Can you run Gpt Oss 20B locally?
Gpt family · · 20.9B parameters (3.6B activated per token) · Hugging Face model card
Gpt Oss 20B is a mixture-of-experts model with 3.6B active parameters per token and 20.9B total open-weight model with a 131,072-token context window, usable locally with roughly 16 GB of memory at Q4-class quantization (calculated; measured speeds vary by hardware).
Minimum: 16 GB+ memory (Q4_K_M, calculated) · Recommended: 16 GB+ memory for full context (Q4_K_M, calculated)
How much VRAM does Gpt Oss 20B need at each quantization?
Gpt Oss 20B needs 14.0 GB of VRAM at Q4_K_M. The table below lists weights-only size and total VRAM including overhead for each common quantization level with 4k and 32k token contexts.
| Quantization | Bits / weight | Weights only | Total + KV @4k ctx | Total + KV @32k ctx |
|---|---|---|---|---|
| Q4_K_M | 4.8 | 12.6 GB | 14.0 GB | 15.4 GB |
| Q5_K_M | 5.7 | 14.9 GB | 16.6 GB | 18.0 GB |
| Q6_K | 6.6 | 17.3 GB | 19.2 GB | 20.6 GB |
| Q8_0 | 8.5 | 22.2 GB | 24.6 GB | 26.1 GB |
| FP16 | 16 | 41.8 GB | 46.2 GB | 47.6 GB |
Method: weights = parameters × bits-per-weight (Q4_K_M ≈ 4.8, Q5_K_M ≈ 5.7, Q6_K ≈ 6.6, Q8_0 ≈ 8.5, FP16 = 16), plus 10% loading overhead, plus KV cache from the verified architecture config (24 layers, 8 KV heads, 64 head dim). Source: HF-verified 2026-09-12 via api (params=20.91B ctx=131072 license=apache-2.0); VRAM figures are documented calculations, not measurements
Which GPUs can run Gpt Oss 20B locally?
At Q4_K_M with a 4k context, Gpt Oss 20B needs 14.0 GB of VRAM. The lists below are computed live from our GPU database and grouped by how much headroom the card has. Disclosure: CompareAIHardware.com participates in the Amazon Associates program and earns from qualifying purchases through links on this page. Affiliate relationships do not influence our recommendations.
Runs Gpt Oss 20B comfortably (25%+ VRAM headroom)
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| Radeon RX 7900 XT | 20 GB | 800 GB/s | Consumer GPU | Check price |
| GeForce RTX 3090EOL | 24 GB | 936 GB/s | Consumer GPU | Check price |
| GeForce RTX 3090 TiEOL | 24 GB | 1008 GB/s | Consumer GPU | Check price |
| GeForce RTX 4090 | 24 GB | 1008 GB/s | Consumer GPU | Check price |
| NVIDIA RTX PRO 4000 Blackwell | 24 GB | 672 GB/s | Pro GPU | Check price |
| Radeon RX 7900 XTX | 24 GB | 960 GB/s | Consumer GPU | Check price |
| AMD Radeon Pro W7800 | 32 GB | 576 GB/s | Pro GPU | Check price |
| GeForce RTX 5090 | 32 GB | 1792 GB/s | Consumer GPU | Check price |
| NVIDIA RTX 5000 Ada | 32 GB | 576 GB/s | Pro GPU | Check price |
| NVIDIA RTX PRO 4500 Blackwell | 32 GB | 896 GB/s | Pro GPU | Check price |
| NVIDIA A100 40GB SXMEOL | 40 GB | 1555 GB/s | Data Center GPU | |
| AMD Radeon Pro W7900 | 48 GB | 864 GB/s | Pro GPU | Check price |
| NVIDIA RTX PRO 5000 Blackwell | 48 GB | 1344 GB/s | Pro GPU | |
| RTX 6000 Ada | 48 GB | 960 GB/s | Pro GPU | Check price |
| RTX A6000 | 48 GB | 768 GB/s | Pro GPU | Check price |
| H100 SXM | 80 GB | 3350 GB/s | Data Center GPU | |
| NVIDIA A100 80GB SXM | 80 GB | 2039 GB/s | Data Center GPU | |
| NVIDIA H100 PCIe 80GB | 80 GB | 2039 GB/s | Data Center GPU | |
| NVIDIA RTX PRO 6000 Blackwell | 96 GB | 1792 GB/s | Pro GPU | Check price |
| H200 SXM | 141 GB | 4800 GB/s | Data Center GPU | |
| AMD Instinct MI300X | 192 GB | 5300 GB/s | Data Center GPU | |
| NVIDIA B200 | 192 GB | 8000 GB/s | Data Center GPU | |
| AMD Instinct MI325X | 256 GB | 6000 GB/s | Data Center GPU | |
| AMD Instinct MI355X | 288 GB | 8000 GB/s | Data Center GPU | |
| NVIDIA B300 (Blackwell Ultra) | 288 GB | 8000 GB/s | Data Center GPU |
Minimum GPUs that fit Gpt Oss 20B at Q4_K_M
These GPUs hold the model but leave little headroom — keep contexts short.
