GPT OSS 120B
GPT OSS 120B is a reasoning model from OpenAI with 117B parameters, supporting a 131,072-token context window, with text modalities. Open-weight for self-hosted or compatible deployments.
GPT OSS 120B
GPT OSS 120B is a reasoning model from OpenAI with 117B parameters, supporting a 131,072-token context window, with text modalities. Open-weight for self-hosted or compatible deployments.
- • FP16 Weights = 117.0B × 2B = 234.00 GB
- • INT4 Weights = 117.0B × 0.55B = 64.35 GB
- • KV Cache (131072 ctx, FP16) ≈ 80.00 GB
- • Activation Buffer = ~20% overhead
Hardware & Execution ParametersReasoning
Genealogical Graph & Evolutionary Provenance
Tracing foundational base architecture ancestry, architectural successors, scale siblings, and reasoning distillation derivatives.
GPT OSS 120B operates as an autonomous foundation model architecture without direct precursor derivatives in this catalog.
Architecture Engineering & Capability Deep-Dive
An objective architectural evaluation of GPT OSS 120B by OpenAI, analyzing underlying compute dynamics, memory constraints, and deployment economics.
Topology & Attention Mechanics
Features an Omni multimodal unified encoder capable of test-time compute scaling via explicit Chain-of-Thought (CoT) reasoning tokens.
Evaluation Profile & Reasoning
Exhibits frontier-tier behavior in reasoning and coding. World-class step-by-step mathematical extraction.
LLM Hardware Sizing & Serving
For open deployments via vLLM/SGLang, quantization (INT4/AWQ) is heavily recommended to fit dense memory constraints, or multi-GPU pipeline parallelism for full FP16.
Inference Economics & Workflows
Reasoning tokens dynamically scale compute on hard problems. Expect higher output costs and varied TTFB, offset by massive reductions in hallucination rates.
Architectural Strengths vs. Considerations
An objective balance sheet analyzing the operational advantages and production constraints of deploying GPT OSS 120B.
Key Architectural Strengths
- Omni multimodal unified encoder with test-time compute scaling (CoT reasoning tokens) maxes out complex problem solving.
- Exceptional adherence to structured JSON schemas accelerates integration into deterministic enterprise pipelines.
- Massive 131,072-token context allows full-repository and book-length ingestion.
- Demonstrated AIME 2024 evaluation score of 95.8% in verified benchmarks.
Operational Considerations
- Autoregressive CoT reasoning tokens can increase Time-to-First-Byte (TTFB) and inflate output token budgets unpredictably.
- 128k+ token prefill stages become heavily compute-bound and balloon KV cache without PagedAttention chunking.
Inference Runtimes & Hardware Sizing
Deployment targets, inference engines, and memory requirements for GPT OSS 120B.
python3 -m vllm.entrypoints.openai.api_server --model GPT OSS 120B --tensor-parallel-size 4 --gpu-memory-utilization 0.95 --max-model-len 4096 --enable-chunked-prefillollama run gpt oss 120bpython3 -m sglang.launch_server --model-path GPT OSS 120B --tp 4 --trust-remote-codetext-generation-launcher --model-id GPT OSS 120B --num-shard 4 --max-batch-prefill-tokens 32000High-throughput PagedAttention server
One-click CLI & local desktop serving
Fast multi-turn structured decoding
Text Generation Inference
GGUF CPU/Apple Silicon execution
~280.8 GB VRAM required
~140.4 GB VRAM (Hopper speedup)
~77.2 GB VRAM
CPU RAM / Apple Silicon optimized
LLM Benchmark Database & Performance Metrics
12 TestedStandardized evaluation results across reasoning, agentic coding, computer use, and alignment.
API & Deployment Pricing
Open-weights model available for local and private cloud deployment. Compute costs depend on the target GPU hardware instance.
| Deployment Tier | Pricing Structure |
|---|---|
| Open Checkpoint Weights | $0.00 (Free Download) |
| Inference Token Consumption | $0.00 / Token |
Comparable Foundation Architectures
Alternative models in the Reasoning class with similar capabilities, context windows, or deployment profiles.
Ling 3.0 Flash Fin
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ.get("OPENAI_KEY", "EMPTY"),
base_url="http://localhost:8000/v1"
)
response = client.chat.completions.create(
model="openai-gpt-oss-120b",
messages=[{"role": "user", "content": "Explain quantum superposition in 2 sentences."}]
)
print(response.choices[0].message.content)Frequently Asked Questions about GPT OSS 120B
Essential facts, architectural specs, hardware constraints, and pricing answers for GPT OSS 120B.
To run GPT OSS 120B (117B) locally, you generally need 8-12 GB VRAM (4-bit to 8-bit). We recommend using quantized GGUF/AWQ formats with Ollama or vLLM to optimize memory footprint.
All technical specifications, parameter distributions, context architectures, and benchmark evaluations for GPT OSS 120B are audited against primary source release documentation, research whitepapers, and verified vendor API endpoints.