GPT OSS 120B Abliterated — Hardware Requirements & GPU Compatibility
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GPT OSS 120B Abliterated is a 116.8B-parameter open language model from wangzhang in the GPT-OSS family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 70.50 GB of VRAM — see which GPUs and Macs can run it below.
GPT OSS 120B Abliterated runs on a 96 GB+ Mac or a workstation GPU
Best local pick → Mac Studio M5 Ultra (96 GB) · Q4_K_M · 70.8 GB · ~89 tok/s
Macs from 48 GB (IQ2_M · ~85 tok/s)
Apache 2.0
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run GPT OSS 120B Abliterated
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/mradermacher/gpt-oss-120b-abliterated-i1-GGUF:Q4_K_MQ4_K_M · needs ~70.8 GB · 16K context
Download: gpt-oss-120b-abliterated.i1-Q4_K_M.gguf87.85 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 96 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
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Runs on RTX PRO 6000 (96 GB) (25 GB headroom) · Q4_K_M
- Generation speed
- ~202 tok/s
- generation speed
- Cost per 1M output tokens
- $1.57
- per 1M output tokens
How Much VRAM Does GPT OSS 120B Abliterated Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 50.1 GB | 50.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Does not fit: needs 50.4 GB | |
| 51.5 GB | 52.3 GB | 3-bit small — 3-bit small quantization | Does not fit: needs 51.8 GB | |
| 57.4 GB | 58.1 GB | 3-bit medium — 3-bit medium quantization | Does not fit: needs 57.7 GB | |
| 58.8 GB | 59.6 GB | 4-bit · legacy — 4-bit legacy quantization | Does not fit: needs 59.1 GB | |
| 70.5 GB | 71.3 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 70.8 GB | |
| 83.7 GB | 84.4 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 84 GB | |
| 96.8 GB | 97.5 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 97.1 GB | |
| 117.2 GB | 118 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 117.6 GB |
Selected Q4_K_M
4-bit medium quantization70.8GBat 16K context
- Weights
- 70.1 GB
- KV cache
- 0.4 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 46.8 GB more
Or .
Which Hardware Can Run GPT OSS 120B Abliterated?
Q4_K_M · 70.5 GBGPT OSS 120B Abliterated (Q4_K_M) requires 70.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 92+ GB is recommended. Using the full 131K context window can add up to 3.3 GB, bringing total usage to 73.8 GB. No consumer GPU has enough memory. 34 devices with unified memory can run GPT OSS 120B Abliterated, including Mac Studio M5 Ultra (256 GB), Mac Studio M5 Ultra (512 GB), Mac Studio M5 Ultra (96 GB).
64–80 GB
Q4_K_M · best fit IQ4_XS (63.5 GB)
1 device family
Orin~39Simulate output on NVIDIA Jetson AGX Orin 64GB96 GB
fits · 25.2 GB free · up to 128K ctx
2 Mac classes
M5 Ultra96 GB~89Simulate output on Mac Studio M5 Ultra (96 GB)M3 Max*96 GB~42Simulate output on MacBook Pro 14" M3 Max (96 GB)
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to GPT OSS 120B Abliterated
GPT-OSS family · 14 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| GLM 4.5 AirZ.ai | 98.4 | 67.6 GB | —needs > 24 GB | same size |
| Llama 4 Scout 17B 16E InstructMeta | 82.1 | 77 GB | —needs > 24 GB | same size |
| Ornith 1.5 122Bornith-ai | — | 75.5 GB | —needs > 24 GB | same size |
| NVIDIA Nemotron 3.5 Super VL 120B A12B BF16NVIDIA | — | 87.7 GB | —needs > 24 GB | same size |
| Ling 3.0 Flash VLInclusion AI | — | 76 GB | —needs > 24 GB | same size |
| Leanstral 1.5 119B A6BMistral AI | — | 84 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- wangzhang
- Family
- GPT-OSS
- Parameters
- 116.8B
- Active parameters
- 5.8B per token
- Architecture
- GptOssForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 201,088
- Release Date
- 2026-04-21
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does GPT OSS 120B Abliterated need?
GPT OSS 120B Abliterated requires 70.5 GB of VRAM at Q4_K_M, or 234.1 GB at BF16. Full 131K context adds up to 3.3 GB (73.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 116.8B × 4.8 bits ÷ 8 = 70.1 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 3.7 GB (at full 131K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run GPT OSS 120B Abliterated?
No — GPT OSS 120B Abliterated requires at least 32.5 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
What's the best quantization for GPT OSS 120B Abliterated?
For GPT OSS 120B Abliterated, Q4_K_M (70.5 GB) offers the best balance of quality and VRAM usage. Q5_K_S (80.7 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 32.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run GPT OSS 120B Abliterated on a Mac?
Yes — Mac Studio M5 Max (48 GB) and 58 other Macs can run GPT OSS 120B Abliterated, the smallest at IQ2_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 70.5 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio M5 Ultra (96 GB).
Can I run GPT OSS 120B Abliterated locally?
Yes — GPT OSS 120B Abliterated can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is GPT OSS 120B Abliterated?
At Q4_K_M, GPT OSS 120B Abliterated can reach ~126 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 3.5 GB active ÷ (8000 × 0.65) = 0.667 ms, plus 36 layers × 0.055 ms = 1.980 ms, so 1000 ÷ 2.647 ms = ~378 tok/s
Estimated speed at Q4_K_M (70.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of GPT OSS 120B Abliterated?
At Q4_K_M, the download is about 70.10 GB. The full-precision BF16 version is 233.66 GB. The smallest option (IQ2_XXS) is 32.13 GB.
Which GPUs can run GPT OSS 120B Abliterated?
No single consumer GPU has enough VRAM to run GPT OSS 120B Abliterated at Q4_K_M (70.5 GB). Multi-GPU or professional hardware is required.
Which devices can run GPT OSS 120B Abliterated?
34 devices with unified memory can run GPT OSS 120B Abliterated at Q4_K_M (70.5 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (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.