Methodology

Speed estimates

Generating each token reads the model's weights from VRAM, so memory bandwidth sets the ceiling: tok/s ≈ bandwidth (GB/s) ÷ GB read per token. We multiply by 0.76, the real-world/theoretical ratio one r/LocalLLaMA user measured (29 of 38 tok/s) in this thread. For mixture-of-experts models we scale the file size by active ÷ total parameters. We ignore KV-cache reads (fine at short context; speeds fall at long context), prompt processing, speculative decoding and multi-GPU tensor parallelism. Every speed on this site is an estimate until we publish measured results.

Fit

A model fits when its Q4 GGUF file + 1.5 GB headroom (our assumption for runtime and short context) ≤ VRAM.

Sources

• GPU VRAM and bandwidth: TechPowerUp GPU Database via the open-source dbgpu 2025.12 dataset, each GPU linked to its TechPowerUp page.
• Model parameters: Hugging Face model pages (safetensors totals). Active parameters for MoE models: model cards. Q4 sizes: bartowski GGUF repos (sum of Q4_K_M files).
• GPU prices: picksbycard (daily read of Newegg in-stock listings; "low" = lowest in stock, "avg" = trimmed average; used cards from Newegg marketplace/refurb), Oct 15, 2026.
• Build parts: specific Newegg listings found via componentprices.com, Oct 15, 2026. Totals use only those prices. No eBay prices (eBay's terms restrict automated collection).
• Apple Mac Studio: Apple specs. Strix Halo: Framework Desktop (bandwidth computed from bus width x memory speed). Unified memory is shared with the OS, so fit results are optimistic.
• No verified price = "check price".

Data accessed Oct 15, 2026.