ChatCodingReasoningMulti-Purpose27.23B
Gemma 2 27B Instruct
Gemma · Gemma License
Google's 27B model with excellent knowledge distillation. Strong reasoning and coding capabilities at a size that fits on a single 24GB GPU at Q4. Limited to 8K context.
- Parameters
- 27.23B
- Context
- 8,192 tokens
- Released
- 2024-06-27
- Engines
- llama.cpp, ollama, vLLM
Gemma 2 27B Instruct (27.23B) requires 18.5 GB VRAM at recommended quality (Q5_K_M). At efficient quality (Q4_K_M), it fits in 15.5 GB VRAM, making it compatible with the NVIDIA GeForce RTX 4060 Ti 16GB. On NVIDIA GeForce RTX 4090, expect approximately 22 tok/s at Q4_K_M. For the best experience, AMD AI Powerhouse ($1,818) is recommended.
— OwnRig methodology, data updated 2026-03-01
VRAM Requirements
| Quality | Quantization | VRAM | File Size |
|---|---|---|---|
| recommended | Q5_K_M | 18.5 GB | 16.3 GB |
| efficient | Q4_K_M | 15.5 GB | 13.6 GB |
| compressed | Q3_K_M | 12.5 GB | 10.6 GB |
| compressed | Q2_K | 9.8 GB | 8.2 GB |
Context Length Impact
KV cache VRAM at Q5_K_M quality. Longer context = more memory.
| Context | KV Cache | Total VRAM |
|---|---|---|
| 2K | 307 MB | 18.8 GB |
| 4K | 614 MB | 19.1 GB |
| 8K | 1.3 GB | 19.8 GB |
Compatible GPUs
Performance data for Gemma 2 27B Instruct across different hardware.
| Device | Quantization | Speed | Rating | Fits in VRAM |
|---|---|---|---|---|
| NVIDIA GeForce RTX 4090 | Q4_K_M | 22 tok/s | Good | ✓ |
| NVIDIA GeForce RTX 4060 Ti 16GB | Q4_K_M | 12 tok/s | Acceptable | ✓ |
| Apple M4 Max (36GB Unified) | Q5_K_M | 15 tok/s | Good | ✓ |
| NVIDIA GeForce RTX 4060 8GB | Q3_K_M | — | Not Viable | ✗ (offload) |
| NVIDIA GeForce RTX 4070 Ti 12GB | Q3_K_M | — | Not Viable | ✗ (offload) |
| NVIDIA GeForce RTX 3080 10GB | Q3_K_M | — | Not Viable | ✗ (offload) |
| Apple M3 Pro (18GB Unified) | Q4_K_M | — | Not Viable | ✗ (offload) |
Recommended Builds
Complete PC builds that can run Gemma 2 27B Instruct.
Mid-Range
High-EndHigh-End
Mid-Range
Mid-RangeFull AI Builder
AI Builder Workstation
NVIDIA GeForce RTX 4090 · 64GB DDR5-5600 (2x32GB)
$2,90224 GB VRAM
Runs 10 models
High-End
High-End AI Workstation
NVIDIA GeForce RTX 4090 · 64GB DDR5-6000 (2x32GB)
$3,67224 GB VRAM
Runs 8 models
High-EndHome AI Server
High-End Home AI Server
2x NVIDIA GeForce RTX 3090 24GB (Used) + NVLink Bridge · 128GB DDR5-5600 (4x32GB)
$3,84248 GB VRAM
Runs 12 models
Mid-RangeHome AI Server
Mid-Range Home AI Server
NVIDIA GeForce RTX 3090 24GB (Used) · 64GB DDR5-5600 (2x32GB)
$1,89224 GB VRAM
Runs 9 models
Frequently Asked Questions
- How much VRAM does Gemma 2 27B Instruct need?
- Gemma 2 27B Instruct requires 18.5 GB VRAM at recommended quality (Q5_K_M). At lower quality settings, it can fit in as little as 9.8 GB.
- What is the best GPU for Gemma 2 27B Instruct?
- The NVIDIA GeForce RTX 4090 delivers the best performance for Gemma 2 27B Instruct, achieving 22 tok/s at Q4_K_M with an good rating.
- Can I run Gemma 2 27B Instruct on an RTX 4060 Ti?
- Yes. On the NVIDIA GeForce RTX 4060 Ti 16GB, Gemma 2 27B Instruct runs at 12 tok/s (Q4_K_M, acceptable).
- What quantization should I use for Gemma 2 27B Instruct?
- For the best quality, use Q5_K_M (18.5 GB VRAM). If your GPU has limited VRAM, Q2_K (9.8 GB) is the most efficient option with acceptable quality.
- Is Gemma 2 27B Instruct good for coding?
- Gemma 2 27B Instruct supports coding use cases. For the best coding experience, pair it with an embedding model for local RAG.
Data confidence: verified. Last updated: 2026-03-01. Source