GPT OSS Tiny Random — Hardware Requirements & GPU Compatibility
ChatFunctions
GPT OSS Tiny Random is a 7M-parameter open language model from yujiepan in the GPT-OSS family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 0.30 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 128K)Best on 24 GB (Q4_K_M · ~2184 tok/s · 128K)Macs from 8 GB (Q4_K_M · ~239 tok/s)
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run GPT OSS Tiny Random
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
vllm serve yujiepan/gpt-oss-tiny-random --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3060 12GB (11 GB headroom) · Q4_K_M
- Generation speed
- ~780 tok/s
- generation speed
- Cost per 1M output tokens
- $0.022
- per 1M output tokens
How Much VRAM Does GPT OSS Tiny Random Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.3 GB | 0.3 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~2184 tok/s | |
| 0.3 GB | 0.3 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~2184 tok/s | |
| 0.3 GB | 0.3 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~2184 tok/s | |
| 0.3 GB | 0.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~2114 tok/s | |
| 0.3 GB | 0.3 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 128K context · ~2114 tok/s | |
| 0.3 GB | 0.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 128K context · ~2114 tok/s | |
| 0.3 GB | 0.3 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 128K context · ~2114 tok/s |
Selected Q4_K_M
4-bit medium quantization0.3GBat 16K context
- Weights
- 0.1 GB
- KV cache
- 0.0 GB
- Overhead
- 0.3 GB
Which Hardware Can Run GPT OSS Tiny Random?
Q4_K_M · 0.3 GBGPT OSS Tiny Random (Q4_K_M) requires 0.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 114 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 185 devices with unified memory can run GPT OSS Tiny Random, including Mac Studio M5 Ultra (256 GB), Mac Studio M5 Ultra (512 GB).
≤ 11 GB
≤ 11 GB tier →fits · 3.7 GB free · up to 128K ctx
68 GPUs · 1 Mac class · 3 device families
RTX 3080~1647Simulate output on RTX 3080RTX 2080 Ti~1335Simulate output on RTX 2080 TiRTX 3070 Ti~1318Simulate output on RTX 3070 TiM3*8 GB~239Simulate output on iMac 24" M3 (8 GB)Orin Nano~221Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~159Simulate output on iPhone 1720 GB
fits · 19.7 GB free · up to 128K ctx
1 GPU
RX 7900 XT~1600Simulate output on RX 7900 XT32–40 GB
fits · 31.7 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 9 device families
RTX 5090~3883Simulate output on RTX 5090M5 Max*36 GB~1073Simulate output on Mac Studio M5 Max (36 GB)M632 GB~397Simulate output on iMac 24" M6 (32 GB)RTX Spark N1X~652Simulate output on NVIDIA RTX Spark N1X Laptop (32 GB)Orin~444Simulate output on NVIDIA Jetson AGX Orin 32GB
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to GPT OSS Tiny Random
GPT-OSS family · 14 models →Specifications
- Publisher
- yujiepan
- Family
- GPT-OSS
- Parameters
- 7M
- Architecture
- GptOssForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 201,088
- Release Date
- 2025-08-06
Frequently Asked Questions
How much VRAM does GPT OSS Tiny Random need?
GPT OSS Tiny Random requires 0.3 GB of VRAM at Q4_K_M, or 0.3 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 7M × 4.8 bits ÷ 8 = 0 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 ≈ 0.3 GB (at full 131K context)
VRAM usage by quantization
What's the best quantization for GPT OSS Tiny Random?
For GPT OSS Tiny Random, Q4_K_M (0.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run GPT OSS Tiny Random on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 158 other Macs can run GPT OSS Tiny Random. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run GPT OSS Tiny Random locally?
Yes — GPT OSS Tiny Random can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is GPT OSS Tiny Random?
At Q4_K_M, GPT OSS Tiny Random can reach ~16000 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~2184 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 ÷ 0.3 × 0.65 = ~17333 tok/s
Estimated speed at Q4_K_M (0.3 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 Tiny Random?
At Q4_K_M, the download is about 0.01 GB. The full-precision BF16 version is 0.01 GB. The smallest option (Q2_K) is 0.01 GB.
Which GPUs can run GPT OSS Tiny Random?
114 consumer GPUs can run GPT OSS Tiny Random at Q4_K_M. Top options include AMD Radeon RX 5600 XT, AMD Radeon RX 5700 XT, AMD Radeon RX 580 8GB. 114 GPUs have plenty of headroom for comfortable inference.
Which devices can run GPT OSS Tiny Random?
185 devices with unified memory can run GPT OSS Tiny Random at Q4_K_M (0.3 GB), including AMD Ryzen 7 8845HS (Hawk Point) Laptop, AMD Ryzen AI 7 350 (Krackan Point) Laptop, AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.