Mistral Small 24B Instruct 2501 Q8 0 GGUF — Hardware Requirements & GPU Compatibility
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9 downloads
2 likesSpecifications
- Publisher
- Karsh-CAI
- Family
- Mistral
- Parameters
- 24B
- License
- Apache 2.0
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How Much VRAM Does Mistral Small 24B Instruct 2501 Q8 0 GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q8_0 | 8.00 | 26.4 GB | — | 24.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Mistral Small 24B Instruct 2501 Q8 0 GGUF?
Q8_0 · 26.4 GBMistral Small 24B Instruct 2501 Q8 0 GGUF (Q8_0) requires 26.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 35+ GB is recommended. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run Mistral Small 24B Instruct 2501 Q8 0 GGUF?
Q8_0 · 26.4 GB15 devices with unified memory can run Mistral Small 24B Instruct 2501 Q8 0 GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Mistral Small 24B Instruct 2501 Q8 0 GGUF need?
Mistral Small 24B Instruct 2501 Q8 0 GGUF requires 26.4 GB of VRAM at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 24B × 8 bits ÷ 8 = 24 GB
KV Cache + Overhead ≈ 2.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q8_026.4 GB- Can I run Mistral Small 24B Instruct 2501 Q8 0 GGUF on a Mac?
Mistral Small 24B Instruct 2501 Q8 0 GGUF requires at least 26.4 GB at Q8_0, 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 Q8 0 GGUF locally?
Yes — Mistral Small 24B Instruct 2501 Q8 0 GGUF can run locally on consumer hardware. At Q8_0 quantization it needs 26.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Mistral Small 24B Instruct 2501 Q8 0 GGUF?
At Q8_0, Mistral Small 24B Instruct 2501 Q8 0 GGUF can reach ~110 tok/s on AMD Instinct MI300X. 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 ÷ 26.4 × 0.55 = ~110 tok/s
Estimated speed at Q8_0 (26.4 GB)
AMD Instinct MI300X~110 tok/sNVIDIA H100 SXM~83 tok/sAMD Instinct MI250X~68 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 Q8 0 GGUF?
At Q8_0, the download is about 24.00 GB.
- Which GPUs can run Mistral Small 24B Instruct 2501 Q8 0 GGUF?
1 consumer GPU can run Mistral Small 24B Instruct 2501 Q8 0 GGUF at Q8_0 (26.4 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run Mistral Small 24B Instruct 2501 Q8 0 GGUF?
15 devices with unified memory can run Mistral Small 24B Instruct 2501 Q8 0 GGUF at Q8_0 (26.4 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.