Gemma

google/gemma-4-31B-it

google/gemma-4-31B-it

Parameters

22.6B

Context

256K

Downloads

9.9M

Likes

3.5K

Architecture

gemma4

60 layers · 16 KV heads · head 256 · hidden 5376

View on HuggingFace

GPU Requirements

VRAM / RAM / disk estimates by quantization and usage scenario

QuantizationMinimumRecommendedProduction
FP1616bit
65.1 GB
RAM 97.7 · Disk 104.8
149.7 GB
RAM 224.6 · Disk 130.8
731.1 GB
RAM 1096.7 · Disk 169.8
FP88bit
38.4 GB
RAM 57.6 · Disk 71.9
121.8 GB
RAM 182.8 · Disk 97.9
702.0 GB
RAM 1053.0 · Disk 136.9
INT88bit
38.4 GB
RAM 57.6 · Disk 71.9
121.8 GB
RAM 182.8 · Disk 97.9
702.0 GB
RAM 1053.0 · Disk 136.9
AWQ4bit
25.6 GB
RAM 38.4 · Disk 56.1
108.4 GB
RAM 162.6 · Disk 82.1
688.0 GB
RAM 1032.0 · Disk 121.1
GPTQ4bit
25.8 GB
RAM 38.6 · Disk 56.3
108.6 GB
RAM 162.9 · Disk 82.3
688.2 GB
RAM 1032.3 · Disk 121.3
GGUF4bit
26.1 GB
RAM 39.1 · Disk 56.7
108.9 GB
RAM 163.4 · Disk 82.7
688.6 GB
RAM 1032.8 · Disk 121.7

GPU Recommendations

Best ValueBEST

4× H200 SXM

564 GB VRAM

$10,220/mo

≈ 1053 tok/s

Effective production throughput ≈ 737.1 tok/s

score 0.3206

Cheapest

8× RTX 4070S Ti

128 GB VRAM

$396/mo

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

Effective production throughput ≈ 0 tok/s

score 0.5259

Deploy on vast ↗
Performance

8× H200 SXM

1128 GB VRAM

$20,440/mo

≈ 1404 tok/s

Effective production throughput ≈ 982.8 tok/s

score 0.2197

Min Complexity

1× H200 SXM

141 GB VRAM

$2,555/mo

≈ 438.8 tok/s

Effective production throughput ≈ 307.2 tok/s

score 0.666

💬 Community — real deployment experience

0 Articles · 0 Benchmarks

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GPUs that fit (single card)

H200 SXM

141 GB VRAM

$2,555

/mo · lambda

B200

180 GB VRAM

$3,013

/mo · vast

B200 SXM

180 GB VRAM

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