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Preparing interactive calculation engine
Preparing interactive calculation engine
Accurately model memory footprints, attention KV-cache scaling, layer offloading, and tokens-per-second generation throughput for running open-weight LLMs on NVIDIA GeForce RTX, Apple Silicon, and AMD Radeon.
Parameter count, layer count, and attention architecture
The community favorite for 12GB–16GB GPUs (RTX 4070, 4060 Ti 16GB, RTX 3060).
VRAM capacity, memory bus bandwidth, and OS compositor
Weight compression ratio and perplexity trade-off
Q4_K_M (4-Bit Medium — Golden Standard): The global community standard for Ollama & llama.cpp. Excellent balance.
Sequence window and Key-Value tensor precision
VRAM exceeded. 43 of 48 layers fit on GPU; remaining 5 layers must be offloaded to System RAM (1.3 GB DDR required). Inference speed will degrade.
ollama run deepseek-r1:14b
To run DeepSeek-R1 Distill Qwen 14B at full GPU speed without offloading to slow system RAM, upgrade to a 16GB or 24GB VRAM GPU (RTX 4060 Ti 16GB, RTX 4070 Ti Super 16GB, or RTX 3090 24GB).
Why do large language models run out of memory during long conversations? How does Grouped-Query Attention (GQA) reduce KV cache bloat? Here is the exact mathematical foundation behind local hardware acceleration.
The baseline memory footprint required to hold model tensors is determined by parameter count and quantization bit-width:
The 1.04 overhead multiplier accounts for non-quantized layer norms, token embedding tables (frequently retained at FP16 or FP32 for semantic fidelity), and tensor index descriptors.
During autoregressive generation, past tokens must be preserved in Key-Value attention tensors so subsequent tokens can attend to previous context:
In older Multi-Head Attention (MHA) models, KV heads equaled query heads (e.g. 32 heads). Modern architectures (Llama 3, Qwen 2.5, DeepSeek) utilize Grouped-Query Attention (GQA), sharing 8 KV heads across 32+ query heads to slash KV cache memory by up to 75%.
During single-user autoregressive token generation (batch size = 1), compute FLOPs are virtually idle. Every new token requires streaming all active model weights through the GPU memory bus:
An RTX 4090 with 1,008 GB/s bandwidth achieves ~70 tok/s on a 14B model, while offloading layers across a PCIe 4.0 bus (25 GB/s) and DDR5 system RAM collapses speeds to 2–5 tok/s.
Apple Silicon (M2/M3/M4 Max and Ultra) unifies CPU, GPU, and Neural Engine memory on a single wide bus. A Mac Studio with 192GB Unified Memory can run 70B and 671B MoE models entirely in RAM without discrete VRAM PCIe transfer penalties.
However, macOS reserves approximately 12–15% of total unified memory for the window server and system kernel, which must be factored into offload capacity.