MacBook Pro M4 Max 48GBOpción económica
48 GB Unified (+16)546 GB/s (+34)
B
Sube la velocidad estimada de decodificación alrededor de un 31%.71.7 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 31%.
~$2,499 MSRP
Can Gemma 2 9B run on Radeon Pro W6800 32GB?
YES — Runs Great
B65Good
Estimated from fit model
Gemma 2 9B needs ~15.0 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~52 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 15.0 GB, 54.8 tok/s, Runs well
15.0 GB required32.0 GB available
47% VRAM used
Fit status
Runs well
Decode
54.8 tok/s
TTFT
3530 ms
Safe context
8K
Memory
15.0 GB / 32.0 GB
Memory breakdown
Weights5.5 GB
KV Cache5.1 GB
Runtime1.2 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsGemma 2 9B on Radeon Pro W6800 32GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 54.8 tok/s decode · 3.5s TTFT (warm) · 137 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 52.2 tok/s | 2022 ms | 8K |
| Coding | B | Runs well | 52.2 tok/s | 3707 ms | 8K |
| Agentic Coding | B | Runs well | 52.2 tok/s | 5392 ms | 8K |
| Reasoning | B | Runs well | 52.2 tok/s | 4381 ms | 8K |
| RAG | B | Runs well | 52.2 tok/s | 6740 ms | 8K |
Quantization options
How Gemma 2 9B (9B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B58 |
Q3_K_S | 3 | 4.4 GB | Low | B58 |
NVFP4 | 4 | 5.0 GB | Medium | B58 |
Q4_K_M | 4 | 5.5 GB | Medium | B58 |
Q5_K_M | 5 | 6.5 GB | High | B59 |
Q6_K | 6 | 7.4 GB | High | B59 |
Q8_0 | 8 | 9.6 GB | Very High | B60 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | B64 |
Get started
Copy-paste commands to run Gemma 2 9B on your machine.
Run
ollama run gemma2Opciones de mejora
Hardware que ejecuta bien Gemma 2 9B
Mac Studio M2 Ultra 64GBMejor relación calidad-precio
64 GB Unified (+32)800 GB/s (+288)
B
Sube la velocidad estimada de decodificación alrededor de un 62%.88.7 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 62%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$3,999 MSRP
Frequently asked questions
Can Radeon Pro W6800 32GB run Gemma 2 9B?
Yes, Radeon Pro W6800 32GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 52.2 tok/s.
How much VRAM does Gemma 2 9B need?
Gemma 2 9B (9B parameters) requires approximately 15.0 GB of memory with Q4_K_M quantization.
What is the best quantization for Gemma 2 9B?
The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.
What speed will Gemma 2 9B run at on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, Gemma 2 9B achieves approximately 52.2 tokens per second decode speed with a time-to-first-token of 3707ms using Q4_K_M quantization.
Can Radeon Pro W6800 32GB run Gemma 2 9B for coding?
For coding workloads, Gemma 2 9B on Radeon Pro W6800 32GB receives a B grade with 52.2 tok/s and 8K context.
What context window can Gemma 2 9B use on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, Gemma 2 9B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
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