GPT OSS 120B Eagle3 v3 — Hardware Requirements & GPU Compatibility
Chat
GPT OSS 120B Eagle3 v3 is a 120B-parameter open language model from NVIDIA in the GPT-OSS family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 72.30 GB of VRAM — see which GPUs and Macs can run it below.
53.3K downloads
13 likesSpecifications
- Publisher
- NVIDIA
- Family
- GPT-OSS
- Parameters
- 120B
- Architecture
- LlamaForCausalLMEagle3
- Context Length
- 131,072 tokens
- Vocabulary Size
- 201,088
- Release Date
- 2026-03-28
- License
- Other
Get Started
HuggingFace
How Much VRAM Does GPT OSS 120B Eagle3 v3 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 51.3 GB | 51.5 GB | 51.00 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 58.8 GB | 59.0 GB | 58.50 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 72.3 GB | 72.5 GB | 72.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 85.8 GB | 86.0 GB | 85.50 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 99.3 GB | 99.5 GB | 99.00 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 120.3 GB | 120.5 GB | 120.00 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 240.3 GB | 240.5 GB | 240.00 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run GPT OSS 120B Eagle3 v3?
Q4_K_M · 72.3 GBGPT OSS 120B Eagle3 v3 (Q4_K_M) requires 72.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 94+ GB is recommended. Using the full 131K context window can add up to 0.2 GB, bringing total usage to 72.5 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run GPT OSS 120B Eagle3 v3?
Q4_K_M · 72.3 GB18 devices with unified memory can run GPT OSS 120B Eagle3 v3, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does GPT OSS 120B Eagle3 v3 need?
GPT OSS 120B Eagle3 v3 requires 72.3 GB of VRAM at Q4_K_M, or 240.3 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 120B × 4.8 bits ÷ 8 = 72 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.5 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M72.3 GBQ4_K_M + full context72.5 GB- Can NVIDIA GeForce RTX 5090 run GPT OSS 120B Eagle3 v3?
No — GPT OSS 120B Eagle3 v3 requires at least 51.3 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for GPT OSS 120B Eagle3 v3?
For GPT OSS 120B Eagle3 v3, Q4_K_M (72.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (85.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 51.3 GB.
VRAM requirement by quantization
Q2_K51.3 GBQ4_K_M ★72.3 GBQ5_K_M85.8 GBQ6_K99.3 GBQ8_0120.3 GBBF16240.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run GPT OSS 120B Eagle3 v3 on a Mac?
GPT OSS 120B Eagle3 v3 requires at least 51.3 GB at Q2_K, 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 120B Eagle3 v3 locally?
Yes — GPT OSS 120B Eagle3 v3 can run locally on consumer hardware. At Q4_K_M quantization it needs 72.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is GPT OSS 120B Eagle3 v3?
At Q4_K_M, GPT OSS 120B Eagle3 v3 can reach ~66 tok/s on AMD Instinct MI350X. 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 ÷ 72.3 × 0.65 = ~72 tok/s
Estimated speed at Q4_K_M (72.3 GB)
~72 tok/s~72 tok/s~66 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 120B Eagle3 v3?
At Q4_K_M, the download is about 72.00 GB. The full-precision BF16 version is 240.00 GB. The smallest option (Q2_K) is 51.00 GB.
- Which GPUs can run GPT OSS 120B Eagle3 v3?
No single consumer GPU has enough VRAM to run GPT OSS 120B Eagle3 v3 at Q4_K_M (72.3 GB). Multi-GPU or professional hardware is required.
- Which devices can run GPT OSS 120B Eagle3 v3?
19 devices with unified memory can run GPT OSS 120B Eagle3 v3 at Q4_K_M (72.3 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.