Can my GPU run it? VRAM calculator
Pick a 2026 model, a quant and your GPU. We show the memory the weights need and whether they fit, with the math shown, then link real measured speeds.
The formula
weights (GB) = parameters (billions) × bits-per-weight ÷ 8fits if weights + headroom ≤ VRAMBits-per-weight for each GGUF quant are llama.cpp's own measured values (llama.cpp quantize README): Q8_0 8.50, Q6_K 6.56, Q5_K_M 5.70, Q4_K_M 4.89, Q3_K_M 4.00, Q2_K 3.16. Real files vary slightly by model. For MoE models all experts must sit in memory (or be offloaded to system RAM), but speed depends mostly on the active parameters.
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Referral link: RigBase may earn RunPod credit if you sign up. It never affects our data.
Weights size by quant (GB)
| Model | Params | Q6_K | Q5_K_M | Q4_K_M | Q3_K_M | Param source |
|---|---|---|---|---|---|---|
| Qwen3.8 27B | 27B | 22.2 | 19.2 | 16.5 | 13.5 | Ollama library tag 27b (nominal) |
| Qwen3.6 35B-A3B | 35.51B (3B active) | 29.1 | 25.3 | 21.7 | 17.7 | GGUF param count via llamabench.ai; 3B active per name |
| Qwen3.6 27B | 27.32B | 22.4 | 19.5 | 16.7 | 13.6 | GGUF param count via llamabench.ai |
| Gemma 4 31B | 30.7B | 25.2 | 21.9 | 18.8 | 15.3 | GGUF param count via llamabench.ai |
| Gemma 4 26B-A4B | 25.23B (4B active) | 20.7 | 18.0 | 15.4 | 12.6 | GGUF param count via llamabench.ai |
| Gemma 4 12B | 11.91B | 9.8 | 8.5 | 7.3 | 5.9 | GGUF param count via llamabench.ai |
| gpt-oss 20B | 20.91B (3.6B active) | 17.2 | 14.9 | 12.8 | 10.4 | GGUF param count via llamabench.ai (OpenAI model card: 21B total, 3.6B active) |
| gpt-oss 120B | 117B (5.1B active) | 96.0 | 83.4 | 71.6 | 58.4 | OpenAI model card: 117B total, 5.1B active |
| Qwen3.5 9B | 8.95B | 7.3 | 6.4 | 5.5 | 4.5 | GGUF param count via llamabench.ai |
| Qwen3.5 122B | 122B | 100.1 | 87.0 | 74.6 | 60.9 | Ollama library tag 122b (nominal) |
| LFM2.5 24B-A2B | 23.97B (2.45B active) | 19.7 | 17.1 | 14.7 | 12.0 | via llamabench.ai |
| Nemotron 3.5 Bolt 74B-A9B | 74.04B (9B active) | 60.7 | 52.8 | 45.3 | 37.0 | via llamabench.ai |
| Nemotron 3 Super 120B-A12B | 120B (12B active) | 98.4 | 85.6 | 73.4 | 59.9 | Ollama library: 120B, 12B active |
| Mistral Medium 3.5 128B | 128B | 105.0 | 91.3 | 78.3 | 63.9 | Ollama library: 128B weights |
| Devstral Small 2 24B | 24B | 19.7 | 17.1 | 14.7 | 12.0 | Ollama library tag 24b |
| Muse Spark Open 816B-A42B | 816B (42B active) | 669.5 | 581.8 | 499.2 | 407.6 | Hugging Face launch post: ~816B total, ~42B active |
| GLM-5.3 | 753B | 617.8 | 536.9 | 460.7 | 376.1 | Hugging Face listing: 753B |
| DeepSeek R1 671B | 671B (37B active) | 550.5 | 478.4 | 410.5 | 335.2 | Ollama library: 671B total, 37B active (V3 base) |
| Llama 3.3 70B (older) | 70B | 57.4 | 49.9 | 42.8 | 35.0 | Ollama library tag 70b |
| Llama 3.1 8B (older) | 8B | 6.6 | 5.7 | 4.9 | 4.0 | Ollama library tag 8b |
gpt-oss ships natively in MXFP4; OpenAI's card says gpt-oss-120b runs on a single 80 GB GPU and gpt-oss-20b within 16 GB of memory.
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