Gemma 2 9B Instruct
9BGoogle Gemma 2
⚠ Superseded · Prefer Gemma 4 E4B IT
This index would suggest Gemma 4 E4B IT instead today — longer context (8K → 128K tokens). The numbers below are still accurate; they are just for a model you probably should not start with. Compare the two →
Google's compact Gemma 2 with sliding window attention. Punches above 9B.
8K
Max Context
3
Quant Variants
GGUF Q8_0
Best Quality
99.8%
Accuracy Retained
Quantization Variants
Per-quant VRAM, quality loss, and inference speed on RTX 4090
Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports
Similar models
Compare with Gemma 2Gemma 2 2B Instruct
Google Gemma 2
Ultra-compact Gemma 2. Runs on 4GB VRAM; great for edge prototyping.
Gemma 2 27B Instruct
Google Gemma 2
Largest open Gemma 2. Strong reasoning; needs 24GB+ VRAM at Q4.
Qwen2.5 14B Instruct
Alibaba Qwen2.5
The sweet spot between performance and resource usage. 16GB VRAM with Q4.
Mistral Nemo 12B Instruct
Mistral AI
Mistral + NVIDIA collaboration. 128K context, excellent multilingual support.
Running Gemma 2 9B Instruct locally
At Q4_K_M and 4K of context, Gemma 2 9B Instruct needs about 7.3 GB — 5.3 GB of weights, 1.31 GB of KV cache and a 0.7 GB activation buffer. The smallest cards in this index that clear that comfortably are 10 GB ones — 2 of them, from the Arc B570 10G up — and 67 of the 78 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.
What longer context costs
Going from 4K to 32K adds about 4.68 GB, taking the total to 12.4 GB. That is gentler than the parameter count suggests: only 21 of 42 layers keep a KV cache that grows with context, and 21 more cap theirs at a sliding window, so the cache scales at a fraction of the usual rate. The model's native window is 8K; holding all of it at Q4_K_M would need about 8 GB.
Which build to download
This index tracks 2 formats for it — GGUF, AWQ — across 3 levels. Q8_0 carries the lowest published perplexity loss at 0.2%. The fastest level listed is AWQ INT4 at about 188 tok/s on an RTX 4090, batch 1 — an estimate, not a run on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.
Common questions
- How much VRAM does Gemma 2 9B Instruct need?
- About 7.3 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 12.4 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
- Will Gemma 2 9B Instruct run on a 10GB GPU?
- Yes — at Q4_K_M and 4K context it needs about 7.3 GB, which leaves 2.7 GB spare on a Arc B570 10G, or on any of the other 10 GB cards here (2 in all). That is the smallest size in this index that clears it comfortably; 67 of 78 do.
- Which quantization of Gemma 2 9B Instruct should I use?
- Q8_0 has the lowest published quality loss (0.2%), and Q4_K_M is the level most people run. All 3 levels in the index are Q4_K_M, Q8_0, AWQ INT4.
Where this model fits
Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.
- Runs comfortably on
- Arc B570 10G10GB · +2.7RTX 3080 10G10GB · +2.7RTX 3060 12G12GB · +4.7Radeon RX 6700 XT12GB · +4.7Radeon RX 7700 XT12GB · +4.7
- Tight but possible
- RTX 40608GBRTX 4060 Ti 8G8GBRadeon RX 9060 XT 8G8GB
- Just misses
- Mac M1 8G+1.3GB overMac M3 8G+1.3GB over
- Compared here
- GGUF vs AWQ
- Try it yourself
- Size it in the calculatorGenerate the run command
How to actually run this
Deployment guides for this model and this class of hardware.
This page's figures change when the model or the runtime does.Last updated 2026-10-20 RSS → /feed.xml