Click a card to populate the GPU selector. Data as of Feb 2026.
🖥️ NVMe-GPU Direct
Bypass CPU/RAM: load model weights directly from NVMe to GPU via GPUDirect Storage / P2P DMA.
Eliminates PCIe bounce through system memory. Requires CUDA 12+ and compatible NVMe controller.
Best for: rapid model swapping, memory-constrained multi-model serving.
📦 GGML/GGUF Quantization
llama.cpp ecosystem: Q4_K_M, Q5_K_M, Q6_K, etc. Run 70B models on consumer hardware.
K-quant variants preserve quality better than naive round-to-nearest.
CPU offload allows exceeding VRAM limits with memory-mapping.
Quality loss: ~1-3% perplexity increase at Q4, negligible at Q6+.
⚡ ASIC Inference Chips
Purpose-built silicon: Groq LPU (deterministic latency, ~500 tok/s per chip),
Cerebras WSE-3 (wafer-scale), Taalas (novel architecture). SambaNova RDU.
Typically cloud-hosted. Unmatched throughput/watt for specific model architectures.
Trade-off: limited flexibility, vendor lock-in.
☁️ Cloud API
Zero CapEx, pay-per-token. Best for: variable load, frontier models (GPT-4o, Claude),
rapid prototyping. Downsides: latency variance, rate limits, data privacy,
cost scales linearly with usage. No amortization benefit.
⚡
Configure your scenario and click Run Analysis to compare local vs cloud inference costs.
Cumulative Cost Over Time
Performance Comparison
Detailed Comparison
Side-by-side metrics for all inference methods
Metric
Local GPU
Quantized
ASIC/Cloud
API
Break-Even Analysis
When does local hardware pay for itself vs cloud API?