Gemma 2 9B needs ~18.7 GB VRAM. Mac Studio M1 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~84 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 well
Decode
84.2 tok/s
TTFT
2301 ms
Safe context
8K
Memory
18.7 GB / 46.1 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 84.2 tok/s | 1255 ms | 8K |
| Coding | B | Runs well | 84.2 tok/s | 2301 ms | 8K |
| Agentic Coding | B | Runs well | 84.2 tok/s | 3346 ms | 8K |
| Reasoning | B | Runs well | 84.2 tok/s | 2719 ms | 8K |
| RAG | B | Runs well | 84.2 tok/s | 4183 ms | 8K |
Inference speed
Estimated decode speed (tokens/sec) for Gemma 2 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 126.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 126.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 125.3 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 86.6 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 80.7 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 78.2 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 71.7 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 67.3 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 63.8 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 54.3 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 45.9 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 45.7 | Heavy offload |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 42.1 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 37.0 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 28.7 | Heavy offload |
| RTX 4060 8GB | 8 GB | Q4_K_M | 10.9 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Gemma 2 9B (9B params) fits at each quantization level on Mac Studio M1 Ultra 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B56 |
Q3_K_S | 3 | 4.4 GB | Low | B56 |
NVFP4 | 4 | 5.0 GB | Medium | B56 |
Q4_K_M | 4 | 5.5 GB | Medium | B56 |
Q5_K_M | 5 | 6.5 GB | High | B57 |
Q6_K | 6 | 7.4 GB | High | B57 |
Q8_0 | 8 | 9.6 GB | Very High | B57 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | B60 |
Copy-paste commands to run Gemma 2 9B on your machine.
Run
ollama run gemma2Yes, Mac Studio M1 Ultra 64GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 84.2 tok/s.
Gemma 2 9B (9B parameters) requires approximately 18.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M1 Ultra 64GB, Gemma 2 9B achieves approximately 84.2 tokens per second decode speed with a time-to-first-token of 2301ms using Q4_K_M quantization.
For coding workloads, Gemma 2 9B on Mac Studio M1 Ultra 64GB receives a B grade with 84.2 tok/s and 8K context.
On Mac Studio M1 Ultra 64GB, 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.
Not always. Mac Studio M1 Ultra 64GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-2-9b-on-m1-ultra-64gb" 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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