Mistral Small 24B Instruct 2501 Q2 K GGUF — Hardware Requirements & GPU Compatibility
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16 downloads
2 likesSpecifications
- Publisher
- NikolayKozloff
- Family
- Mistral
- Parameters
- 24B
- License
- Apache 2.0
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How Much VRAM Does Mistral Small 24B Instruct 2501 Q2 K GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 11.2 GB | — | 10.20 GB | 2-bit quantization with K-quant improvements |
Which GPUs Can Run Mistral Small 24B Instruct 2501 Q2 K GGUF?
Q2_K · 11.2 GBMistral Small 24B Instruct 2501 Q2 K GGUF (Q2_K) requires 11.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 15+ GB is recommended. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Mistral Small 24B Instruct 2501 Q2 K GGUF?
Q2_K · 11.2 GB27 devices with unified memory can run Mistral Small 24B Instruct 2501 Q2 K GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Mistral Small 24B Instruct 2501 Q2 K GGUF need?
Mistral Small 24B Instruct 2501 Q2 K GGUF requires 11.2 GB of VRAM at Q2_K.
VRAM = Weights + KV Cache + Overhead
Weights = 24B × 3.4 bits ÷ 8 = 10.2 GB
KV Cache + Overhead ≈ 1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q2_K11.2 GB- Can I run Mistral Small 24B Instruct 2501 Q2 K GGUF on a Mac?
Mistral Small 24B Instruct 2501 Q2 K GGUF requires at least 11.2 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 Small 24B Instruct 2501 Q2 K GGUF locally?
Yes — Mistral Small 24B Instruct 2501 Q2 K GGUF can run locally on consumer hardware. At Q2_K quantization it needs 11.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Mistral Small 24B Instruct 2501 Q2 K GGUF?
At Q2_K, Mistral Small 24B Instruct 2501 Q2 K GGUF can reach ~260 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~58 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 11.2 × 0.55 = ~260 tok/s
Estimated speed at Q2_K (11.2 GB)
AMD Instinct MI300X~260 tok/sNVIDIA GeForce RTX 4090~58 tok/sNVIDIA H100 SXM~194 tok/sAMD Instinct MI250X~161 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Mistral Small 24B Instruct 2501 Q2 K GGUF?
At Q2_K, the download is about 10.20 GB.
- Which GPUs can run Mistral Small 24B Instruct 2501 Q2 K GGUF?
26 consumer GPUs can run Mistral Small 24B Instruct 2501 Q2 K GGUF at Q2_K (11.2 GB). Top options include AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, AMD Radeon RX 6700 XT. 6 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Mistral Small 24B Instruct 2501 Q2 K GGUF?
27 devices with unified memory can run Mistral Small 24B Instruct 2501 Q2 K GGUF at Q2_K (11.2 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.