GPT OSS 120B DFlash — Hardware Requirements & GPU Compatibility
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GPT OSS 120B DFlash is a 120B-parameter open language model from z-lab in the GPT-OSS family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 72.32 GB of VRAM — see which GPUs and Macs can run it below.
GPT OSS 120B DFlash runs on a 96 GB+ Mac or a workstation GPU
Best local pick → Mac Studio (M3 Ultra, 96GB) · Q4_K_M · 72.5 GB · ~8 tok/s
Macs from 64 GB (Q3_K_M · ~10 tok/s)
MIT
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run GPT OSS 120B DFlash
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve z-lab/gpt-oss-120b-DFlash --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX PRO 6000 (96 GB) (23 GB headroom) · Q4_K_M
- Generation speed
- ~16 tok/s
- generation speed
- Cost per 1M output tokens
- $23.04
- per 1M output tokens
How Much VRAM Does GPT OSS 120B DFlash Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 51.3 GB | 51.7 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Does not fit: needs 51.5 GB | |
| 58.8 GB | 59.2 GB | 3-bit medium — 3-bit medium quantization | Does not fit: needs 59 GB | |
| 72.3 GB | 72.7 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 72.5 GB | |
| 85.8 GB | 86.2 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 86 GB | |
| 99.3 GB | 99.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 99.5 GB | |
| 120.3 GB | 120.7 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 120.5 GB | |
| 240.3 GB | 240.7 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 240.5 GB |
Selected Q4_K_M
4-bit medium quantization72.5GBat 16K context
- Weights
- 72.0 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 48.5 GB more
Which Hardware Can Run GPT OSS 120B DFlash?
Q4_K_M · 72.3 GBGPT OSS 120B DFlash (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, 95+ GB is recommended. Using the full 131K context window can add up to 1.5 GB, bringing total usage to 73.8 GB. No consumer GPU has enough memory. 30 devices with unified memory can run GPT OSS 120B DFlash, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio (M3 Ultra, 96GB).
64–80 GB
Q4_K_M · best fit Q3_K_M (59.0 GB)
3 Mac classes · 1 device family
M2 Ultra*64 GB~10Simulate output on Mac Pro M2 Ultra (64 GB)M5 Max64 GB~7Simulate output on Mac Studio M5 Max (64 GB)Orin~2Simulate output on NVIDIA Jetson AGX Orin 64GB96 GB
fits · 23.5 GB free · up to 128K ctx
2 Mac classes
M3 Ultra*96 GB~8Simulate output on Mac Studio (M3 Ultra, 96GB)M3 Max*96 GB~3Simulate 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 DFlash
GPT-OSS family · 14 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| 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 |
| Qwen1.5 110BAlibaba | — | 72.4 GB | —needs > 24 GB | same size |
| Laguna S 2.1poolside | — | 72.5 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- z-lab
- Family
- GPT-OSS
- Parameters
- 120B
- Architecture
- DFlashDraftModel
- Context Length
- 131,072 tokens
- Vocabulary Size
- 201,088
- Release Date
- 2026-02-26
- License
- MIT
Frequently Asked Questions
How much VRAM does GPT OSS 120B DFlash need?
GPT OSS 120B DFlash requires 72.3 GB of VRAM at Q4_K_M, or 240.3 GB at BF16. Full 131K context adds up to 1.5 GB (73.8 GB total).
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)
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 ≈ 1.8 GB (at full 131K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run GPT OSS 120B DFlash?
No — GPT OSS 120B DFlash 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 DFlash?
For GPT OSS 120B DFlash, 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
★ Recommended — best balance of quality and VRAM usage.
Can I run GPT OSS 120B DFlash on a Mac?
Yes — Mac Pro M2 Ultra (64 GB) and 41 other Macs can run GPT OSS 120B DFlash, the smallest at Q3_K_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 72.3 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio (M3 Ultra, 96GB).
Can I run GPT OSS 120B DFlash locally?
Yes — GPT OSS 120B DFlash can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is GPT OSS 120B DFlash?
At Q4_K_M, GPT OSS 120B DFlash 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)
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 DFlash?
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 DFlash?
No single consumer GPU has enough VRAM to run GPT OSS 120B DFlash at Q4_K_M (72.3 GB). Multi-GPU or professional hardware is required.
Which devices can run GPT OSS 120B DFlash?
30 devices with unified memory can run GPT OSS 120B DFlash at Q4_K_M (72.3 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.