ASUS Ascent GX10 128GB vs NVIDIA GeForce RTX 4090

For running models locally, priced in Canada. At 64k context the first runs 69 models fully in memory, the second 40.

In short

The ASUS Ascent GX10 128GB has more memory for models: 128 GB against 24 GB. At 64k context the ASUS Ascent GX10 128GB runs 69 of the models we track fully in memory and the NVIDIA GeForce RTX 4090 40; only the ASUS Ascent GX10 128GB holds DeepSeek V4 Flash. On the models both run fully in memory, the NVIDIA GeForce RTX 4090 is about 3.7× as fast as the ASUS Ascent GX10 128GB (estimated, median across models). In Canada the ASUS Ascent GX10 128GB costs $8,299 at Amazon and the NVIDIA GeForce RTX 4090 $7,107 at Amazon: the NVIDIA GeForce RTX 4090 is $1,192 cheaper.

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

Specifications

SpecASUS Ascent GX10 128GBNVIDIA GeForce RTX 4090
ChipGB10 Grace Blackwell–
Memory bus256-bit384-bit
Memory bandwidth273 GB/s1,008 GB/s
FP16 compute62 TFLOPS165 TFLOPS
SoftwareCUDACUDA
Unified memory128 GB–
GPU can use by default119 GB–
GPU can use, tuned122 GB–
Operating systemdgx-os–
Processor20-core Arm (GB10)–
Storage1000 GB–
GPU–GeForce RTX 4090
Memory–24 GB GDDR6X
Board power–450 W
PCIe generation–Gen 4
PCIe lanes–x16

