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Run LLM Locally

Qwen2.5 14B Instruct on the RX 7900 XTX

With 24 GB of VRAM, the RX 7900 XTX runs Qwen2.5 14B Instruct at Q4_K_M entirely on the GPU with an 8K context (about 11.3 GB needed). At Q8_0 it fits entirely.

Qwen2.5 14B Instruct

Q4_K_M · 8K context

Excellent

The model needs about 11.3 GB and fits in the 23.3 GB available, with 12 GB to spare.

Your hardware
RX 7900 XTX 24 GB · 64 GB RAM
Estimated memory
~11.3 GB estimate
VRAM (usable memory)
23.3 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
52–79 tok/s estimate
Largest context in fast memory
68K tokens
Confidence: low

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

Memory breakdown

~11.3 GB (10.7 GB–11.9 GB) needed · estimate

Fits entirely in VRAM · 12 GB left

  • Model weights8.4 GB
  • KV cache1.5 GB
  • Runtime overhead0.9 GB
  • Safety margin0.5 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (ROCm) · flash attention on · weights = real file size

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 XTX

Qwen2.5 14B Instruct on RX 7900 XTX + 64 GB RAM · 8K context
Qwen2.5 14B Instruct Q8_08K~17.8 GB · 34–50 tok/s estimateExcellent
Qwen2.5 14B Instruct Q6_K8K~14.3 GB · 42–62 tok/s estimateExcellent
Qwen2.5 14B Instruct Q5_K_M8K~12.8 GB · 47–69 tok/s estimateExcellent
Qwen2.5 14B Instruct Q4_K_M8K~11.3 GB · 52–79 tok/s estimateExcellent
Qwen2.5 14B Instruct IQ4_XS8K~10.4 GB · 56–85 tok/s estimateExcellent
Qwen2.5 14B Instruct Q3_K_M8K~9.7 GB · 60–92 tok/s estimateExcellent

Alternatives

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Radeon RX 7900 XTX

24 GB GDDR6 · 960 GB/s · 355 W

ExcellentRuns Qwen2.5 14B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RX 7900 XTX run Qwen2.5 14B Instruct?
The model needs about 11.3 GB and fits in the 23.3 GB available, with 12 GB to spare.
Which quantization should I use on the RX 7900 XTX?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~17.8 GB / 23.3 GB.
What is the largest context on the RX 7900 XTX?
At Q4_K_M, about 68K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Qwen2.5 14B Instruct (RX 7900 XTX)?
We estimate 52–79 tok/s (estimate, confidence low). Based on: memory bandwidth (960 GB/s) and model size.