GPT-OSS 20B VRAM Requirements — RTX 4060 Ti, RTX 4070, RTX 4090, Apple Silicon Guide
GPT-OSS 20B (21B MoE, 3.6B active) needs ~12 GB at Q4_K_M. Runs on RTX 4060 Ti 16GB, RTX 4070 12GB, and Apple Silicon. Full VRAM table and per-GPU verdicts.
If you are searching for GPT-OSS 20B VRAM requirements or "gpt-oss-20b RTX 4060 Ti / RTX 4090", this is the exact 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. That MoE sparsity is the key insight for hardware planning: the model loads 21B weights into VRAM, but generates tokens at the compute cost of a ~4B model. It is fast.
Quick answers
- GPT-OSS 20B VRAM (Q4_K_M): ~12 GB weights + ~2 GB KV cache = ~14 GB total
- GPT-OSS 20B on RTX 4060 Ti 16GB: fits comfortably at Q4_K_M
- GPT-OSS 20B on RTX 4070 12GB: very tight at Q4_K_M; use IQ3_M for reliable fit
- GPT-OSS 20B on RTX 4090 24GB: comfortable at Q4_K_M or Q6_K
- GPT-OSS 20B on Apple Silicon: fits on 16 GB (marginal) or 24 GB+ (comfortable)
GPT-OSS 20B model specs
| Feature | GPT-OSS 20B |
|---|---|
| Total parameters | 21 billion |
| Active per token | 3.6 billion (MoE) |
| Architecture | Mixture of Experts (24 layers, 32 experts, top-4 routing) |
| Context window | 128,000 tokens |
| License | Apache 2.0 |
| Provider | OpenAI |
| Release | August 2025 |
| HF repo | openai/gpt-oss-20b |
| Ollama | ollama pull gpt-oss |
Benchmarks from the official release: GPQA Diamond 71.5%, SWE-Bench Verified 60.7%, LiveCodeBench 74.6%, HumanEval 78.2%.
GPT-OSS 20B exact VRAM table
MoE models load all expert weights into VRAM; only the routing and top-4 experts are computed per token. VRAM usage reflects the full 21B weight footprint.
| Quant | Weight size | + KV cache (8K ctx) | Total VRAM | Fits on |
|---|---|---|---|---|
| IQ3_M | ~8.5 GB | ~1 GB | ~9.5 GB | RTX 4070 12GB, RTX 3060 12GB |
| Q4_K_M | ~12 GB | ~2 GB | ~14 GB | RTX 4060 Ti 16GB, RTX 4080 Super 16GB |
| Q5_K_M | ~14 GB | ~2 GB | ~16 GB | RTX 4060 Ti 16GB, RTX 4080 Super 16GB (tight) |
| Q6_K | ~16.5 GB | ~2 GB | ~18.5 GB | RTX 4090 24GB, RTX 3090 24GB |
| Q8_0 | ~22 GB | ~2 GB | ~24 GB | RTX 4090 24GB (tight), RTX 5090 32GB |
| FP16 | ~43 GB | ~3 GB | ~46 GB | RTX 5090 32GB (no), H100 80GB, Mac M4 Max 64GB+ |
KV cache scales with context length. At 128K context, add ~8-15 GB on top of weights — plan your context window accordingly if you're doing long agentic runs.
Per-GPU verdict
RTX 4060 Ti 16GB — Recommended
Verdict: fits at Q4_K_M, comfortable at standard context.
Q4_K_M weights (~12 GB) + KV cache at short context (~2 GB) = ~14 GB total. The RTX 4060 Ti 16GB handles this well. The MoE sparsity means generation speed is faster than you'd expect for a 21B model — approximately 30-45 tokens/second. Avoid pushing context beyond 32K as KV cache will approach the 16 GB ceiling.
Best setup: Ollama with gpt-oss model, or llama.cpp with Q4_K_M GGUF from Hugging Face. This is the best value path to GPT-OSS 20B quality on consumer hardware.
RTX 4070 12GB — Marginal at Q4_K_M
Verdict: too tight at Q4_K_M; use IQ3_M for comfortable operation.
Q4_K_M needs ~12 GB for weights alone, leaving almost nothing for runtime overhead and KV cache. Occasional OOM errors are likely at Q4_K_M. Use IQ3_M (~9.5 GB total) for reliable operation — quality loss is noticeable but the model is still far ahead of smaller open-source alternatives.
If you have an RTX 4070 Super 12GB (same memory but higher bandwidth), the situation is identical — the limit is VRAM capacity, not bandwidth.
Alternative: wait for a Q4_K_S GGUF (~11 GB) which may fit on 12 GB with minimal runtime overhead.
RTX 4090 24GB — Ideal consumer GPU
Verdict: comfortable at Q4_K_M through Q6_K; strong performance.
