NVIDIA H20 96GBBudget pick4000 GB/s (+3454)
B
Raises estimated decode speed by about 383%.128.4 tok/s decode
Raises estimated decode speed by about 383%.
Adds memory headroom for longer context windows and future model growth.
~$12,000 MSRP
Gemma 2 27B needs ~39.0 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~27 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
26.6 tok/s
TTFT
7278 ms
Safe context
8K
Memory
39.0 GB / 69.1 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 | 26.6 tok/s | 3970 ms | 8K |
| Coding | B | Runs well | 26.6 tok/s | 7278 ms | 8K |
| Agentic Coding | A | Runs well | 26.6 tok/s | 10587 ms | 8K |
| Reasoning | B | Runs well | 26.6 tok/s | 8602 ms | 8K |
| RAG | A | Runs well | 26.6 tok/s | 13233 ms | 8K |
How Gemma 2 27B (27B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | B61 |
Q3_K_S | 3 | 13.2 GB | Low | B61 |
NVFP4 | 4 | 15.1 GB | Medium | B61 |
Q4_K_M | 4 | 16.5 GB | Medium | B62 |
Q5_K_M | 5 | 19.4 GB | High | B62 |
Q6_K | 6 | 22.1 GB | High | B63 |
Q8_0 | 8 | 28.9 GB | Very High | B64 |
F16Best for your GPU | 16 | 55.4 GB | Maximum | B67 |
Copy-paste commands to run Gemma 2 27B on your machine.
Run
ollama run gemma2:27bUpgrade options
NVIDIA H20 96GBBudget pickRaises estimated decode speed by about 383%.
Adds memory headroom for longer context windows and future model growth.
~$12,000 MSRP
NVIDIA A100 80GBBest valueRaises estimated decode speed by about 155%.
~$15,000 MSRP
Yes, MacBook Pro M4 Max 96GB can run Gemma 2 27B with a B grade (Runs well). Expected decode speed: 26.6 tok/s.
Gemma 2 27B (27B parameters) requires approximately 39.0 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 MacBook Pro M4 Max 96GB, Gemma 2 27B achieves approximately 26.6 tokens per second decode speed with a time-to-first-token of 7278ms using Q4_K_M quantization.
For coding workloads, Gemma 2 27B on MacBook Pro M4 Max 96GB receives a B grade with 26.6 tok/s and 8K context.
On MacBook Pro M4 Max 96GB, 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.
Not always. MacBook Pro M4 Max 96GB 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-2-27b-on-m4-max-96gb" 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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