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| Arc A770 16GB | 16 GB | 560 GB/s | Consumer GPU | Check price |
| GeForce RTX 4060 Ti 16GB | 16 GB | 288 GB/s | Consumer GPU | Check price |
| GeForce RTX 4070 Ti SUPEREOL | 16 GB | 672 GB/s | Consumer GPU | Check price |
| GeForce RTX 4080EOL | 16 GB | 716 GB/s | Consumer GPU | Check price |
| GeForce RTX 4080 SUPER | 16 GB | 736 GB/s | Consumer GPU | Check price |
| GeForce RTX 5060 Ti 16GB | 16 GB | 448 GB/s | Consumer GPU | Check price |
| GeForce RTX 5070 Ti | 16 GB | 896 GB/s | Consumer GPU | Check price |
| GeForce RTX 5080 | 16 GB | 960 GB/s | Consumer GPU | Check price |
| NVIDIA RTX PRO 2000 Blackwell | 16 GB | 288 GB/s | Pro GPU | Check price |
| Radeon RX 6800 XTEOL | 16 GB | 512 GB/s | Consumer GPU | Check price |
| Radeon RX 7600 XT | 16 GB | 288 GB/s | Consumer GPU | Check price |
| Radeon RX 7800 XT | 16 GB | 624 GB/s | Consumer GPU | Check price |
| Radeon RX 9070 | 16 GB | 640 GB/s | Consumer GPU | Check price |
| Radeon RX 9070 XT | 16 GB | 640 GB/s | Consumer GPU | Check price |
Needs 2+ GPUs to run Gpt Oss 20B
One of these cards is too small on its own, but a pair (tensor or pipeline parallel, ~90% efficiency) covers the 14.0 GB requirement. See our multi-GPU guide for setup.
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| Arc A580 | 8 GB ×2 | 512 GB/s | Consumer GPU | Check price |
| Arc A750 | 8 GB ×2 | 512 GB/s | Consumer GPU | Check price |
| GeForce RTX 3070EOL | 8 GB ×2 | 448 GB/s | Consumer GPU | Check price |
| GeForce RTX 4060 | 8 GB ×2 | 272 GB/s | Consumer GPU | Check price |
| GeForce RTX 4060 Ti 8GB | 8 GB ×2 | 288 GB/s | Consumer GPU | Check price |
| GeForce RTX 5050 | 8 GB ×2 | 320 GB/s | Consumer GPU | Check price |
| GeForce RTX 5060 | 8 GB ×2 | 448 GB/s | Consumer GPU | Check price |
| Radeon RX 7600 | 8 GB ×2 | 288 GB/s | Consumer GPU | Check price |
| Arc B570 | 10 GB ×2 | 380 GB/s | Consumer GPU | Check price |
| GeForce RTX 3080 10GBEOL | 10 GB ×2 | 760 GB/s | Consumer GPU | Check price |
| GeForce RTX 2080 TiEOL | 11 GB ×2 | 616 GB/s | Consumer GPU | Check price |
| Arc B580 | 12 GB ×2 | 456 GB/s | Consumer GPU | Check price |
| GeForce RTX 3060 12GB | 12 GB ×2 | 360 GB/s | Consumer GPU | Check price |
| GeForce RTX 3080 TiEOL | 12 GB ×2 | 912 GB/s | Consumer GPU | Check price |
| GeForce RTX 4070EOL | 12 GB ×2 | 504 GB/s | Consumer GPU | Check price |
| GeForce RTX 4070 SUPEREOL | 12 GB ×2 | 504 GB/s | Consumer GPU | Check price |
| GeForce RTX 5070 | 12 GB ×2 | 672 GB/s | Consumer GPU | Check price |
| Radeon RX 7700 XT | 12 GB ×2 | 432 GB/s | Consumer GPU | Check price |
Can a Mac run Gpt Oss 20B?
Yes — these Apple Silicon machines fit Gpt Oss 20B at Q4_K_M, since macOS lets the GPU use about 75% of unified memory. Generation speed is bound by memory bandwidth, so the GB/s column matters as much as capacity.
| Apple Silicon | Unified memory | Usable by GPU (~75%) | Bandwidth |
|---|---|---|---|
| Apple M3 | 24 GB | 18 GB | 150 GB/s |
| Apple M4 | 32 GB | 24 GB | 120 GB/s |
| Apple M3 Pro | 36 GB | 27 GB | 300 GB/s |
| Apple M4 Pro | 64 GB | 48 GB | 273 GB/s |
| Apple M3 Max | 128 GB | 96 GB | 400 GB/s |
| M4 Max (MacBook Pro) | 128 GB | 96 GB | 546 GB/s |
| M3 Ultra (Mac Studio) | 512 GB | 384 GB | 819 GB/s |
Can Gpt Oss 20B run on mini PCs, Jetson, or NPU devices?
These edge and NPU devices from our database have enough memory for Gpt Oss 20B at Q4_K_M. Their memory bandwidth is far below discrete GPUs, so expect a fraction of desktop generation speed.
| Device | Memory | Bandwidth | Type |
|---|---|---|---|
| Jetson Orin NX 16GB Super | 16 GB | 102 GB/s | Edge Compute Device |
| Intel Core Ultra 9 288V (Lunar Lake) | 32 GB | 136 GB/s | NPU Chip |
| Jetson AGX Orin 32GB | 32 GB | 204 GB/s | Edge Compute Device |
| Jetson AGX Orin 64GB | 64 GB | 204 GB/s | Edge Compute Device |
| Snapdragon X Elite (X1E-84-100) | 64 GB | 135 GB/s | NPU Chip |
| AMD Ryzen AI 9 HX 370 (Strix Point) | 96 GB | 120 GB/s | NPU Chip |
Which pre-built systems can run Gpt Oss 20B?
These mini PCs and workstations from our database fit Gpt Oss 20B at Q4_K_M. Usable-memory figures are conservative: Windows shares about half of system RAM with the GPU by default (Linux can expose more), macOS lets Apple Silicon GPUs use about 75% of unified memory, and Linux unified-memory systems such as GB10 expose roughly 90%. Generation speed is bound by memory bandwidth, so compare the GB/s column before buying.