Mistral Small 24B Instruct 2501 — Hardware Requirements & GPU Compatibility
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Mistral Small 24B Instruct is Mistral AI's January 2025 release targeting the mid-range parameter sweet spot. At 24 billion parameters it sits between lightweight 7B models and heavier 70B-class offerings, delivering strong instruction-following, reasoning, and coding performance without demanding top-tier hardware. This model fits comfortably on a single GPU with 16–24 GB of VRAM at common quantization levels, making it an attractive option for users with cards like the RTX 4090 or RTX 3090 who want a noticeable step up from 7B models. It strikes an appealing balance between quality and resource requirements for serious local use.
56.8K downloads
957 likesSpecifications
- Publisher
- Mistral AI
- Family
- Mistral
- Parameters
- 23.6B
- Architecture
- MistralForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2025-01-28
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Mistral Small 24B Instruct 2501 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 10.7 GB | 17.0 GB | 10.02 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 11.0 GB | 17.3 GB | 10.31 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 12.2 GB | 18.5 GB | 11.49 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 12.5 GB | 18.8 GB | 11.79 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 14.9 GB | 21.1 GB | 14.14 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 17.5 GB | 23.8 GB | 16.80 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 20.2 GB | 26.5 GB | 19.45 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 24.3 GB | 30.6 GB | 23.57 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Mistral Small 24B Instruct 2501?
Q4_K_M · 14.9 GBMistral Small 24B Instruct 2501 (Q4_K_M) requires 14.9 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 21.1 GB. 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?
Q4_K_M · 14.9 GB47 devices with unified memory can run Mistral Small 24B Instruct 2501, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomWhere to Download Mistral Small 24B Instruct 2501
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Mistral Small 24B Instruct 2501 need?
Mistral Small 24B Instruct 2501 requires 14.9 GB of VRAM at Q4_K_M, or 47.9 GB at BF16. Full 33K context adds up to 6.3 GB (21.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 23.6B × 4.8 bits ÷ 8 = 14.1 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M14.9 GBQ4_K_M + full context21.1 GB- Can NVIDIA GeForce RTX 4090 run Mistral Small 24B Instruct 2501?
Yes, at Q6_K (20.2 GB) or lower. Higher quantizations like Q8_0 (24.3 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Mistral Small 24B Instruct 2501?
For Mistral Small 24B Instruct 2501, Q4_K_M (14.9 GB) offers the best balance of quality and VRAM usage. Q4_K_L (15.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 7.8 GB.
VRAM requirement by quantization
IQ2_XS7.8 GBQ3_K_S11.0 GBQ4_K_S14.0 GBQ4_K_M ★14.9 GBQ5_K_S16.9 GBBF1647.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Mistral Small 24B Instruct 2501 on a Mac?
Mistral Small 24B Instruct 2501 requires at least 7.8 GB at IQ2_XS, 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 locally?
Yes — Mistral Small 24B Instruct 2501 can run locally on consumer hardware. At Q4_K_M quantization it needs 14.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Mistral Small 24B Instruct 2501?
At Q4_K_M, Mistral Small 24B Instruct 2501 can reach ~296 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~44 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 ÷ 14.9 × 0.65 = ~350 tok/s
Estimated speed at Q4_K_M (14.9 GB)
~350 tok/s~44 tok/s~350 tok/s~296 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?
At Q4_K_M, the download is about 14.14 GB. The full-precision BF16 version is 47.14 GB. The smallest option (IQ2_XS) is 7.07 GB.
- Which GPUs can run Mistral Small 24B Instruct 2501?
26 consumer GPUs can run Mistral Small 24B Instruct 2501 at Q4_K_M (14.9 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 7 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Mistral Small 24B Instruct 2501?
49 devices with unified memory can run Mistral Small 24B Instruct 2501 at Q4_K_M (14.9 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.