Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC — Hardware Requirements & GPU Compatibility
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Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC is a 12.2B-parameter open language model from DavidAU in the Mistral family. It supports a context window of up to 1,024,000 tokens. At Q4_K_M it needs about 8.07 GB of VRAM — see which GPUs and Macs can run it below.
3.5K downloads
33 likesSpecifications
- Publisher
- DavidAU
- Family
- Mistral
- Parameters
- 12.2B
- Architecture
- MistralForCausalLM
- Context Length
- 1,024,000 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2026-01-09
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How Much VRAM Does Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 5.9 GB | 215.2 GB | 5.21 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 6.7 GB | 216.0 GB | 5.97 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 8.1 GB | 217.4 GB | 7.35 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 9.4 GB | 218.7 GB | 8.73 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 10.8 GB | 220.1 GB | 10.10 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 13.0 GB | 222.3 GB | 12.25 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 25.2 GB | 234.5 GB | 24.50 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC?
Q4_K_M · 8.1 GBMistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC (Q4_K_M) requires 8.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 11+ GB is recommended. Using the full 1024K context window can add up to 209.3 GB, bringing total usage to 217.4 GB. 39 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC?
Q4_K_M · 8.1 GB49 devices with unified memory can run Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPad Pro M5 13" (16 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC need?
Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC requires 8.1 GB of VRAM at Q4_K_M, or 25.2 GB at BF16. Full 1024K context adds up to 209.3 GB (217.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 12.2B × 4.8 bits ÷ 8 = 7.3 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 210.1 GB (at full 1024K context)
VRAM usage by quantization
Q4_K_M8.1 GBQ4_K_M + full context217.4 GB- Can NVIDIA GeForce RTX 4090 run Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC?
Yes, at Q8_0 (13.0 GB) or lower. Higher quantizations like BF16 (25.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC?
For Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC, Q4_K_M (8.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (9.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 5.9 GB.
VRAM requirement by quantization
Q2_K5.9 GBQ4_K_M ★8.1 GBQ5_K_M9.4 GBQ6_K10.8 GBQ8_013.0 GBBF1625.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC on a Mac?
Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC requires at least 5.9 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC locally?
Yes — Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC can run locally on consumer hardware. At Q4_K_M quantization it needs 8.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC?
At Q4_K_M, Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC can reach ~545 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~81 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 8.1 × 0.65 = ~644 tok/s
Estimated speed at Q4_K_M (8.1 GB)
~644 tok/s~81 tok/s~644 tok/s~545 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC?
At Q4_K_M, the download is about 7.35 GB. The full-precision BF16 version is 24.50 GB. The smallest option (Q2_K) is 5.21 GB.
- Which GPUs can run Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC?
39 consumer GPUs can run Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC at Q4_K_M (8.1 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC?
52 devices with unified memory can run Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC at Q4_K_M (8.1 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.