Coding AIDeepSeekMIT

DeepSeek-Coder 6.7B

deepseek-ai/deepseek-coder-6.7b-instruct

Parameters

6.7B

Context

16K

Downloads

510.2K

Likes

508

Architecture

transformer

View on HuggingFace

GPU Requirements

VRAM / RAM / disk estimates by quantization and usage scenario

QuantizationMinimumRecommendedProduction
FP1616bit
20.7 GB
RAM 32.0 · Disk 58.5
37.2 GB
RAM 55.8 · Disk 84.5
134.8 GB
RAM 202.2 · Disk 123.5
FP88bit
12.8 GB
RAM 32.0 · Disk 48.7
29.0 GB
RAM 43.4 · Disk 74.7
126.2 GB
RAM 189.3 · Disk 113.7
INT88bit
12.8 GB
RAM 32.0 · Disk 48.7
29.0 GB
RAM 43.4 · Disk 74.7
126.2 GB
RAM 189.3 · Disk 113.7
AWQ4bit
9.0 GB
RAM 32.0 · Disk 44.1
25.0 GB
RAM 37.5 · Disk 70.1
122.1 GB
RAM 183.1 · Disk 109.1
GPTQ4bit
9.1 GB
RAM 32.0 · Disk 44.1
25.0 GB
RAM 37.6 · Disk 70.1
122.1 GB
RAM 183.2 · Disk 109.1
GGUF4bit
9.2 GB
RAM 32.0 · Disk 44.2
25.1 GB
RAM 37.7 · Disk 70.2
122.2 GB
RAM 183.3 · Disk 109.2

GPU Recommendations

Best ValueBEST

1× RTX 5090

32 GB VRAM

$244/mo

≈ 553.1 tok/s

Effective production throughput ≈ 387.2 tok/s

score 0.6896

Deploy on vast ↗
Cheapest

2× Tesla V100

32 GB VRAM

$19.9/mo

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

Effective production throughput ≈ 0 tok/s

score 0.6389

Deploy on vast ↗
Performance

8× H200 SXM

1128 GB VRAM

$20,440/mo

≈ 4740.7 tok/s

Effective production throughput ≈ 3318.5 tok/s

score 0.2197

Min Complexity

1× RTX A6000

48 GB VRAM

$210/mo

≈ 237 tok/s

Effective production throughput ≈ 165.9 tok/s

score 0.6299

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

$586

/mo · vast

A100 80GB

80 GB VRAM

$876

/mo · vast

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