Coding AIDeepSeekMIT

DeepSeek-Coder-V2 Lite 16B

deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct

Parameters

16B

Context

128K

Downloads

545.5K

Likes

630

Architecture

moe_transformer

View on HuggingFace

GPU Requirements

VRAM / RAM / disk estimates by quantization and usage scenario

QuantizationMinimumRecommendedProduction
FP1616bit
42.7 GB
RAM 64.0 · Disk 85.5
60.1 GB
RAM 90.2 · Disk 111.5
158.7 GB
RAM 238.1 · Disk 150.5
FP88bit
23.8 GB
RAM 35.7 · Disk 62.3
40.4 GB
RAM 60.6 · Disk 88.3
138.2 GB
RAM 207.3 · Disk 127.3
INT88bit
23.8 GB
RAM 35.7 · Disk 62.3
40.4 GB
RAM 60.6 · Disk 88.3
138.2 GB
RAM 207.3 · Disk 127.3
AWQ4bit
14.7 GB
RAM 32.0 · Disk 51.1
30.9 GB
RAM 46.4 · Disk 77.1
128.3 GB
RAM 192.4 · Disk 116.1
GPTQ4bit
14.9 GB
RAM 32.0 · Disk 51.2
31.1 GB
RAM 46.6 · Disk 77.2
128.4 GB
RAM 192.6 · Disk 116.2
GGUF4bit
15.1 GB
RAM 32.0 · Disk 51.5
31.3 GB
RAM 46.9 · Disk 77.5
128.7 GB
RAM 193.0 · Disk 116.5

GPU Recommendations

Best ValueBEST

1× H100 SXM

80 GB VRAM

$1,172/mo

≈ 433.4 tok/s

Effective production throughput ≈ 303.4 tok/s

score 0.5926

Deploy on vast ↗
Cheapest

2× Tesla V100

32 GB VRAM

$42.2/mo

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

Effective production throughput ≈ 0 tok/s

score 0.5933

Deploy on vast ↗
Performance

8× H200 SXM

1128 GB VRAM

$20,440/mo

≈ 1987.1 tok/s

Effective production throughput ≈ 1391 tok/s

score 0.2197

Min Complexity

1× RTX A6000

48 GB VRAM

$210/mo

≈ 99.4 tok/s

Effective production throughput ≈ 69.6 tok/s

score 0.6629

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

RTX 5090

32 GB VRAM

$244

/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

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