Mistral Small 3.1 24B Instruct 2503 — Hardware Requirements & GPU Compatibility
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Mistral Small 3.1 24B Instruct 2503 is a 24-billion-parameter model from Mistral AI, the French AI lab, built on the earlier text-only Mistral Small 3 with added support for image input alongside text. It can reason about images in the same conversation as written prompts, useful for document understanding and multimodal chat. At 24 billion parameters, it needs quantization and a single high-end 24GB-class consumer or workstation GPU for local inference rather than budget hardware. The model supports a 128K token context window for long documents or extended conversations. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use. Published in March 2025, it added vision understanding and a longer context window to the earlier text-only Mistral Small while keeping the same 24B parameter budget.
483.4K downloads
1.4K likesSpecifications
- Publisher
- Mistral AI
- Family
- Mistral
- Parameters
- 24.0B
- Architecture
- Mistral3ForConditionalGeneration
- Context Length
- 131,072 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2025-03-11
- License
- Apache 2.0
Get Started
How Much VRAM Does Mistral Small 3.1 24B Instruct 2503 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 10.9 GB | 37.4 GB | 10.20 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 11.2 GB | 37.6 GB | 10.50 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 12.4 GB | 38.9 GB | 11.71 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 12.7 GB | 39.1 GB | 12.01 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 15.1 GB | 41.5 GB | 14.41 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 17.8 GB | 44.3 GB | 17.11 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 20.5 GB | 47.0 GB | 19.81 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 24.7 GB | 51.1 GB | 24.01 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Mistral Small 3.1 24B Instruct 2503?
Q4_K_M · 15.1 GBMistral Small 3.1 24B Instruct 2503 (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 131K context window can add up to 26.4 GB, bringing total usage to 41.5 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 3.1 24B Instruct 2503?
Q4_K_M · 15.1 GB47 devices with unified memory can run Mistral Small 3.1 24B Instruct 2503, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomWhere to Download Mistral Small 3.1 24B Instruct 2503
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Mistral Small 3.1 24B Instruct 2503 need?
Mistral Small 3.1 24B Instruct 2503 requires 15.1 GB of VRAM at Q4_K_M, or 48.7 GB at BF16. Full 131K context adds up to 26.4 GB (41.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 24.0B × 4.8 bits ÷ 8 = 14.4 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 27.2 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M15.1 GBQ4_K_M + full context41.5 GB- Can NVIDIA GeForce RTX 4090 run Mistral Small 3.1 24B Instruct 2503?
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 3.1 24B Instruct 2503?
For Mistral Small 3.1 24B Instruct 2503, Q4_K_M (15.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (17.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 7.3 GB.
VRAM requirement by quantization
IQ2_XXS7.3 GBQ3_K_S11.2 GBQ4_114.2 GBQ4_K_M ★15.1 GBQ5_K_S17.2 GBBF1648.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Mistral Small 3.1 24B Instruct 2503 on a Mac?
Mistral Small 3.1 24B Instruct 2503 requires at least 7.3 GB at IQ2_XXS, 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 3.1 24B Instruct 2503 locally?
Yes — Mistral Small 3.1 24B Instruct 2503 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 3.1 24B Instruct 2503?
At Q4_K_M, Mistral Small 3.1 24B Instruct 2503 can reach ~317 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 15.1 × 0.65 = ~344 tok/s
Estimated speed at Q4_K_M (15.1 GB)
~344 tok/s~43 tok/s~344 tok/s~317 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 3.1 24B Instruct 2503?
At Q4_K_M, the download is about 14.41 GB. The full-precision BF16 version is 48.02 GB. The smallest option (IQ2_XXS) is 6.60 GB.
- Which GPUs can run Mistral Small 3.1 24B Instruct 2503?
26 consumer GPUs can run Mistral Small 3.1 24B Instruct 2503 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. 7 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Mistral Small 3.1 24B Instruct 2503?
49 devices with unified memory can run Mistral Small 3.1 24B Instruct 2503 at Q4_K_M (15.1 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.