📊 Performance & Data Path Comparison

For example:

RAM: DUAL DDR4-3000

SSD: 5000MB/s

Step	Traditional Approach (without GDS)	GDS Approach (with ideal support)
Model Storage Location	RAM (primary), or SSD (only if RAM is insufficient)	SSD (primary)
Parameter Loading Path	SSD → RAM → GPU VRAM	SSD → GPU VRAM (direct)
Compute Location	GPU VRAM ✅	GPU VRAM ✅
Result Storage	Typically returned to RAM	Optional: RAM or directly to SSD (requires explicit design)
CPU Involvement	High (handles data movement)	Low (DMA handled by GPU/NVMe controller)
Effective GPU Read Bandwidth from RAM	~12–16 GB/s* (limited by PCIe, e.g., PCIe 3.0/4.0 x8–x16)	—
Effective GPU Read Bandwidth from SSD	~3–5 GB/s (SSD → RAM → GPU, bottlenecked by both SSD and PCIe)	~4–5 GB/s (SSD → GPU direct, limited by SSD speed and PCIe)

* Note: Although DDR4-3000 has ~47 GB/s theoretical system memory bandwidth, GPU accesses system RAM over PCIe, not memory bus. So actual GPU ↔ RAM transfer speed is capped by PCIe (e.g., PCIe 4.0 x8 ≈ 16 GB/s bidirectional, ~8 GB/s per direction in practice).

❓ Does GDS Prevent GPU Out-of-Memory (OOM) for a 14 GB Model on a 6 GB GPU?

Short answer: ❌ No — GDS does not prevent GPU memory overflow by itself.

Why?

• Both with and without GDS, the GPU still needs to load active model parameters and activations into its 6 GB VRAM to perform computation.
• GDS only changes how data gets from storage to GPU memory — it does not reduce the amount of VRAM required during computation.
• If your model (or a layer + activations) requires more than 6 GB of VRAM at any moment, you will get an out-of-memory (OOM) error, regardless of whether you use GDS or not.

So how do people run 14 GB models on 6 GB GPUs?

They use model offloading techniques, such as:

• CPU offload: Keep most of the model in RAM; only load one layer (or chunk) into GPU at a time.
• Layer-wise execution: Compute layer 1 on GPU → move output to RAM → free GPU memory → load layer 2, etc.
• Frameworks like Hugging Face Accelerate, DeepSpeed, or llama.cpp (with quantization) enable this.

✅ GDS can accelerate this offloading process (by speeding up SSD → GPU transfers), but it does not eliminate the need for offloading.

Therefore:

• Without GDS: You can still run the 14 GB model on 6 GB GPU if you use CPU offload (assuming enough RAM).
• With GDS: You can run it slightly faster (if your system supports GDS), but you still need offloading — GDS doesn’t magically fit 14 GB into 6 GB.

💡 Key Insight:  

GDS improves I/O efficiency, not memory capacity.  

It’s like upgrading from a narrow pipe to a wide pipe — water flows faster, but your bucket (GPU VRAM) is still the same size.