gemma 2 2b it needs ~16.6 GB VRAM. Mac Studio M1 Ultra 128GB has 92.2 GB. With Q6_K quantization, expect ~28 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
28.0 tok/s
TTFT
6914 ms
Safe context
5.2M
Memory
16.6 GB / 92.2 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 | C | Runs well | 28.0 tok/s | 3771 ms | 5.2M |
| Coding | C | Runs well | 28.0 tok/s | 6914 ms | 5.2M |
| Agentic Coding | C | Runs well | 28.0 tok/s | 10057 ms | 5.2M |
| Reasoning | C | Runs well | 28.0 tok/s | 8171 ms | 5.2M |
| RAG | C | Runs well | 28.0 tok/s | 12571 ms | 5.2M |
How gemma 2 2b it (2B params) fits at each quantization level on Mac Studio M1 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.8 GB | Low | D40 |
Q3_K_S | 3 | 1.0 GB | Low | D40 |
NVFP4 | 4 |
Copy-paste commands to run gemma 2 2b it on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "bartowski/gemma-2-2b-it-GGUF" \
--hf-file "gemma-2-2b-it-GGUF-Q6_K.gguf" \
-c 4096 -ngl 99Yes, Mac Studio M1 Ultra 128GB can run gemma 2 2b it with a C grade (Runs well). Expected decode speed: 28.0 tok/s.
gemma 2 2b it (2B parameters) requires approximately 16.6 GB of memory with Q6_K quantization.
The recommended quantization for gemma 2 2b it is Q6_K, which balances quality and memory efficiency.
On Mac Studio M1 Ultra 128GB, gemma 2 2b it achieves approximately 28.0 tokens per second decode speed with a time-to-first-token of 6914ms using Q6_K quantization.
For coding workloads, gemma 2 2b it on Mac Studio M1 Ultra 128GB receives a C grade with 28.0 tok/s and 5.2M context.
On Mac Studio M1 Ultra 128GB, gemma 2 2b it can safely use up to 5.2M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-bartowski--gemma-2-2b-it-gguf-on-m1-ultra-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
1.1 GB |
| Medium |
| D40 |
Q4_K_M | 4 | 1.2 GB | Medium | D40 |
Q5_K_M | 5 | 1.4 GB | High | D40 |
Q6_K | 6 | 1.6 GB | High | D40 |
Q8_0 | 8 | 2.1 GB | Very High | D40 |
F16Best for your GPU | 16 | 4.1 GB | Maximum | D40 |
Not always. Mac Studio M1 Ultra 128GB 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.