Gemma 3 27B IT (Multimodal) VRAM Requirements, GGUF Sizes & G…

Interactive Gemma 3 27B IT (Multimodal) Memory & Context Simulator

Weight Quantization (bpw)

KV Cache Precision

Target GPU / Unified Memory Tier

Devices (TP)

Context Length (tokens) 32,768 tokens

Memory Breakdown & Bandwidth Ceiling

Tight Fit (>90% Usable)

Model Weights: 77.1% KV: 14.5% Overhead: 8.4%

Total Required Memory

20.06 GB

Per-Device Allocation: 20.06 GB

Model Weights

15.47 GB

KV Cache (FP16): 2.91 GB

Est. Single-Stream Decode

29.7 tok/s

Runtime & TP Overhead: 1.68 GB

Max Context Before OOM

13,224

tokens

Architecture KV compression saves 90.6% vs naive MHA FP16 (31.00 GB).

Precision & Quantization VRAM Matrix (8K / 32K / 128K Context)

Total GPU memory required across weight precisions and context lengths (batch = 1, FP16 KV cache)
Precision / Format	Effective bpw	Weights Only	Total @ 8K ctx	Total @ 32K ctx	Total @ 128K ctx	Recommended Tier (@ 32K)
FP16 / BF16	16.00 bpw	51.04 GB	54.82 GB	56.69 GB	64.19 GB	NVIDIA A100 80GB
FP8	8.50 bpw	27.11 GB	30.18 GB	32.05 GB	39.55 GB	Apple M4 Pro 48GB
Q8_0	8.50 bpw	27.11 GB	30.18 GB	32.05 GB	39.55 GB	Apple M4 Pro 48GB
Q6_K	6.56 bpw	20.92 GB	23.80 GB	25.68 GB	33.18 GB	RTX 5090 32GB
Q5_K_M	5.69 bpw	18.15 GB	20.94 GB	22.82 GB	30.32 GB	RTX 3090 24GB
Q4_K_M	4.85 bpw	15.47 GB	18.18 GB	20.06 GB	27.56 GB	RTX 3090 24GB
AWQ INT4	4.50 bpw	14.35 GB	17.03 GB	18.91 GB	26.41 GB	RTX 3090 24GB
IQ4_XS	4.25 bpw	13.56 GB	16.21 GB	18.09 GB	25.59 GB	RTX 3090 24GB

Hardware Compatibility & Decode Speed (Gemma 3 27B IT (Multimodal) @ Q4_K_M, 32K Context)

Fit status, memory utilization, and estimated decode throughput across flagship GPUs and Apple Silicon
Hardware Tier	Memory & Bandwidth	Required (32K)	Fit Verdict	Est. Decode Speed
RTX 3090 24GB	24 GB · 936 GB/s	20.06 GB (84%)	Tight Fit (>90% Usable)	29.7 tok/s
RTX 4090 24GB	24 GB · 1008 GB/s	20.06 GB (84%)	Tight Fit (>90% Usable)	32.0 tok/s
RTX 5090 32GB	32 GB · 1792 GB/s	20.06 GB (63%)	Fits Comfortably	56.8 tok/s
Apple M-Max 64GB	64 GB · 546 GB/s	19.49 GB (30%)	Fits Comfortably	16.3 tok/s
Mac Studio 128GB	128 GB · 546 GB/s	19.49 GB (15%)	Fits Comfortably	16.3 tok/s
Mac Studio 192GB	192 GB · 800 GB/s	19.49 GB (10%)	Fits Comfortably	23.9 tok/s
NVIDIA A100 80GB	80 GB · 2039 GB/s	20.06 GB (25%)	Fits Comfortably	68.4 tok/s
NVIDIA H100 80GB	80 GB · 3350 GB/s	20.06 GB (25%)	Fits Comfortably	112.5 tok/s

Verified Transformer Architecture Parameters

Checkpoint ID: google/gemma-3-27b-it
Ollama Tag: gemma3:27b
License / Release: Gemma Terms · 2025-Q1
Topology & Attention: Dense Transformer
Attention Mechanism: Hybrid Sliding-Window (SWA)
Total Params (B): 27.4B
Active Params / Token (B): 27.4B
Hidden Layers / Hidden Size: 62 / 5376
Query Heads / KV Heads: 32Q / 16KV (d=128)
Sliding Window: 1024 (83% local)
Vocab / Max Position: 262,208 / 131,072

Quantized formats store per-block scale factors and zero-points alongside packed integer weights. A 4-bit GGUF Q4_K_M checkpoint averages 4.85 effective bits per parameter (not 4.00), while Q8_0 averages 8.50 bpw. For Mixture-of-Experts models, all routed and shared experts remain resident in memory to prevent token-by-token host paging.

Standard Grouped-Query Attention allocates 2 × L × H_kv × d_head × S × B × bytes_kv across layers. DeepSeek Multi-head Latent Attention (MLA) compresses keys and values into L × (d_kv_lora + d_qk_rope) × S × B × bytes_kv, slashing long-context memory by over 90%. Hybrid sliding-window models cap local-attention layers at min(S, window_size) while scaling global layers linearly with S.

Quick-Start vLLM & Ollama Commands for Gemma 3 27B IT (Multimodal)

vllm serve google/gemma-3-27b-it \
  --tensor-parallel-size 1 \
  --max-model-len 32768 \
  --max-num-seqs 8 \
  --gpu-memory-utilization 0.95 \
  --kv-cache-dtype fp8 \
  --enable-prefix-caching \
  --port 8000

# Save as Modelfile, then run: ollama create gemma-3-27b-custom -f Modelfile
FROM gemma3:27b
PARAMETER num_ctx 32768
PARAMETER num_batch 512
PARAMETER flash_attn true

# Environment variables for ollama serve:
# OLLAMA_NUM_PARALLEL=8 OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama serve