Mistral Small 24B Instruct 2501 Writer — Hardware Requirements & GPU Compatibility
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Mistral Small 24B Instruct 2501 Writer is a 23.6B-parameter open language model from lars1234 in the Mistral family. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 14.86 GB of VRAM — see which GPUs and Macs can run it below.
7 downloads
19 likesSpecifications
- Publisher
- lars1234
- Family
- Mistral
- Parameters
- 23.6B
- Architecture
- MistralForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2025-03-06
- License
- Apache 2.0
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How Much VRAM Does Mistral Small 24B Instruct 2501 Writer 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 |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Mistral Small 24B Instruct 2501 Writer?
Q4_K_M · 14.9 GBMistral Small 24B Instruct 2501 Writer (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 Writer?
Q4_K_M · 14.9 GB47 devices with unified memory can run Mistral Small 24B Instruct 2501 Writer, 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 Writer
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 Writer need?
Mistral Small 24B Instruct 2501 Writer requires 14.9 GB of VRAM at Q4_K_M, or 47.9 GB at FP16. 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 Writer?
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 Writer?
For Mistral Small 24B Instruct 2501 Writer, 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 GBIQ4_XS13.4 GBQ4_K_M ★14.9 GBQ5_K_S16.9 GBFP1647.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Mistral Small 24B Instruct 2501 Writer on a Mac?
Mistral Small 24B Instruct 2501 Writer 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 Writer locally?
Yes — Mistral Small 24B Instruct 2501 Writer 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 Writer?
At Q4_K_M, Mistral Small 24B Instruct 2501 Writer 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 Writer?
At Q4_K_M, the download is about 14.14 GB. The full-precision FP16 version is 47.14 GB. The smallest option (IQ2_XS) is 7.07 GB.
- Which GPUs can run Mistral Small 24B Instruct 2501 Writer?
26 consumer GPUs can run Mistral Small 24B Instruct 2501 Writer 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 Writer?
49 devices with unified memory can run Mistral Small 24B Instruct 2501 Writer 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.