Mistral Small 24B Instruct 2501 GGUF — Hardware Requirements & GPU Compatibility
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12.4K downloads
27 likesSpecifications
- Publisher
- Unsloth
- Family
- Mistral
- Parameters
- 24B
- Architecture
- MistralForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 131,072
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Mistral Small 24B Instruct 2501 GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 10.9 GB | 17.2 GB | 10.20 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 12.4 GB | 18.7 GB | 11.70 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 15.1 GB | 21.4 GB | 14.40 GB | 4-bit medium quantization — most popular sweet spot |
| Q6_K | 6.60 | 20.5 GB | 26.8 GB | 19.80 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 24.7 GB | 31.0 GB | 24.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Mistral Small 24B Instruct 2501 GGUF?
Q4_K_M · 15.1 GBMistral Small 24B Instruct 2501 GGUF (Q4_K_M) requires 15.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 20+ GB is recommended. Using the full 33K context window can add up to 6.3 GB, bringing total usage to 21.4 GB. 17 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 GGUF?
Q4_K_M · 15.1 GB27 devices with unified memory can run Mistral Small 24B Instruct 2501 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 GGUF need?
Mistral Small 24B Instruct 2501 GGUF requires 15.1 GB of VRAM at Q4_K_M, or 24.7 GB at Q8_0. Full 33K context adds up to 6.3 GB (21.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 24B × 4.8 bits ÷ 8 = 14.4 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M15.1 GBQ4_K_M + full context21.4 GB- Can NVIDIA GeForce RTX 4090 run Mistral Small 24B Instruct 2501 GGUF?
Yes, at Q6_K (20.5 GB) or lower. Higher quantizations like Q8_0 (24.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Mistral Small 24B Instruct 2501 GGUF?
For Mistral Small 24B Instruct 2501 GGUF, Q4_K_M (15.1 GB) offers the best balance of quality and VRAM usage. Q6_K (20.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 10.9 GB.
VRAM requirement by quantization
Q2_K10.9 GB~75%Q3_K_M12.4 GB~83%Q4_K_M ★15.1 GB~89%Q6_K20.5 GB~95%Q8_024.7 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Mistral Small 24B Instruct 2501 GGUF on a Mac?
Mistral Small 24B Instruct 2501 GGUF requires at least 10.9 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 Small 24B Instruct 2501 GGUF locally?
Yes — Mistral Small 24B Instruct 2501 GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 15.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Mistral Small 24B Instruct 2501 GGUF?
At Q4_K_M, Mistral Small 24B Instruct 2501 GGUF can reach ~193 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~43 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 ÷ 15.1 × 0.55 = ~193 tok/s
Estimated speed at Q4_K_M (15.1 GB)
AMD Instinct MI300X~193 tok/sNVIDIA GeForce RTX 4090~43 tok/sNVIDIA H100 SXM~144 tok/sAMD Instinct MI250X~119 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 GGUF?
At Q4_K_M, the download is about 14.40 GB. The full-precision Q8_0 version is 24.00 GB. The smallest option (Q2_K) is 10.20 GB.
- Which GPUs can run Mistral Small 24B Instruct 2501 GGUF?
17 consumer GPUs can run Mistral Small 24B Instruct 2501 GGUF at Q4_K_M (15.1 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 5 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Mistral Small 24B Instruct 2501 GGUF?
27 devices with unified memory can run Mistral Small 24B Instruct 2501 GGUF at Q4_K_M (15.1 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.