AI & Local LLM Hardware Suite
Empirical memory formulas, Ollama layer offloading plans, and exact VRAM calculators verified on real NVIDIA RTX, Apple Silicon, and AMD silicon.
Core Hardware Calculators
LLM VRAM & Quantization Calculator
Compute exact GPU memory for weights, KV cache, and CUDA overhead for DeepSeek R1, Qwen 2.5 Coder, Llama 3.3, and Gemma 2.
- Dynamic KV cache sizing at 4K to 128K context
- Precise GGUF quantizations (Q3, Q4_K_M, Q5, Q8)
- Estimated generation speeds (tokens/second)
Ollama Hardware & Layer Offloader
Calculate exact layer splits between GPU VRAM and DDR5 system RAM to maximize tokens/sec without crashing on out-of-memory (OOM).
- Optimal layer offload splits for 6GB, 8GB, 12GB
- 1-Click direct Ollama CLI commands
- FlashAttention memory footprint savings
DeepSeek-R1 Hardware Sizer
Specialized requirements for DeepSeek-R1 Full (671B MoE) and Distilled models (1.5B, 7B, 8B, 14B, 32B, 70B) across consumer hardware.
- MoE active vs total parameter RAM overhead
- Multi-GPU and unified memory configurations
- Reasoning trace context window scaling
AI Token Cost & API vs Local ROI
Compare cloud API pricing across Claude 3.5 Sonnet, GPT-4o, and DeepSeek-V3 against the break-even cost of owning your own GPU.
- Live pricing per million input/output tokens
- Electricity vs cloud token financial models
- Break-even ROI time horizon simulator
GPU Hardware & Architecture Matrix
GeForce RTX 4090
Runs 32B models unquantized and 70B models at Q3_K_M with extreme throughput.
GeForce RTX 4080 Laptop
High-end mobile workstation for 14B coding models and 8B at full 32K context.
GeForce RTX 4070 Laptop
Fast inference for 8B models and Q3 quants of Qwen 2.5 Coder 14B with offload.
GeForce RTX 4060 Laptop
The developer sweet spot: runs Qwen 2.5 Coder 14B at 38 tok/s using Q3_K_M.
GeForce RTX 3060 Desktop
Budget desktop champion for 14B local models and full FP16 8B agents.
GeForce RTX 3060 Laptop
Reliable baseline for Llama 3 8B at Q4_K_S and Phi-4 14B with system RAM offload.
Apple Silicon (M1-M4)
Run massive 70B and 671B MoE models directly in unified RAM without clustering.
Explore Full Matrix →
View all benchmarks and memory bandwidth analysis
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