CUDA Cores
VRAM
GB/s
0
CUDA Cores
0
Base MHz
0
Boost MHz
12GB GDDR6
192-bit bus
~12
FP32 TFLOPS
80
FP16 TFLOPS
225W
TDP
0
Available Instances
$0.00/hr
Starting Price
| Architecture | TrueGPU (Unknown) |
| Release Date | 2026-05-15 |
| Launch Price | $480.00 |
| Process | 6nm |
| Transistors | Unknown |
Gen 3
Tensor Cores
Disabled
Transformer Engine
Not Supported
Flash Attention
294mm
Length
120mm
Width
49mm
Height
The Lisuan LX 7G100 was introduced in 2026 on the TrueGPU architecture. Its specification combines its vendor-specific compute units with 12GB of GDDR6, which defines both the workloads it can run and the batch sizes it can hold.
Based on its 12GB memory capacity, the LX 7G100 is best evaluated for budget inference, computer vision, local development, and carefully sized fine-tuning jobs. Always size the model, optimizer state, KV cache, and framework overhead together rather than choosing a GPU from core count alone.
GPUvec does not currently have an active hourly cloud listing for the LX 7G100. This is not a zero-dollar offer: it means no comparable provider price is available in the present dataset. Check the provider directory for availability or compare a related GPU with an active listing.
For software planning, the LX 7G100 combines 384 GB/s of memory bandwidth, CUDA compute capability the latest, and a 6nm manufacturing process. Confirm the minimum CUDA, PyTorch, TensorFlow, or driver version required by your application before renting or purchasing hardware.
Learn more about GPUs from these authoritative sources:
Official CUDA programming guide
NVIDIA GPU Specifications →Official NVIDIA GPU specs
TechPowerUp GPU Database →Comprehensive GPU specifications
CUDA Compute Capability Guide →GPU compute capability reference
The Lisuan LX 7G100 uses the TrueGPU architecture and combines vendor-specific compute units with 12GB of GDDR6. GPUvec records 384 GB/s memory bandwidth alongside architecture, release date, process node, accelerator features, and physical specifications so the page can be evaluated from source data instead of model-name assumptions.
GPUvec currently has no comparable active hourly listing for this model; this means pricing is unavailable, not free. Provider rates can vary by region, host configuration, billing model, and availability. Use the provider directory and related-GPU links to compare an available alternative when this exact accelerator is not listed.
Start with the 12GB memory limit, then evaluate 384 GB/s memory bandwidth, compute features, and software compatibility for your workload. Model weights, KV cache, activations, optimizer state, and framework overhead all consume memory. Compare this page with related accelerators rather than assuming that a higher core count alone guarantees a better training or inference result.
| Category | Rank 1 | Rank 2 | Rank 3 |
|---|---|---|---|
| Best for Training | NVIDIA H200 | NVIDIA H100 | NVIDIA B200 |
| Best for Inference | NVIDIA A40 | NVIDIA A100 | NVIDIA A10 |
Compare GPU specifications and cloud instances to find the best GPU for your workload.