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.
Runs great on a 24 GB GPU
Runs from 12 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~81 tok/s · 64K)Macs from 16 GB (Q4_K_M · ~17 tok/s)
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve DavidAU/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on Tesla V100 PCIe 16GB (5 GB headroom) · Q4_K_M
- Generation speed
- ~73 tok/s
- generation speed
- Cost per 1M output tokens
- $0.37
- per 1M output tokens
How Much VRAM Does Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 5.9 GB | 12.2 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 64K context · ~111 tok/s | |
| 6.7 GB | 13 GB | 3-bit medium — 3-bit medium quantization | Fits: 64K context · ~98 tok/s | |
| 8.1 GB | 14.4 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 64K context · ~81 tok/s | |
| 9.4 GB | 15.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 64K context · ~69 tok/s | |
| 10.8 GB | 17.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 64K context · ~61 tok/s | |
| 13.0 GB | 19.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~51 tok/s | |
| 25.2 GB | 31.5 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 28.2 GB |
Selected Q4_K_M
4-bit medium quantization11.0GBat 16K context
- Weights
- 7.4 GB
- KV cache
- 3.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 13.0 GB free · ~81 tok/s
≥50 tok/s needs ≥620 GB/s · RTX 5090 ~144, RTX 3090 Ti ~81
Which Hardware 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. 40 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti. 150 devices with unified memory can run Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
12 GB
12 GB tier →fits · 1.0 GB free · up to 16K ctx
10 GPUs
RTX 3080 Ti~74Simulate output on RTX 3080 TiRTX 5070~54Simulate output on RTX 5070RTX 4070~41Simulate output on RTX 407020 GB
fits · 9.0 GB free · up to 32K ctx
1 GPU
RX 7900 XT~60Simulate output on RX 7900 XT32–40 GB
fits · 21.0 GB free · up to 64K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~144Simulate output on RTX 5090M5 Max36 GB~40Simulate output on Mac Studio M5 Max (36 GB)M532 GB~13Simulate output on MacBook Air 13" M5 (32 GB)Orin~17Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~11Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC
Mistral family · 34 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Gemma 3 12B ITGoogle | 74.2 | 10.9 GB | ~82 | same size |
| Gemma 4 12BGoogle | — | 9 GB | ~80 | same size |
| Baichuan2 13B Basebaichuan-inc | — | 11.5 GB | ~76 | same size |
| Llama 2 13B Chat HFMeta | — | 11.5 GB | ~76 | same size |
| Vicuna 13B V1.3LMSYS | — | 8.6 GB | ~76 | same size |
| Vllm Translategemma 12B ITchbae624 | — | 9.8 GB | ~73 | same size |
Specifications
- Publisher
- DavidAU
- Family
- Mistral
- Parameters
- 12.2B
- Architecture
- MistralForCausalLM
- Context Length
- 1,024,000 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2026-01-09
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 210.1 GB (at full 1024K context)
VRAM usage by quantization
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
★ 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?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 8.1 GB of usable unified memory (RAM minus macOS overhead).
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. 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 ~595 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 ÷ 8.07 × 0.65 = ~644 tok/s
Estimated speed at Q4_K_M (8.1 GB)
Real-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?
40 consumer GPUs can run Mistral Nemo 2407 12B Thinking Claude Gemini GPT5.2 Uncensored HERETIC at Q4_K_M. 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?
150 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, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.