Tools / LLM GPU calculator / Gemma 3 27B
Gemma 3 27B VRAM requirements
GPU memory for the weights and KV cache at FP16, FP8 and 4-bit, and how many GPUs that takes. Figures from the model's config.json.
Short answer
Gemma 3 27B needs about 62 GB of GPU memory at FP16/BF16, 32 GB at FP8 and 18 GB at 4-bit to serve one user at an 8K-token context. Its 27.4B parameters take 54.8 GB at 16-bit, and that conversation adds 1.1 GB of KV cache. On NVIDIA H100 80 GB GPUs that is 1 GPU at FP16 or FP8; for a desktop, at 4-bit it fits a single 24 GB GeForce RTX 4090.
Updated . KV cache at FP16, plus 10% headroom for activations and the runtime.
GPU memory by precision
| Weights | Weight memory | 1 user, 8K context | 32 users, 8K context | 1 user, 128K context |
|---|---|---|---|---|
| FP16/BF16 | 54.8 GB | 62 GB | 100 GB | 73 GB |
| FP8 | 27.4 GB | 32 GB | 70 GB | 43 GB |
| 4-bit (INT4) | 15.1 GB | 18 GB | 56 GB | 29 GB |
Required memory = (weights + KV cache) ÷ 0.9. An FP8 KV cache halves the cache share; set it in the calculator.
How many GPUs for one user at 8K context
| GPU | Memory | FP16/BF16 | FP8 | 4-bit (INT4) |
|---|---|---|---|---|
| Data center · tensor parallel | ||||
| NVIDIA L40S | 48 GB | 2 GPUs · TP2 | 1 GPU | 1 GPU |
| NVIDIA RTX PRO 6000 Blackwell Server Edition | 96 GB | 1 GPU | 1 GPU | 1 GPU |
| NVIDIA H100 SXM | 80 GB | 1 GPU | 1 GPU | 1 GPU |
| NVIDIA H100 NVL | 94 GB | 1 GPU | 1 GPU | 1 GPU |
| NVIDIA H200 | 141 GB | 1 GPU | 1 GPU | 1 GPU |
| NVIDIA B200 | 180 GB | 1 GPU | 1 GPU | 1 GPU |
| NVIDIA B300 | 288 GB | 1 GPU | 1 GPU | 1 GPU |
| AMD Instinct MI300X | 192 GB | 1 GPU | 1 GPU | 1 GPU |
| AMD Instinct MI355X | 288 GB | 1 GPU | 1 GPU | 1 GPU |
| Workstation & consumer · layer split | ||||
| NVIDIA RTX PRO 6000 Blackwell Workstation Edition | 96 GB | 1 card | 1 card | 1 card |
| NVIDIA GeForce RTX 3090 | 24 GB | 3 cards | 2 cards | 1 card |
| NVIDIA GeForce RTX 4090 | 24 GB | 3 cards | 2 cards | 1 card |
| NVIDIA GeForce RTX 5090 | 32 GB | 2 cards | 1 card | 1 card |
| Apple Mac Studio M3 Ultra / M5 Ultra | 96 GB (72 usable) | 1 Mac | 1 Mac | 1 Mac |
| Apple Mac Studio M3 Ultra / M5 Ultra | 256 GB (192 usable) | 1 Mac | 1 Mac | 1 Mac |
| Apple Mac Studio M5 Ultra | 512 GB (384 usable) | 1 Mac | 1 Mac | 1 Mac |
Data center GPUs are counted at a tensor-parallel size (TP) that divides the model’s attention heads, within one 8-GPU server. Workstation and consumer cards are counted for a layer split, which works with any number of cards. Mac Studio counts about 75% of unified memory as usable by the GPU. Capacity only: speed depends on memory bandwidth and the serving stack.
Memory is step one. Throughput, latency, power and the network decide the real build.
Size my actual workloadGemma 3 27B architecture
- Parameters
- 27.4B
- Layers with a KV cache
- 62
- Attention heads / KV heads
- 32 / 16
- Head dimension
- 128
- Attention
- 52 sliding-window layers (1,024 tokens) + 10 global
- Maximum context
- 131,072 tokens
- KV cache per token (FP16)
- 2 × 10 global layers × 16 KV heads × 128 head dim × 2 bytes = 81,920 bytes (0.08 MB) per token, plus 8,192 bytes per token in each of the 52 sliding-window layers, for at most 1,024 tokens
Source: google/gemma-3-27b-it config.json on Hugging Face. Parameter counts are the Hub’s count of the repo’s safetensors files, or the model card’s figure.
Questions about Gemma 3 27B
How much VRAM does Gemma 3 27B need?
About 62 GB at FP16/BF16, 32 GB at FP8 and 18 GB at 4-bit, for one user at an 8K-token context. That is 54.8 GB, 27.4 GB or 15.1 GB of weights, plus 1.1 GB of KV cache, plus 10% headroom for activations and the runtime. Serving 32 users at 8K each takes 100 GB at FP16 or 70 GB at FP8, because every conversation keeps its own KV cache.
How many H100 GPUs does Gemma 3 27B need?
At FP16/BF16 it takes 1 GPU for one user at 8K and 2 GPUs (tensor parallel 2) for 32 users. At FP8, 1 GPU, for one user at 8K or for 32 users. One user at FP8 takes 1 GPU on H200 (141 GB) and 1 GPU on B200 (180 GB).
Can Gemma 3 27B run on an RTX 4090 or RTX 5090?
At 4-bit it needs about 18 GB for one user at 8K: a single RTX 4090 (24 GB) or a single RTX 5090 (32 GB). At FP8 (32 GB) it takes 2 RTX 4090s or a single RTX 5090 (32 GB). Several cards work with a layer split, as in llama.cpp, which divides the model's layers between them.
Can Gemma 3 27B run on a Mac Studio?
macOS lets the GPU use about 75% of unified memory by default: about 72 GB of a 96 GB, 192 GB of a 256 GB and 384 GB of a 512 GB Mac Studio. At 4-bit Gemma 3 27B needs 18 GB for one user at 8K, so it fits one 96 GB Mac Studio; at FP8 (32 GB) it fits one 96 GB Mac Studio.
How large is Gemma 3 27B's KV cache?
52 of its 62 layers use a sliding window and cache at most 1,024 tokens; only the other 10 keep the whole context. At FP16 that is 2 × 10 global layers × 16 KV heads × 128 head dim × 2 bytes = 81,920 bytes (0.08 MB) per token, plus 8,192 bytes per token in each of the 52 sliding-window layers, for at most 1,024 tokens. An 8K-token conversation holds 1.1 GB at FP16, and a 128K-token one holds 11 GB.
What is Gemma 3 27B's maximum context length?
131,072 tokens, according to its Hugging Face configuration. One user at that full length holds 11 GB of KV cache at FP16, for 43 GB in total with FP8 weights, or 37 GB with an FP8 KV cache as well.
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Not listed? The LLM GPU calculator sizes any Hugging Face model from its ID, link or config.json.
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