Mistral Small 24B Instruct 2501 Quantized.w8a8 — Hardware Requirements & GPU Compatibility
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Mistral Small 24B Instruct 2501 Quantized.w8a8 is a 23.6B-parameter open language model from RedHatAI 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.
15.7K downloads
2 likesSpecifications
- Publisher
- RedHatAI
- Family
- Mistral
- Parameters
- 23.6B
- Architecture
- MistralForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2025-03-03
- License
- Apache 2.0
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How Much VRAM Does Mistral Small 24B Instruct 2501 Quantized.w8a8 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 10.7 GB | 17.0 GB | 10.02 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 12.2 GB | 18.5 GB | 11.49 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 14.9 GB | 21.2 GB | 14.15 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 17.5 GB | 23.8 GB | 16.80 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 20.2 GB | 26.5 GB | 19.45 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 24.3 GB | 30.6 GB | 23.58 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 47.9 GB | 54.2 GB | 47.15 GB | Brain floating point 16 — preferred for training |
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 Quantized.w8a8?
Q4_K_M · 14.9 GBMistral Small 24B Instruct 2501 Quantized.w8a8 (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 21.2 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 Quantized.w8a8?
Q4_K_M · 14.9 GB47 devices with unified memory can run Mistral Small 24B Instruct 2501 Quantized.w8a8, 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 Quantized.w8a8 need?
Mistral Small 24B Instruct 2501 Quantized.w8a8 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 (21.2 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 ≈ 7.1 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M14.9 GBQ4_K_M + full context21.2 GB- Can NVIDIA GeForce RTX 4090 run Mistral Small 24B Instruct 2501 Quantized.w8a8?
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 Quantized.w8a8?
For Mistral Small 24B Instruct 2501 Quantized.w8a8, Q4_K_M (14.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (17.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 10.7 GB.
VRAM requirement by quantization
Q2_K10.7 GBQ4_K_M ★14.9 GBQ5_K_M17.5 GBQ6_K20.2 GBQ8_024.3 GBBF1647.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Mistral Small 24B Instruct 2501 Quantized.w8a8 on a Mac?
Mistral Small 24B Instruct 2501 Quantized.w8a8 requires at least 10.7 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 Quantized.w8a8 locally?
Yes — Mistral Small 24B Instruct 2501 Quantized.w8a8 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 Quantized.w8a8?
At Q4_K_M, Mistral Small 24B Instruct 2501 Quantized.w8a8 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 Quantized.w8a8?
At Q4_K_M, the download is about 14.15 GB. The full-precision BF16 version is 47.15 GB. The smallest option (Q2_K) is 10.02 GB.
- Which GPUs can run Mistral Small 24B Instruct 2501 Quantized.w8a8?
26 consumer GPUs can run Mistral Small 24B Instruct 2501 Quantized.w8a8 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 Quantized.w8a8?
49 devices with unified memory can run Mistral Small 24B Instruct 2501 Quantized.w8a8 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.