At 24 GB, the RTX 4090 runs GPT-OSS 20B at Q4_K_M with ~10 GB headroom for long context. Q6_K (~18.5 GB total) is also comfortable. Expect 60-80 tokens/second at Q4_K_M — fast enough for complex agentic chains and coding sessions. This is the best single-GPU consumer setup for GPT-OSS 20B.
For Q8_0 (~24 GB total), the RTX 4090 is very tight and may OOM under long context. Use an RTX 5090 32GB if Q8 quality is important to you.
Apple Silicon — Strong option for unified memory
Verdict: M4 Pro 24GB is the sweet spot; M4 16GB is marginal.
Apple Silicon Macs use unified memory, meaning RAM and VRAM share the same pool. This is advantageous for MoE models — loading all 21B expert weights is less punishing when unified memory can reach 96-128 GB.
| Mac | Unified RAM | GPT-OSS 20B fit | Speed est. |
|---|---|---|---|
| MacBook Air M4 16GB | 16 GB | Marginal at Q4_K_M; use IQ3_M | ~25 tok/s |
| Mac M4 Pro 24GB | 24 GB | Comfortable Q4_K_M | ~38 tok/s |
| Mac M4 Pro 48GB | 48 GB | Comfortable Q6_K or Q8_0 | ~40 tok/s |
| Mac M4 Max 36GB | 36 GB | Comfortable Q5_K_M, Q6_K | ~45 tok/s |
| Mac M4 Max 64GB | 64 GB | Q8_0 + long context | ~50 tok/s |
Recommended runtime: Ollama (ollama pull gpt-oss) or llama.cpp. Both support MoE routing efficiently on Apple Metal.
Expected tokens/second by GPU
MoE sparsity makes GPT-OSS 20B faster than its parameter count implies. Only 3.6B parameters are computed per forward pass, so bandwidth-bound inference runs close to a 4B dense model:
| Hardware | Quant | Tokens/sec (est.) |
|---|---|---|
| RTX 4060 Ti 16GB | Q4_K_M | ~35-45 tok/s |
| RTX 4070 12GB | IQ3_M | ~45-55 tok/s |
| RTX 4070 Ti Super 16GB | Q4_K_M | ~50-60 tok/s |
| RTX 4080 Super 16GB | Q4_K_M | ~55-65 tok/s |
| RTX 4090 24GB | Q4_K_M | ~65-80 tok/s |
| RTX 5090 32GB | Q5_K_M | ~85-100 tok/s |
| Mac M4 Pro 24GB | Q4_K_M | ~35-42 tok/s |
| Mac M4 Max 64GB | Q6_K | ~48-55 tok/s |
These are community-reported estimates for MoE models at these VRAM tiers. Actual performance depends on your runtime (Ollama, llama.cpp, LM Studio), batch size, and context length.
What hardware should I buy for GPT-OSS 20B?
| Tier | Hardware | VRAM | Quant | Est. price |
|---|---|---|---|---|
| Minimum value | RTX 4060 Ti 16GB | 16 GB | Q4_K_M | ~$400-450 |
| Sweet spot | RTX 4070 Ti Super 16GB | 16 GB | Q4_K_M/Q5 | ~$550-650 |
| Best consumer | RTX 4090 24GB | 24 GB | Q6_K | ~$1,600-1,900 |
| Best new GPU | RTX 5090 32GB | 32 GB | Q6_K/Q8 | ~$1,999-2,499 |
| Mac sweet spot | Mac M4 Pro 24GB | 24 GB unified | Q4_K_M | $1,999+ |
For 12 GB GPUs (RTX 4070, RTX 5070): GPT-OSS 20B is technically possible at IQ3_M but compromised. Consider running GPT-OSS 20B at low quant, or use a smaller model like Qwen 3.5 9B (5.5 GB at Q4, comfortably faster on 12 GB).
Reasoning modes and VRAM
GPT-OSS 20B supports three reasoning effort levels: low, medium, and high. Higher reasoning effort uses longer chain-of-thought traces, which directly increases KV cache usage:
- Low effort: minimal thinking tokens, standard KV cache footprint
- Medium effort: moderate reasoning chain, adds ~1-3 GB KV cache at 128K context
- High effort: extended thinking, can add 5-15 GB KV cache for complex problems
For reasoning-heavy workloads on 16 GB cards, use low or medium reasoning effort to avoid KV cache overflow.
GPT-OSS 20B vs other local models
| Model | Quant | VRAM needed | Quality tier |
|---|---|---|---|
| GPT-OSS 20B | Q4_K_M | ~14 GB | 89.8 (SWE-Bench 60.7%) |
| Qwen 3.6 27B | Q4_K_M | ~16.8 GB | Strong coding |
| Llama 3.3 70B | Q4_K_M | ~43 GB | Strong general |
| Gemma 4 9B | Q4_K_M | ~5.5 GB | Fast, smaller |
| Qwen 3.5 9B | Q4_K_M | ~5.5 GB | Fast 9B class |
GPT-OSS 20B punches well above 9B models on reasoning and coding tasks while fitting on 16 GB consumer hardware — a rare combination. 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