GPT OSS 20B RichardErkhov Heresy — Hardware Requirements & GPU Compatibility
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GPT OSS 20B RichardErkhov Heresy is a 21.5B-parameter open language model from MuXodious in the GPT-OSS family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 13.28 GB of VRAM — see which GPUs and Macs can run it below.
66.0K downloads
27 likesSpecifications
- Publisher
- MuXodious
- Family
- GPT-OSS
- Parameters
- 21.5B
- Architecture
- GptOssForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 201,088
- Release Date
- 2026-02-07
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does GPT OSS 20B RichardErkhov Heresy Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 9.5 GB | 14.0 GB | 9.14 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 9.8 GB | 14.2 GB | 9.41 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 10.9 GB | 15.3 GB | 10.49 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 11.1 GB | 15.6 GB | 10.76 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 13.3 GB | 17.7 GB | 12.91 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 15.7 GB | 20.2 GB | 15.33 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 18.1 GB | 22.6 GB | 17.75 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 21.9 GB | 26.3 GB | 21.51 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run GPT OSS 20B RichardErkhov Heresy?
Q4_K_M · 13.3 GBGPT OSS 20B RichardErkhov Heresy (Q4_K_M) requires 13.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 18+ GB is recommended. Using the full 131K context window can add up to 4.5 GB, bringing total usage to 17.7 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 GPT OSS 20B RichardErkhov Heresy?
Q4_K_M · 13.3 GB47 devices with unified memory can run GPT OSS 20B RichardErkhov Heresy, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomWhere to Download GPT OSS 20B RichardErkhov Heresy
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does GPT OSS 20B RichardErkhov Heresy need?
GPT OSS 20B RichardErkhov Heresy requires 13.3 GB of VRAM at Q4_K_M, or 43.4 GB at BF16. Full 131K context adds up to 4.5 GB (17.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 21.5B × 4.8 bits ÷ 8 = 12.9 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.8 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M13.3 GBQ4_K_M + full context17.7 GB- Can NVIDIA GeForce RTX 4090 run GPT OSS 20B RichardErkhov Heresy?
Yes, at Q8_0 (21.9 GB) or lower. Higher quantizations like BF16 (43.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for GPT OSS 20B RichardErkhov Heresy?
For GPT OSS 20B RichardErkhov Heresy, Q4_K_M (13.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (15.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 6.3 GB.
VRAM requirement by quantization
IQ2_XXS6.3 GBIQ3_XS9.2 GBQ3_K_M10.9 GBQ4_K_M ★13.3 GBQ5_K_S15.2 GBBF1643.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run GPT OSS 20B RichardErkhov Heresy on a Mac?
GPT OSS 20B RichardErkhov Heresy requires at least 6.3 GB at IQ2_XXS, 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 GPT OSS 20B RichardErkhov Heresy locally?
Yes — GPT OSS 20B RichardErkhov Heresy can run locally on consumer hardware. At Q4_K_M quantization it needs 13.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is GPT OSS 20B RichardErkhov Heresy?
At Q4_K_M, GPT OSS 20B RichardErkhov Heresy can reach ~185 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~171 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 ÷ 13.3 × 0.65 = ~530 tok/s
Estimated speed at Q4_K_M (13.3 GB)
~530 tok/s~171 tok/s~530 tok/s~445 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of GPT OSS 20B RichardErkhov Heresy?
At Q4_K_M, the download is about 12.91 GB. The full-precision BF16 version is 43.02 GB. The smallest option (IQ2_XXS) is 5.92 GB.
- Which GPUs can run GPT OSS 20B RichardErkhov Heresy?
26 consumer GPUs can run GPT OSS 20B RichardErkhov Heresy at Q4_K_M (13.3 GB). Top options include AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, AMD Radeon RX 6800. 8 GPUs have plenty of headroom for comfortable inference.
- Which devices can run GPT OSS 20B RichardErkhov Heresy?
49 devices with unified memory can run GPT OSS 20B RichardErkhov Heresy at Q4_K_M (13.3 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.