What each runs

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

ModelASUS Ascent GX10 128GBNVIDIA GeForce RTX 4090
DeepSeek V4 Flash8tok/sUD-Q2_K_XLwon't run
GLM 5.3 Flash11tok/sUD-Q2_K_XLwon't run
Qwen 3.8 Flash Next2tok/sQ4_K_Mwon't run
Qwen 3.8 27B5tok/sQ8_029tok/sQ4_K_M
MiniMax M2.75tok/sUD-Q3_K_Mwon't run
Qwen 3.5 122B-A10B10tok/sQ6_Koffload
Qwen 3.6 27B5tok/sQ8_026tok/sQ5_K_M
gpt-oss 120B18tok/sMXFP4won't run
Qwen3 Coder Next18tok/sQ8_0offload
Nemotron 3 Super 120B-A12B9tok/sUD-Q6_Kwon't run
Gemma 4 31B4tok/sQ8_0offload
Mistral Small 4 119B14tok/sUD-Q6_Koffload
Qwen 3.6 35B-A3B23tok/sQ8_0115tok/sQ4_K_M
Qwen 3.5 27B5tok/sQ8_030tok/sQ4_K_M
Gemma 4 26B-A4B18tok/sQ8_078tok/sUD-Q5_K_M
GLM 4.5 Air4tok/sQ6_Kwon't run
Qwen 3.5 35B-A3B23tok/sQ8_0116tok/sQ4_K_M
Qwen3 Next 80B-A3B21tok/sQ8_0offload
GLM 4.7 Flash12tok/sQ8_057tok/sQ4_K_M
Devstral Small 2 24B5tok/sQ8_0offload
Nemotron 3.5 Lightning 30B-A3B27tok/sUD-Q6_K_XLoffload
Kimi Linear 48B-A3B27tok/sQ8_0offload
gpt-oss 20B25tok/sMXFP493tok/sMXFP4
Qwen3 Coder 30B-A3B11tok/sQ8_0offload
Qwen3 32B3tok/sQ8_0offload
Gemma 4 12B12tok/sQ8_044tok/sQ8_0
Qwen 3.5 9B14tok/sQ8_053tok/sQ8_0
Gemma 3 27B5tok/sQ8_028tok/sQ4_K_M
Mistral Small 3.2 24B5tok/sQ8_0offload
DeepSeek R1 Distill Qwen 32B3tok/sQ8_0offload
Llama 3.3 70B2tok/sQ8_0won't run
Ministral 3 14B7tok/sQ8_029tok/sQ6_K
Qwen3 14B6tok/sQ8_027tok/sQ6_K
DeepSeek R1 0528 Qwen3 8B9tok/sQ8_034tok/sQ8_0
Qwen3 8B9tok/sQ8_034tok/sQ8_0
Phi-4 14B6tok/sQ8_026tok/sQ5_K_M
Qwen 3.5 4B25tok/sQ8_094tok/sQ8_0
Ministral 3 8B9tok/sQ8_034tok/sQ8_0
Granite 4.2 8B8tok/sQ8_031tok/sQ8_0
Gemma 4 E4B17tok/sQ8_062tok/sQ8_0
LFM2.5 8B-A1B24tok/sQ8_088tok/sQ8_0
Llama 3.1 8B11tok/sQ6_K41tok/sQ6_K
Qwen2.5 0.5B127tok/sQ8_0468tok/sQ8_0
Qwen3.5 0.8B104tok/sQ8_0382tok/sQ8_0
Qwen3.5 2B75tok/sQ5_K_M277tok/sQ5_K_M
DeepSeek R1 Distill Qwen 1.5B45tok/sQ8_0166tok/sQ8_0
MiniCPM5 2B31tok/sQ8_0114tok/sQ8_0
Mellum2.1 12B A2.5B Thinking28tok/sQ8_0103tok/sQ8_0
LFM2.5 2.6B25tok/sQ8_091tok/sQ8_0
LFM2.5 VL 3B25tok/sQ8_091tok/sQ8_0
Kimi VL A3B19tok/smlx-8bit71tok/smlx-8bit
granite vision 4.1 4b19tok/sQ8_070tok/sQ8_0
granite 4.2 3b18tok/sQ8_068tok/sQ8_0
DeepSeek R1 Distill Qwen 7B14tok/sQ8_053tok/sQ8_0
LFM2.5 24B A2B14tok/sQ8_054tok/sQ5_K_M
GLM 4.6V Flash13tok/sQ8_049tok/sQ8_0
GLM 4.1V 9B Thinking12tok/smlx-8bit43tok/smlx-8bit
DeepSeek R1 Distill Llama 8B10tok/sQ8_037tok/sQ8_0
Ministral 3 8B Reasoning 25129tok/sQ8_034tok/sQ8_0
DeepSeek R1 Distill Qwen 14B6tok/sQ8_027tok/sQ5_K_M
Devstral Small 25075tok/sQ8_0offload
Magistral Small 25065tok/sQ8_0offload
Magistral Small 25075tok/sQ8_0offload
Magistral Small 25095tok/sQ8_0offload
GLM 4.5V4tok/sQ8_0won't run
GLM 4.6V4tok/sQ8_0won't run
granite 4.2 30b4tok/sQ8_0offload
DeepSeek R1 Distill Llama 70B2tok/sQ8_0won't run
Mistral Medium 3.5 128B1tok/sQ6_Kwon't run
DeepSeek R1won't runwon't run
DeepSeek R1 0528won't runwon't run
DeepSeek V3 0324won't runwon't run
DeepSeek V3.1won'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 4.7won'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 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

Questions

Which is better for local AI, the ASUS Ascent GX10 128GB or the NVIDIA GeForce RTX 4090?
Choose the ASUS Ascent GX10 128GB to run bigger models. Choose the NVIDIA GeForce RTX 4090 for speed. Choose the NVIDIA GeForce RTX 4090 to spend less.
Which can run bigger models, the ASUS Ascent GX10 128GB or the NVIDIA GeForce RTX 4090?
At 64k context the ASUS Ascent GX10 128GB runs 69 of the models we track fully in memory and the NVIDIA GeForce RTX 4090 40; only the ASUS Ascent GX10 128GB holds DeepSeek V4 Flash.
Which is faster for local AI, the ASUS Ascent GX10 128GB or the NVIDIA GeForce RTX 4090?
On the models both run fully in memory, the NVIDIA GeForce RTX 4090 is about 3.7× as fast as the ASUS Ascent GX10 128GB (estimated, median across models).
What is the price difference between the ASUS Ascent GX10 128GB and the NVIDIA GeForce RTX 4090?
In Canada the ASUS Ascent GX10 128GB costs $8,299 at Amazon and the NVIDIA GeForce RTX 4090 $7,107 at Amazon: the NVIDIA GeForce RTX 4090 is $1,192 cheaper.

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