VRAM table: 10 popular local models, ranked by memory needed

Real download sizes from Hugging Face GGUF repos for the ten open-weight models people run most on their own machines, smallest first. Every number links to where it came from. Where we couldn't find a published figure, the cell says not yet sourced rather than guessing.

How we picked these 10

Most-pulled model families in the Ollama library (all-time pulls, what people actually run locally) cross-checked with the most-downloaded chat models on Hugging Face (last 30 days, what is popular right now), both checked 2026-10-23.

• Skip embedding, OCR and vision-only models.
• Skip models under 3B parameters and over 200B (not realistic on one consumer machine).
• Skip an older generation when a newer release in the same line also qualifies (Gemma 2/3 → Gemma 4, Qwen 2.5/3 → Qwen3.8).
• At most two sizes per family.
• Llama 3.1 70B is kept because it is still the most asked-about large model on r/LocalLLaMA, even though its Hugging Face downloads are lower.

Near misses: Mistral 7B v0.3 (Ollama mistral 34M pulls; 1.74M HF downloads/30d), older and superseded in practice; Qwen3.6 35B A3B (3.73M HF downloads/30d); Qwen3 14B and 8B (superseded by Qwen3.8); DeepSeek V4 Flash models (over 200B).

We could not count r/LocalLLaMA mentions systematically (Reddit blocks automated search), so that signal is qualitative.

How to read the columns

• Q4_K_M, Q8_0, FP16/BF16 are the file sizes of the GGUF downloads (one quantizer, bartowski, for every model so they compare like for like). The weights take about this much memory once loaded. Sizes are decimal GB (1 GB = 1,000,000,000 bytes).
• KV cache / 1K tokens is the extra memory the conversation itself takes at FP16, worked out from each model's config.json: 2 (keys and values) × layers × KV heads × head size × 2 bytes, per 1,000 tokens. For models that mix sliding-window or linear attention with full attention, only the layers that grow with the conversation are counted, and the note says so.
• Smallest single GPU and Mac unified memory apply our rule: the Q4_K_M file plus an 8K-token KV cache must fit in 90% of the memory. A * means the KV cache isn't sourced yet, so only the file was counted. macOS also keeps part of unified memory for the system by default, so treat the Mac column as the minimum, not a comfortable fit.
• Released is the date the official Hugging Face repo was created, which is usually the public release day.
• Active params only applies to mixture-of-experts (MoE) models. They still need memory for all parameters, but run about as fast as a model the size of the active part.

Frequently asked questions

Which of these models runs on an 8 GB graphics card?

Llama 3.2 3B (2.02 GB at Q4_K_M), Llama 3.1 8B (4.92 GB) and DeepSeek-R1-0528-Qwen3-8B (5.03 GB) all fit an 8 GB card such as the RTX 5060 with an 8K context, going by our 90% rule.

Why is gpt-oss-120b almost the same size at Q4_K_M and Q8_0?

OpenAI shipped its mixture-of-experts weights in the 4-bit MXFP4 format, and the GGUF builds keep the experts in MXFP4 at every quant level. Only the small non-expert part changes, so the files are 62.84 GB at Q4_K_M and 63.39 GB at Q8_0.

What does "not yet sourced" mean?

We only publish numbers we can point to. If a model's KV cache can't be worked out from its published config (Gemma 4 and Qwen3.8 Flash Next use attention tricks whose cache size isn't documented), the cell stays blank until we find a source.