Unsloth Q1_S 24B Parameters

VRAM Requirements for
DictaLM-3.0-24B-Thinking-GGUF Q1_S

To run DictaLM-3.0-24B-Thinking-GGUF locally at Q1_S quantization, you need at minimum 26.39 GB of GPU VRAM.

26.39 GB
Required VRAM
24 GB
File Size
8K tokens
Context Window
24B
Parameters
Estimated VRAM Required
26.39
GB
Prosumer / Workstation
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $1,200 – $1,800

2× RTX 3090 (48GB total)

48 GB

Dual-GPU via tensor parallelism. Best cost per GB at this tier.

Balanced $4,000 – $5,500

NVIDIA A6000 (48GB)

48 GB

Single-card 48GB pro GPU. Clean setup, no multi-GPU overhead.

Ultimate $8,000 – $12,000

NVIDIA A100 40GB SXM

40 GB HBM2e

Data-centre HBM2e bandwidth. Dramatically faster throughput.

📊 VRAM Calculation Breakdown

Model File Size (Q1_S) 24 GB
Context Overhead (8,192 tokens × 24B × 2 ÷ 1M) 0.393 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 26.39 GB

Try a Different Quantization

Use the interactive calculator to compare DictaLM-3.0-24B-Thinking-GGUF across all available formats.

Open Live Calculator →

DictaLM-3.0-24B-Thinking-GGUF — Other Quantizations

Q2_K 7.5 GB file
9.89 GB
Q2_K_L 7.5 GB file
9.89 GB
Q2_K_XL 7.5 GB file
9.89 GB
Q2_M 7.5 GB file
9.89 GB
Q2_XXS 7.5 GB file
9.89 GB
Q3_K_M 10.5 GB file
12.89 GB
Q3_K_S 10.5 GB file
12.89 GB
Q3_K_XL 10.5 GB file
12.89 GB
Q3_XXS 10.5 GB file
12.89 GB
Q4_XS 13.5 GB file
15.89 GB
Q4_NL 13.5 GB file
15.89 GB
Q4_K_XL 13.5 GB file
15.89 GB
Q4_K_S 13.5 GB file
15.89 GB
Q4_K_M 13.5 GB file
15.89 GB
Q4_1 13.5 GB file
15.89 GB
Q4_0 13.5 GB file
15.89 GB
Q5_K_M 16.5 GB file
18.89 GB
Q5_K_S 16.5 GB file
18.89 GB
Q5_K_XL 16.5 GB file
18.89 GB
Q6_K 19.5 GB file
21.89 GB
Q6_K_XL 19.5 GB file
21.89 GB
Q1_M 24 GB file
26.39 GB
Q1_S Current
26.39 GB
Q8_0 25.5 GB file
27.89 GB
Q8_K_XL 25.5 GB file
27.89 GB
BF16 48 GB file
50.39 GB
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Frequently Asked Questions

Can I run DictaLM-3.0-24B-Thinking-GGUF Q1_S on a consumer GPU?
Running DictaLM-3.0-24B-Thinking-GGUF Q1_S locally requires 26.39 GB VRAM, which exceeds consumer GPUs. You'll need prosumer cards like the NVIDIA A6000 (48GB) or an A100 (80GB).
What happens if I don't have enough VRAM?
If your GPU VRAM is insufficient, llama.cpp and similar tools will offload model layers to system RAM (CPU inference). This is much slower — expect 10-50× the generation latency compared to full GPU inference.
Can I use multiple GPUs to run DictaLM-3.0-24B-Thinking-GGUF?
Yes! Tools like llama.cpp, vLLM, and Ollama support tensor parallelism across multiple GPUs. For example, 2× RTX 3090 (24GB each) gives you 48GB total VRAM, which can run many large models.
Is Q1_S quality good enough for production?
Q1_S is suitable for specialized use cases. Check community benchmarks for specific quality metrics.