GPT-OSS 20B VRAM Requirements — RTX 4060 Ti, RTX 4070, RTX 4090, Apple Silicon Guide

If you are searching for GPT-OSS 20B VRAM requirements or "gpt-oss-20b RTX 4060 Ti / RTX 4090", this is the hardware reference you need.

GPT-OSS 20B is OpenAI's first open-weight model — a 21B-parameter mixture-of-experts (MoE) architecture that activates only 3.6B parameters per token. Two facts drive hardware planning:

1. It ships in MXFP4. OpenAI released the weights already quantized to 4 bits, so you don't pick a GGUF quant the way you would for a dense Llama or Qwen. The site models it at MXFP4: about 11.3 GB of weights.
2. It is sparse. All 21B weights must be in memory, but each token only touches 3.6B of them, so decode is far faster than a dense 20B model on the same card.

All figures below are our estimates from the site's fit engine (linked pages), not measurements, for a 16K-token coding workload.

Quick answers

• GPT-OSS 20B memory (MXFP4): ~11.3 GB of weights, ~14.1-14.9 GB in total at 16K context
• RTX 4060 Ti 16GB: tight fit (reserve intact), ~66 tok/s
• RTX 4070 12GB: runs only with a little CPU offload at MXFP4 (~93 tok/s, no safe context)
• RTX 4090 24GB: fits with room to spare, ~239 tok/s, full 131K context
• Apple Silicon: tight fit on an M4 Pro 24GB (~73 tok/s), comfortable on 36 GB and up; 16 GB needs heavy offload

GPT-OSS 20B model specs

Benchmarks from the official release: GPQA Diamond 71.5%, SWE-Bench Verified 60.7%, LiveCodeBench 74.6%, HumanEval 78.2%.

GPT-OSS 20B VRAM table

MoE models load every expert into memory, so VRAM reflects the full 21B footprint. "Memory needed" is the total the site compares with your card: weights + KV cache at 16K context + runtime overhead + safety reserve. The figures below are from the RTX 4090 page; the reserve scales a little with the card, so the same build shows ~14.1 GB on a 16 GB card and ~16.0 GB on an RTX 5090.

The practical takeaway: on the site's numbers, the Q4_K_M-Q8_0 GGUF builds of GPT-OSS 20B are all within about 0.5 GB of the native MXFP4 file. Unlike a dense model, going "below Q8" saves almost no memory, so there is little reason to leave MXFP4 unless you are squeezing into 12 GB.

KV cache is small at this context: the RTX 4090 page breaks the 14.9 GB down as 11.3 GB weights, 0.4 GB KV cache, 0.9 GB runtime and 2.4 GB headroom. That is why even 16 GB cards keep a safe context of ~97K tokens.

Per-GPU verdict

RTX 4060 Ti 16GB — Recommended entry point

Verdict: tight fit (reserve intact) at MXFP4.

The RTX 4060 Ti 16GB page puts GPT-OSS 20B at ~14.1 GB of 16.0 GB, with a safe context of about 97K tokens. Our speed estimate is ~66 tok/s. That is slower than other 16 GB cards because the 4060 Ti has much less memory bandwidth, but it is still very usable for chat and coding.

Best setup: Ollama with the gpt-oss model, or llama.cpp with the MXFP4 GGUF from Hugging Face. This is the cheapest card on which the model fits entirely in VRAM.

RTX 4070 12GB — Needs offload at the native format

Verdict: runs only with CPU offload at MXFP4; Q3_K_S or Q2_K fit, at a quality cost.

At MXFP4 the model needs ~13.7 GB, so the RTX 4070 12GB page rates it "Runs with offload (needs ~0.5 GB host RAM)". Our estimate is still ~93 tok/s because only a sliver spills to system RAM, but the page lists no safe context window: there is no memory left for a long conversation.

If you want it to fit in VRAM:

• Q3_K_S (~12.0 GB): fits, reduced reserve
• Q2_K (~10.1 GB): tight fit (reserve intact), and the only build the page recommends for this card

Both are low-quality builds. The RTX 4070 Super 12GB and RTX 5070 12GB are in the same position: capacity, not bandwidth, is the limit (the RTX 5070 page also shows offload, ~100 tok/s).

RTX 4090 24GB — Ideal consumer GPU

Verdict: fits comfortably; full 131K context.

The RTX 4090 page shows ~14.9 GB of 24 GB used, a safe context of 131K tokens (the model's limit), and an estimated ~239 tok/s at MXFP4, falling to about 171 tok/s with the 16K context filled. There is no memory pressure at any GGUF build up to Q8_0; only F16 (~43.6 GB) does not fit.

An RTX 5090 32GB is faster again (~374 tok/s by our estimate) but buys no extra capability for this model. A used RTX 3090 24GB gets the same full context at ~204 tok/s.

Apple Silicon — 24 GB is the floor

Verdict: M4 Pro 24GB is a tight fit; 36 GB and up is comfortable; 16 GB needs heavy offload.

Macs share memory between CPU and GPU, but macOS only lets the GPU use part of it, so the "usable" figure on each page is lower than the RAM size.

On a 16 GB M4, the page suggests Q2_K instead (~10.6 GB, tight fit with reserve intact). More memory does not make the M4 Pro faster: the 24 GB and 48 GB models share the same bandwidth, so both land at ~73 tok/s. The M4 Max is faster because of its wider memory bus.

Recommended runtime: Ollama (ollama pull gpt-oss) or llama.cpp; both run the MXFP4 weights on Apple Metal.

Expected tokens/second by GPU

MoE sparsity makes GPT-OSS 20B much faster than its parameter count implies. Our estimates at MXFP4 (single stream, from each linked page):

These are bandwidth-based estimates for an empty context. Real numbers depend on your runtime (Ollama, llama.cpp, LM Studio), its build, and how full the context is; speed drops as the context fills. An RTX 4060 8GB cannot run the model even at Q2_K without heavy offload.

What hardware should I buy for GPT-OSS 20B?

For 12 GB GPUs (RTX 4070, RTX 5070, RTX 3060 12GB): the native MXFP4 build spills slightly into system RAM and leaves no room for context. You can drop to Q3_K_S or Q2_K, or use a smaller model like Qwen 3.5 9B that fits a 12 GB card with room for context.

Reasoning modes and context

GPT-OSS 20B supports three reasoning effort levels: low, medium, and high. Higher effort produces longer chain-of-thought traces, which fill the context window faster and slow decode as the context grows (on the RTX 4090, our estimate drops from ~239 to ~171 tok/s with 16K tokens in context).

Memory is rarely the constraint: the site's safe-context estimates are 131K tokens on 24 GB cards and ~97K on 16 GB cards. On a 12 GB card at MXFP4 there is no safe context at all, so long high-effort runs are where 12 GB setups struggle first.

GPT-OSS 20B vs other local models

GPT-OSS 20B punches well above 9B models on reasoning and coding tasks while fitting on 16 GB consumer hardware — a rare combination. Check any of these against your exact card in the calculator. The Apache 2.0 license also makes it a strong choice for professional and commercial use.

Related guides

• VRAM Calculator — check your exact GPU
• Qwen 3.5 9B VRAM Requirements — best 8B-class alternative
• Best AI Models for 24GB VRAM — full comparison at the 24 GB tier
• Best Local Coding LLMs for Apple Silicon — Mac-specific guide
• Image Generation VRAM Guide 2026 — if you also run diffusion models