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Run LLM Locally

Gemma 2 9B IT on the RX 7900 XT

With 20 GB of VRAM, the RX 7900 XT runs Gemma 2 9B IT at Q4_K_M entirely on the GPU with an 8K context (about 8.8 GB needed). At Q8_0 it fits entirely.

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Gemma 2 9B IT

Q4_K_M · 8K context

Excellent

The model needs about 8.8 GB and fits in the 19.4 GB available, with 10.6 GB to spare.

Your hardware
RX 7900 XT 20 GB · 64 GB RAM
Estimated memory
~8.8 GB estimate
VRAM (usable memory)
19.4 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
62–93 tok/s estimate
Largest context in fast memory
8K tokens
Confidence: low

Based on: memory bandwidth (800 GB/s) and model size

Memory breakdown

~8.8 GB (8.2 GB–9.4 GB) needed · estimate

Fits entirely in VRAM · 10.6 GB left

  • Model weights5.4 GB
  • KV cache2 GB
  • Runtime overhead1 GB
  • Safety margin0.4 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (ROCm) · 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 RX 7900 XT

Gemma 2 9B IT on RX 7900 XT + 64 GB RAM · 8K context
Gemma 2 9B IT Q8_08K~12.8 GB · 43–62 tok/s estimateExcellent
Gemma 2 9B IT Q6_K8K~10.6 GB · 51–76 tok/s estimateExcellent
Gemma 2 9B IT Q5_K_M8K~9.7 GB · 56–84 tok/s estimateExcellent
Gemma 2 9B IT Q4_K_M8K~8.8 GB · 62–93 tok/s estimateExcellent
Gemma 2 9B IT IQ4_XS8K~8.2 GB · 66–101 tok/s estimateExcellent
Gemma 2 9B IT Q3_K_M8K~7.8 GB · 69–107 tok/s estimateExcellent

Alternatives

GPUs that run Gemma 2 9B IT Q4_K_M entirely in VRAM

Other models that fit the RX 7900 XT

Where to buy a RX 7900 XT

Prices and links follow the market you choose.

Radeon RX 7900 XT

20 GB GDDR6 · 800 GB/s · 315 W

ExcellentRuns Gemma 2 9B IT Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RX 7900 XT run Gemma 2 9B IT?
The model needs about 8.8 GB and fits in the 19.4 GB available, with 10.6 GB to spare.
Which quantization should I use on the RX 7900 XT?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~12.8 GB / 19.4 GB.
What is the largest context on the RX 7900 XT?
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 9B IT (RX 7900 XT)?
We estimate 62–93 tok/s (estimate, confidence low). Based on: memory bandwidth (800 GB/s) and model size.