readygpu

AMD Radeon PRO W7900 vs NVIDIA GeForce RTX 5070 Ti

For running models locally, priced in Canada. At 64k context the first runs 63 models fully in memory, the second 31.

In short

The AMD Radeon PRO W7900 has more memory for models: 48 GB against 16 GB. At 64k context the AMD Radeon PRO W7900 runs 63 of the models we track fully in memory and the NVIDIA GeForce RTX 5070 Ti 31; only the AMD Radeon PRO W7900 holds Qwen 3.8 27B. On the models both run fully in memory, the NVIDIA GeForce RTX 5070 Ti is about 1.2× as fast as the AMD Radeon PRO W7900 (estimated, median across models).

Updated . Speeds are estimates; how they are worked out.

AMD Radeon PRO W7900

Not priced here

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NVIDIA GeForce RTX 5070 Ti

$1,760

Amazon

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Specifications

SpecAMD Radeon PRO W7900NVIDIA GeForce RTX 5070 Ti
GPURadeon PRO W7900GeForce RTX 5070 Ti
Memory48 GB GDDR616 GB GDDR7
Memory bus384-bit256-bit
Memory bandwidth864 GB/s896 GB/s
FP16 compute122.6 TFLOPS88 TFLOPS
SoftwareROCmCUDA
Board power295 W300 W
PCIe generationGen 4Gen 5
PCIe lanesx16x16

What each runs

Estimated decode speed with the most precise quant that fits each one est.

