Gemma 2 27B needs ~31.8 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~18 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs with offload
Decode
18.2 tok/s
TTFT
10612 ms
Safe context
8K
Memory
31.8 GB / 32.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 18.2 tok/s | 5788 ms | 8K |
| Coding | B | Runs with offload | 18.2 tok/s | 10612 ms | 8K |
| Agentic Coding | F | Too heavy | 7.6 tok/s | 36934 ms | 8K |
| Reasoning | B | Runs with offload | 18.2 tok/s | 12541 ms | 8K |
| RAG | F | Too heavy | 7.6 tok/s | 46168 ms | 8K |
How Gemma 2 27B (27B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | B66 |
Q3_K_S | 3 | 13.2 GB | Low | B67 |
NVFP4 | 4 | 15.1 GB | Medium | B68 |
Q4_K_M | 4 | 16.5 GB | Medium | B69 |
Q5_K_M | 5 | 19.4 GB | High | B69 |
Q6_KBest for your GPU | 6 | 22.1 GB | High | B68 |
Q8_0 | 8 | 28.9 GB | Very High | F0 |
F16 | 16 | 55.4 GB | Maximum | F0 |
Copy-paste commands to run Gemma 2 27B on your machine.
Run
ollama run gemma2:27bOpciones de mejora
Sube la velocidad estimada de decodificación alrededor de un 27%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$3,999 MSRP
Sube la velocidad estimada de decodificación alrededor de un 27%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$3,999 MSRP
Sube la velocidad estimada de decodificación alrededor de un 142%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$10,000 MSRP
Yes, Radeon AI PRO R9700 32GB can run Gemma 2 27B with a B grade (Runs with offload). Expected decode speed: 18.2 tok/s.
Gemma 2 27B (27B parameters) requires approximately 31.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 2 27B is Q4_K_M, which balances quality and memory efficiency.
On Radeon AI PRO R9700 32GB, Gemma 2 27B achieves approximately 18.2 tokens per second decode speed with a time-to-first-token of 10612ms using Q4_K_M quantization.
For coding workloads, Gemma 2 27B on Radeon AI PRO R9700 32GB receives a B grade with 18.2 tok/s and 8K context.
On Radeon AI PRO R9700 32GB, Gemma 2 27B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-2-27b-on-radeon-ai-pro-r9700-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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