Coding AIDeepSeekMIT

DeepSeek-Coder-V2 236B

deepseek-ai/DeepSeek-Coder-V2-Instruct

236B MoE coding model.

Parameters

236B

Context

128K

Downloads

890K

Likes

2.1K

Architecture

moe_transformer

View on HuggingFace

GPU Requirements

VRAM / RAM / disk estimates by quantization and usage scenario

QuantizationMinimumRecommendedProduction
FP1616bit
561.0 GB
RAM 841.5 · Disk 724.8
602.1 GB
RAM 903.1 · Disk 750.8
724.2 GB
RAM 1086.3 · Disk 789.8
FP88bit
283.0 GB
RAM 424.5 · Disk 381.9
311.4 GB
RAM 467.1 · Disk 407.9
420.9 GB
RAM 631.4 · Disk 446.9
INT88bit
283.0 GB
RAM 424.5 · Disk 381.9
311.4 GB
RAM 467.1 · Disk 407.9
420.9 GB
RAM 631.4 · Disk 446.9
AWQ4bit
149.2 GB
RAM 223.8 · Disk 216.9
171.5 GB
RAM 257.2 · Disk 242.9
274.9 GB
RAM 412.4 · Disk 281.9
GPTQ4bit
150.9 GB
RAM 226.4 · Disk 219.0
173.3 GB
RAM 260.0 · Disk 245.0
276.8 GB
RAM 415.3 · Disk 284.0
GGUF4bit
154.4 GB
RAM 231.6 · Disk 223.3
176.9 GB
RAM 265.4 · Disk 249.3
280.6 GB
RAM 421.0 · Disk 288.3

GPU Recommendations

Best ValueBEST

8× H200 SXM

1128 GB VRAM

$20,440/mo

≈ 134.7 tok/s

Effective production throughput ≈ 94.3 tok/s

score 0.2603

Cheapest

8× RTX 3090

192 GB VRAM

$630/mo

≈ 26.3 tok/s

Effective production throughput ≈ 18.4 tok/s

score 0.5301

Deploy on vast ↗
Performance

4× H200 SXM

564 GB VRAM

$10,220/mo

≈ 101 tok/s

Effective production throughput ≈ 70.7 tok/s

score 0.3505

Min Complexity

1× B200

180 GB VRAM

$3,013/mo

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

Effective production throughput ≈ 0 tok/s

score 0.5739

Deploy on vast ↗

💬 Community — real deployment experience

0 Articles · 0 Benchmarks

View Community →

GPUs that fit (single card)

B200

180 GB VRAM

$3,013

/mo · vast

B200 SXM

180 GB VRAM

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