Model library / Chat / reasoning

gpt-oss 120B: GPU requirements and hosting

The larger gpt-oss reasoning checkpoint. Evaluate it when task quality warrants a larger memory budget, especially for tool-using workflows with verifiable results.

Sources reviewed 2026-09-30 · B3IQ engineering

Runtime profiles and requirements

MoE sparsity reduces active computation, not the need to hold all model weights. Confirm MXFP4 support and runtime overhead before selecting the GPU configuration.

ollama · gpt-oss-120b

Catalog GPU threshold: 80 GB
Configured context: 16,384 tokens
Catalog system RAM: 128 GB

Runtime: ollama
Artifact identifier: gpt-oss:120b
Precision: Not pinned in catalog
Configured concurrency: 1 request(s)
Profile inputs / outputs: text → text
Deployed artifact revision: Not verified

Catalog thresholds are configuration guidance, not measured peak memory. A positive GPU requirement is not recorded for every profile; unspecified is not zero. Context, concurrency and runtime overhead can increase memory use. Configured context is a profile setting, not the upstream maximum. Concurrency is configuration, not a load-test result.

Weight-only arithmetic and a planning estimate

Using the catalog's approximate 120B total parameters, 16-bit weights alone occupy about 240 GB (parameters × 2 bytes). The shared sizing helper rounds a 20% planning reserve to 288 GB.

This arithmetic does not describe the selected quantized artifact. It excludes a workload-specific KV-cache calculation and cannot guarantee fit at the configured context or concurrency. A mixture-of-experts model still stores its full weights.

See the assumptions →

Plan the machine.

Memory-based machine candidates depend on the current visible store configurations. Confirm runtime compatibility, GPU count, interconnect and workload before purchase. Pricing and availability are shown on the machine page.

Explore machines

Before deployment

Test the workload you need.

Measure time to first token and completed-task latency at your chosen reasoning effort. Tokens per second alone can obscure how long the user waits.

These are catalog profiles and planning figures. No B3IQ performance measurement or live capacity is claimed. Confirm the artifact, runtime, workload and machine before deployment.

Evidence available

Upstream identity / license: Source reviewed
Runtime settings: Catalog configuration
Memory fit: Guidance; not a measured peak
B3IQ performance: No published benchmark
Installation / live capacity: Confirm for your machine

Read our evidence standard →

Source and access.

Upstream checkpoint: openai/gpt-oss-120b ↗
Upstream license metadata: Apache-2.0 ↗
Access: No access gate reported by the upstream repository at review.
Reviewed source revision: b5c939de8f754692c1647ca79fbf85e8c1e70f8a ↗

The reviewed revision identifies the source used for this guide. It is not a claim that this revision is installed on a B3IQ machine. Review the publisher's current license and acceptable-use terms for your application.