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Run LLM Locally

Gemma 2 27B IT on the A100 80GB

With 80 GB of VRAM, the A100 80GB runs Gemma 2 27B IT at Q4_K_M entirely on the GPU with an 8K context (about 19.6 GB needed). At Q8_0 it fits entirely.

Gemma 2 27B IT

Q4_K_M · 8K context

Excellent

The model needs about 19.6 GB and fits in the 77.6 GB available, with 58 GB to spare.

Your hardware
A100 80GB · 64 GB RAM
Estimated memory
~19.6 GB estimate
VRAM (usable memory)
77.6 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
68–98 tok/s estimate
Largest context in fast memory
8K tokens
Confidence: low

Based on: memory bandwidth (1,935 GB/s) and model size

Memory breakdown

~19.6 GB (18.9 GB–20.4 GB) needed · estimate

Fits entirely in VRAM · 58 GB left

  • Model weights15.5 GB
  • KV cache2.2 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 4,096 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 A100 80GB

Gemma 2 27B IT on A100 80GB + 64 GB RAM · 8K context
Gemma 2 27B IT Q8_08K~31.6 GB · 44–60 tok/s estimateExcellent
Gemma 2 27B IT Q6_K8K~25.2 GB · 54–76 tok/s estimateExcellent
Gemma 2 27B IT Q5_K_M8K~22.3 GB · 61–86 tok/s estimateExcellent
Gemma 2 27B IT Q4_K_M8K~19.6 GB · 68–98 tok/s estimateExcellent
Gemma 2 27B IT IQ4_XS8K~17.8 GB · 74–108 tok/s estimateExcellent
Gemma 2 27B IT Q3_K_M8K~16.5 GB · 80–117 tok/s estimateExcellent

Alternatives

Hardware that runs Gemma 2 27B IT Q4_K_M entirely in memory

Prices and links follow the market you choose.

Radeon RX 7900 XTX

AMD

ExcellentRuns Gemma 2 27B IT Q4_K_M entirely in memory

Price not available

Availability unknown

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Affiliate link

AMD Ryzen AI Max+ 395

Unified memory PC

CompatibleRuns Gemma 2 27B IT Q4_K_M entirely in memory

Price not available

Availability unknown

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GeForce RTX 2080 Ti

Lowest memory tier

CompatibleRuns Gemma 2 27B IT 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 A100 80GB run Gemma 2 27B IT?
The model needs about 19.6 GB and fits in the 77.6 GB available, with 58 GB to spare.
Which quantization should I use on the A100 80GB?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~31.6 GB / 77.6 GB.
What is the largest context on the A100 80GB?
At Q4_K_M, about 8K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Gemma 2 27B IT (A100 80GB)?
We estimate 68–98 tok/s (estimate, confidence low). Based on: memory bandwidth (1,935 GB/s) and model size.