimate
Largest context in fast memory
128K tokens
Confidence: low

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

Memory breakdown

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

Fits entirely in VRAM · 28.2 GB left

  • 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

Other quantizations on the RTX 6000 Ada

Gemma 3 27B IT on RTX 6000 Ada + 64 GB RAM · 8K context
Gemma 3 27B IT Q8_08K~30.2 GB · 23–31 tok/s estimateExcellent
Gemma 3 27B IT Q6_K8K~23.8 GB · 29–40 tok/s estimateExcellent
Gemma 3 27B IT Q5_K_M8K~21 GB · 33–45 tok/s estimateExcellent
Gemma 3 27B IT Q4_K_M8K~18.3 GB · 37–52 tok/s estimateExcellent
Gemma 3 27B IT IQ4_XS8K~16.6 GB · 41–57 tok/s estimateExcellent
Gemma 3 27B IT Q3_K_M8K~15.3 GB · 44–63 tok/s estimateExcellent

Alternatives

GPUs that run Gemma 3 27B IT Q4_K_M entirely in VRAM

Other models that fit the RTX 6000 Ada

Where to buy a RTX 6000 Ada

Prices and links follow the market you choose.

RTX 6000 Ada Generation

48 GB GDDR6 ECC · 960 GB/s · 300 W

ExcellentRuns Gemma 3 27B IT Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RTX 6000 Ada run Gemma 3 27B IT?
The model needs about 18.3 GB and fits in the 46.6 GB available, with 28.2 GB to spare.
Which quantization should I use on the RTX 6000 Ada?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~30.2 GB / 46.6 GB.
What is the largest context on the RTX 6000 Ada?
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 27B IT (RTX 6000 Ada)?
We estimate 37–52 tok/s (estimate, confidence low). Based on: memory bandwidth (960 GB/s) and model size.