LLMDeepSeek

deepseek-ai/DeepSeek-R1

deepseek-ai/DeepSeek-R1

Reasoning-focused MoE LLM, 671B total with 37B active. State-of-the-art chain-of-thought reasoning.

Parameters

38.5B

Context

160K

Downloads

8M

Likes

13.6K

Architecture

deepseek_v3

61 layers · 128 KV heads · head 56 · hidden 7168

View on HuggingFace

GPU Requirements

VRAM / RAM / disk estimates by quantization and usage scenario

QuantizationMinimumRecommendedProduction
FP1616bit
1586.0 GB
RAM 2379.0 · Disk 1988.7
1673.6 GB
RAM 2510.4 · Disk 2014.7
1842.4 GB
RAM 2763.6 · Disk 2053.7
FP88bit
795.5 GB
RAM 1193.2 · Disk 1013.9
847.2 GB
RAM 1270.7 · Disk 1039.9
980.0 GB
RAM 1470.0 · Disk 1078.9
INT88bit
795.5 GB
RAM 1193.2 · Disk 1013.9
847.2 GB
RAM 1270.7 · Disk 1039.9
980.0 GB
RAM 1470.0 · Disk 1078.9
AWQ4bit
415.0 GB
RAM 622.6 · Disk 544.7
449.4 GB
RAM 674.1 · Disk 570.7
565.0 GB
RAM 847.4 · Disk 609.7
GPTQ4bit
420.0 GB
RAM 630.0 · Disk 550.8
454.6 GB
RAM 681.9 · Disk 576.8
570.4 GB
RAM 855.5 · Disk 615.8
GGUF4bit
429.9 GB
RAM 644.8 · Disk 563.0
464.9 GB
RAM 697.4 · Disk 589.0
581.1 GB
RAM 871.7 · Disk 628.0

GPU Recommendations

Best ValueBEST

8× H200 SXM

1128 GB VRAM

$20,440/mo

≈ 47.4 tok/s

Effective production throughput ≈ 33.2 tok/s

score 0.326

Cheapest

8× A100 80GB

640 GB VRAM

$7,008/mo

≈ 20.1 tok/s

Effective production throughput ≈ 14.1 tok/s

score 0.4479

Deploy on vast ↗
Performance

4× H200 SXM

564 GB VRAM

$10,220/mo

≈ 35.5 tok/s

Effective production throughput ≈ 24.8 tok/s

score 0.4569

Min Complexity

4× H200 SXM

564 GB VRAM

$10,220/mo

≈ 35.5 tok/s

Effective production throughput ≈ 24.8 tok/s

score 0.4569

💬 Community — real deployment experience

0 Articles · 0 Benchmarks

View Community →
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