How Recommendations Work

Data sources

The site’s model list was imported from the Hugging Face Hub API: the top models by download count across the major pipeline types (text generation, code generation, multimodal, image generation, video generation), plus the latest models from the most active open-source providers at the time. The automated daily refresh has been off since 2 May 2026, and the list was last changed on 29 May 2026, so models released after that date are not in it. Guides cover newer models from their official model cards.

Hardware specs are taken from manufacturer spec sheets and updated in the weekly maintenance run. GPU pages show the manufacturer list price with the source and the date it was checked. They do not show an Amazon price. Each product link sends you to Amazon for the live price.

VRAM calculation

Required VRAM is estimated from the model's parameter count, the quantization format, and a runtime overhead factor. The formula we use is:

Where:

• parameters_billions: total parameter count in billions (e.g. 7 for a 7B model).
• bytes_per_param: bytes per weight at the chosen quantization (table below).
• overhead_factor: 1.2 to cover KV cache, activations, and framework overhead at modest (4K to 8K) context lengths.
• os_reserve: 2 GB held back for the OS and the inference engine itself (llama.cpp, Ollama, vLLM, LM Studio).

bytes_per_param by quantization:

Worked example: Llama 3 8B at Q4_K_M

That matches what an 8B Q4_K_M GGUF actually consumes on a single GPU in llama.cpp with a 4K context window, cross-checked against the runs in the XiongjieDai repo cited below.

KV cache scales with context length

The 1.2 overhead factor is only honest at short contexts. The KV cache itself scales linearly with context length:

The leading 2 covers separate keys and values. bytes_per_element is 2 for FP16/BF16 KV cache, 1 for INT8 KV-cache quantization. For a Llama 3 8B model (32 layers, 8 KV heads, head_dim 128, FP16 KV cache) that works out to roughly 131 KB per token, so a 32K context adds about 4 GB on top of the weights. For long-context workloads we use that figure directly instead of the overhead factor.

Hardware tiers

Each model gets three hardware recommendations:

Hardware is matched to tiers by comparing the estimated VRAM requirement at each quantization level against the GPU or unified-memory capacity. The algorithm finds the cheapest hardware that satisfies each tier's VRAM threshold.

Where our numbers come from

This site does not generate tokens-per-second or wattage numbers. Where a page quotes one, it should come from one of the open community benchmarks listed here. "Fits on this card" claims come from the site’s VRAM formula.

Parameter detection

Parameter counts are extracted from the model ID and tags using regex patterns that match common naming conventions (for example 7B, 70b, 0.5B). When a model does not encode its size in its name or tags, no VRAM estimate is shown.

What we do NOT claim

• We do not run a benchmark rig, and this site does not measure hardware. Speed figures on some guides are approximate and not all are cited yet; memory-fit guidance uses the site VRAM formula.
• We do not publish original tokens-per-second numbers. Pages are drafted with AI assistance and reviewed before publishing.
• We do not run controlled studies on model output quality. For evals we link to the community benchmarks that do.
• We are not a hardware vendor and we do not receive sample hardware. Affiliate links on this site are disclosed; they do not change which hardware gets recommended.
• If a number on this site looks wrong, please use the contact page with the page URL.

Cross-checks and corrections

Memory-fit numbers come from the site VRAM formula, and not every speed figure is cited yet. Reader-flagged errors are logged on the corrections page and the original guide is updated with a dated note. Send corrections through the contact page with the page URL and the source you would prefer we cite.

Limitations

• Estimates assume a single GPU or unified memory pool. Multi-GPU setups, CPU offloading, and model parallelism are not modeled.
• Mixture-of-Experts (MoE) models (DeepSeek, Mixtral) activate only a fraction of parameters per token. Our formula uses total parameter count and therefore overestimates VRAM for MoE models.
• Apple Silicon unified memory is shared between CPU and GPU. Available VRAM depends on system RAM allocation and other running applications.
• Context length significantly affects KV cache size. The 1.2 overhead factor is calibrated for 4K to 8K context; 32K+ context needs the explicit KV-cache formula above.
• Quantization quality varies by implementation. GGUF Q4_K_M and GPTQ Q4 are not equivalent even at the same bit width.

Disclaimer

All recommendations are for informational purposes only. Verify requirements before purchasing. See our full disclaimer.