ModelAMD Radeon PRO W7900NVIDIA GeForce RTX 5070 Ti
Qwen 3.8 27B14tok/sQ8_0offload
Qwen 3.5 122B-A10B56tok/sUD-Q2_K_XLwon't run
Qwen 3.6 27B14tok/sQ8_0offload
Gemma 4 31B12tok/sQ8_0offload
Mistral Small 4 119B73tok/sUD-Q2_K_XLoffload
Qwen 3.6 35B-A3B64tok/sQ8_0offload
Qwen 3.5 27B14tok/sQ8_0offload
Gemma 4 26B-A4B50tok/sQ8_0offload
Qwen 3.5 35B-A3B64tok/sQ8_0offload
GLM 4.7 Flash32tok/sQ8_0offload
Devstral Small 2 24B13tok/sQ8_0offload
Nemotron 3.5 Lightning 30B-A3B76tok/sUD-Q6_K_XLoffload
Kimi Linear 48B-A3B94tok/sQ6_Koffload
gpt-oss 20B71tok/sMXFP483tok/sMXFP4
Qwen3 Coder 30B-A3B30tok/sQ8_0offload
Qwen3 32B11tok/sQ6_Koffload
Gemma 4 12B34tok/sQ8_040tok/sQ8_0
Qwen 3.5 9B41tok/sQ8_047tok/sQ8_0
Gemma 3 27B14tok/sQ8_0offload
Mistral Small 3.2 24B13tok/sQ8_0offload
DeepSeek R1 Distill Qwen 32B11tok/sQ6_Koffload
Ministral 3 14B19tok/sQ8_0offload
Qwen3 14B18tok/sQ8_0offload
DeepSeek R1 0528 Qwen3 8B26tok/sQ8_038tok/sQ4_K_M
Qwen3 8B26tok/sQ8_038tok/sQ4_K_M
Phi-4 14B16tok/sQ8_0offload
Qwen 3.5 4B71tok/sQ8_083tok/sQ8_0
Ministral 3 8B26tok/sQ8_037tok/sQ5_K_M
Granite 4.2 8B24tok/sQ8_0offload
Gemma 4 E4B47tok/sQ8_055tok/sQ8_0
LFM2.5 8B-A1B67tok/sQ8_078tok/sQ8_0
Llama 3.1 8B31tok/sQ6_K37tok/sQ6_K
Qwen2.5 0.5B356tok/sQ8_0416tok/sQ8_0
Qwen3.5 0.8B291tok/sQ8_0340tok/sQ8_0
Qwen3.5 2B210tok/sQ5_K_M246tok/sQ5_K_M
DeepSeek R1 Distill Qwen 1.5B126tok/sQ8_0147tok/sQ8_0
MiniCPM5 2B86tok/sQ8_0101tok/sQ8_0
Spark X2.5 4B86tok/smlx-4bit101tok/smlx-4bit
Mellum2.1 12B A2.5B Thinking78tok/sQ8_092tok/sQ8_0
FrogNano 4B 260970tok/sQ8_082tok/sQ8_0
LFM2.5 2.6B69tok/sQ8_081tok/sQ8_0
LFM2.5 VL 3B69tok/sQ8_081tok/sQ8_0
Kimi VL A3B Thinking 250655tok/sQ8_084tok/sQ4_K_M
Kimi VL A3B54tok/smlx-8bit79tok/smlx-5bit
granite vision 4.1 4b53tok/sQ8_062tok/sQ8_0
granite 4.2 3b51tok/sQ8_060tok/sQ8_0
Ministral 3 3B Instruct 251245tok/sQ8_052tok/sQ8_0
GLM 4 9B 041444tok/smlx-5bit51tok/smlx-5bit
DeepSeek R1 Distill Qwen 7B40tok/sQ8_047tok/sQ8_0
LFM2.5 24B A2B38tok/sQ8_0offload
GLM 4.6V Flash38tok/sQ8_044tok/sQ8_0
GLM 4.1V 9B Thinking33tok/smlx-8bit38tok/smlx-8bit
DeepSeek R1 Distill Llama 8B28tok/sQ8_037tok/sQ6_K
Ministral 3 8B Reasoning 251226tok/sQ8_037tok/sQ5_K_M
Ministral 3 14B Reasoning 251219tok/sQ8_0offload
Qwen3.8 27B OBLITERATED19tok/smlx-4bitoffload
DeepSeek R1 Distill Qwen 14B17tok/sQ8_0offload
Muse Glimmer 30B14tok/sQ8_0offload
Devstral Small 250713tok/sQ8_0offload
Magistral Small 250613tok/sQ8_0offload
Magistral Small 250713tok/sQ8_0offload
Magistral Small 250913tok/sQ8_0offload
granite 4.2 30b10tok/sQ8_0offload
Qwen3 Coder Nextoffloadwon't run
Nemotron 3 Super 120B-A12Boffloadwon't run
gpt-oss 120Boffloadwon't run
GLM 4.5 Airoffloadwon't run
Qwen3 Next 80B-A3Boffloadwon't run
Llama 3.3 70Boffloadwon't run
GLM 4.5Voffloadwon't run
DeepSeek R1 Distill Llama 70Boffloadwon't run
DeepSeek V4 Flashwon't runwon't run
GLM 5.3 Flashwon't runwon't run
Qwen 3.8 Flash Nextwon't runwon't run
MiniMax M2.7won't runwon't run
DeepSeek R1won't runwon't run
DeepSeek R1 0528won't runwon't run
DeepSeek R1 Zerowon't runwon't run
DeepSeek V3won't runwon't run
DeepSeek V3 0324won't runwon't run
DeepSeek V3.1won't runwon't run
DeepSeek V3.1 Terminuswon't runwon't run
DeepSeek V3.2won't runwon't run
DeepSeek V4 Flash 0731won't runwon't run
DeepSeek V4 Flash Vision Expwon't runwon't run
Devstral 2 123B Instruct 2512won't runwon't run
GLM 4.5won't runwon't run
GLM 4.6won't runwon't run
GLM 4.6Vwon't runwon't run
GLM 4.7won't runwon't run
GLM 5won't runwon't run
GLM 5.1won't runwon't run
GLM 5.2won't runwon't run
GLM 5.3won't runwon't run
Kimi Dev 72Bwon't runwon't run
Kimi K2won't runwon't run
Kimi K2 Instruct 0905won't runwon't run
Kimi K2 Thinkingwon't runwon't run
Kimi K2.5won't runwon't run
MiniMax M2won't runwon't run
MiniMax M2.1won't runwon't run
MiniMax M2.5won't runwon't run
MiniMax M3won't runwon't run
Mistral Medium 3.5 128Bwon't runwon't run

Questions

Which is better for local AI, the AMD Radeon PRO W7900 or the NVIDIA GeForce RTX 5070 Ti?
Choose the AMD Radeon PRO W7900 to run bigger models. Choose the NVIDIA GeForce RTX 5070 Ti for speed.
Which can run bigger models, the AMD Radeon PRO W7900 or the NVIDIA GeForce RTX 5070 Ti?
At 64k context the AMD Radeon PRO W7900 runs 63 of the models we track fully in memory and the NVIDIA GeForce RTX 5070 Ti 31; only the AMD Radeon PRO W7900 holds Qwen 3.8 27B.
Which is faster for local AI, the AMD Radeon PRO W7900 or the NVIDIA GeForce RTX 5070 Ti?
On the models both run fully in memory, the NVIDIA GeForce RTX 5070 Ti is about 1.2× as fast as the AMD Radeon PRO W7900 (estimated, median across models).

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