Openai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF — Hardware Requirements & GPU Compatibility
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1.0K downloads
13 likesSpecifications
- Publisher
- DavidAU
- Family
- GPT-OSS
- Parameters
- 20B
- Release Date
- 2025-08-07
- License
- Apache 2.0
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How Much VRAM Does Openai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ4_NL | 4.50 | 12.4 GB | — | 11.25 GB | Importance-weighted 4-bit, non-linear |
Which GPUs Can Run Openai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF?
IQ4_NL · 12.4 GBOpenai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF (IQ4_NL) requires 12.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 17+ GB is recommended. 17 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 Openai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF?
IQ4_NL · 12.4 GB27 devices with unified memory can run Openai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF, 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 Openai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF need?
Openai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF requires 12.4 GB of VRAM at IQ4_NL.
VRAM = Weights + KV Cache + Overhead
Weights = 20B × 4.5 bits ÷ 8 = 11.3 GB
KV Cache + Overhead ≈ 1.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
IQ4_NL12.4 GB- Can I run Openai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF on a Mac?
Openai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF requires at least 12.4 GB at IQ4_NL, 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 Openai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF locally?
Yes — Openai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF can run locally on consumer hardware. At IQ4_NL quantization it needs 12.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Openai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF?
At IQ4_NL, Openai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF can reach ~236 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~53 tok/s. 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 ÷ 12.4 × 0.55 = ~236 tok/s
Estimated speed at IQ4_NL (12.4 GB)
AMD Instinct MI300X~236 tok/sNVIDIA GeForce RTX 4090~53 tok/sNVIDIA H100 SXM~176 tok/sAMD Instinct MI250X~146 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Openai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF?
At IQ4_NL, the download is about 11.25 GB.
- Which GPUs can run Openai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF?
17 consumer GPUs can run Openai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF at IQ4_NL (12.4 GB). Top options include AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, AMD Radeon RX 6800. 6 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Openai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF?
27 devices with unified memory can run Openai GPT OSS 20B CODER NEO CODE DI MATRIX GGUF at IQ4_NL (12.4 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.