LLMOpenai

openai/gpt-oss-20b

openai/gpt-oss-20b

Parameters

3B

Context

128K

Downloads

8.1M

Likes

4.9K

Architecture

gpt_oss

24 layers · 8 KV heads · head 64 · hidden 2880

View on HuggingFace

GPU Requirements

VRAM / RAM / disk estimates by quantization and usage scenario

QuantizationMinimumRecommendedProduction
FP1616bit
54.4 GB
RAM 81.7 · Disk 100.0
72.4 GB
RAM 108.7 · Disk 126.0
171.6 GB
RAM 257.4 · Disk 165.0
FP88bit
29.7 GB
RAM 44.5 · Disk 69.5
46.6 GB
RAM 69.9 · Disk 95.5
144.6 GB
RAM 216.9 · Disk 134.5
INT88bit
29.7 GB
RAM 44.5 · Disk 69.5
46.6 GB
RAM 69.9 · Disk 95.5
144.6 GB
RAM 216.9 · Disk 134.5
AWQ4bit
17.8 GB
RAM 32.0 · Disk 54.8
34.1 GB
RAM 51.2 · Disk 80.8
131.6 GB
RAM 197.4 · Disk 119.8
GPTQ4bit
17.9 GB
RAM 32.0 · Disk 55.0
34.3 GB
RAM 51.4 · Disk 81.0
131.8 GB
RAM 197.7 · Disk 120.0
GGUF4bit
18.3 GB
RAM 32.0 · Disk 55.4
34.6 GB
RAM 51.9 · Disk 81.4
132.1 GB
RAM 198.2 · Disk 120.4

GPU Recommendations

Best ValueBEST

1× H100 SXM

80 GB VRAM

$1,172/mo

≈ 330 tok/s

Effective production throughput ≈ 231 tok/s

score 0.6032

Deploy on vast ↗
Cheapest

4× Tesla V100

64 GB VRAM

$84.4/mo

≈ 0 tok/s (N/A — benchmark unavailable)

Effective production throughput ≈ 0 tok/s

score 0.5404

Deploy on vast ↗
Performance

8× H200 SXM

1128 GB VRAM

$20,440/mo

≈ 1513.3 tok/s

Effective production throughput ≈ 1059.3 tok/s

score 0.2197

Min Complexity

1× RTX A6000

48 GB VRAM

$210/mo

≈ 75.7 tok/s

Effective production throughput ≈ 53 tok/s

score 0.6807

Deploy on vast ↗

💬 Community — real deployment experience

0 Articles · 0 Benchmarks

View Community →

GPUs that fit (single card)

RTX A6000

48 GB VRAM

$210

/mo · vast

L40S

48 GB VRAM

$341

/mo · vast

RTX PRO 6000 WS

48 GB VRAM

$482

/mo · vast

RTX PRO 6000 S

48 GB VRAM

$633

/mo · vast

A100 80GB

80 GB VRAM

$876

/mo · vast

AmciHub

AI Model Deployment & Compute Intelligence — analyze AI model requirements, GPU performance, cloud pricing and deployment costs to find the right deployment solution.

© 2026 AmciHub. All rights reserved. Model → Requirement → GPU → Benchmark → Cloud → Cost → Recommendation