NVIDIA Radeon RX 6700 XT GPU: Specs, VRAM, Price & Benchmarks (2026)
The Radeon RX 6700 XT is a real GPU (launched 2021), but no provider on Aquanode is listing it for rental right now, so there is no live hourly price to quote. Availability changes as providers add and retire hardware; the specs, VRAM and price context below still apply.
How much VRAM does the Radeon RX 6700 XT have?
The Radeon RX 6700 XT has 12GB GDDR6, with 384 GB/s of peak memory bandwidth.
Radeon RX 6700 XT VRAM calculator: check which models fit in its memory at each precision.
All models that fit in 12 GB: the open models whose weights and overhead fit, at native, FP8 and INT4 precision.
What can the Radeon RX 6700 XT run?
Popular open models from small to frontier scale, with the memory each needs and how many Radeon RX 6700 XT cards (12GB GDDR6 each) that takes.
| Model | As published | FP8 | INT4 |
|---|---|---|---|
| Qwen/Qwen3-8B 8.2B | BF16: not supported | FP8: not supported | INT4: not supported |
| Qwen/Qwen2.5-14B-Instruct 14.8B | BF16: not supported | FP8: not supported | INT4: not supported |
| Qwen/Qwen3-32B 32.8B | BF16: not supported | FP8: not supported | INT4: not supported |
| Qwen/Qwen-72B 72.3B | BF16: not supported | FP8: not supported | INT4: not supported |
| MiniMaxAI/MiniMax-M2.7 228.7B | FP8: not supported | – | INT4: not supported |
| deepseek-ai/DeepSeek-R1 684.5B | FP8: not supported | – | INT4: not supported |
Estimates: weights at the stated precision plus a flat 20% for KV cache and overhead, at a moderate context length. A dash means the precision is not offered for that model (it is already published at that size). INT4 needs a published quantized checkpoint. Open any model for a per-GPU breakdown, or use the Radeon RX 6700 XT VRAM calculator.
Radeon RX 6700 XT specs
| Architecture | launched 2021 |
| VRAM | 12GB GDDR6 |
| Memory bandwidth | 384 GB/s |
| TDP | 230W |
| Form factor | PCIe |
Specs sourced from the vendor's public product page. See the source.
GPU Glossary: What is VRAM?, Tensor Cores, CUDA Cores, TFLOPS
Good for
12GB for 7B models at INT8 and 13-14B models at INT4, through llama.cpp-style runtimes on ROCm or Vulkan.
Not good for
RDNA 2 has no matrix or AI units, so there is no BF16 or FP8 path and quantization kernels built for tensor cores do not apply. ROCm support for this card is unofficial on many releases.
Related guides
Other models in the same generation. The full list is in the GPU index.
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