japanese stablelm instruct gamma 7B needs ~12.9 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~98 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
98.0 tok/s
TTFT
1976 ms
Safe context
663K
Memory
12.9 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 | C | Runs well | 98.0 tok/s | 1078 ms | 663K |
| Coding | C | Runs well | 98.0 tok/s | 1976 ms | 663K |
| Agentic Coding | C | Runs well | 98.0 tok/s | 2873 ms | 663K |
| Reasoning | C | Runs well | 98.0 tok/s | 2335 ms | 663K |
| RAG | C | Runs well | 98.0 tok/s | 3592 ms | 663K |
How japanese stablelm instruct gamma 7B (7B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C41 |
Q3_K_S | 3 | 3.4 GB | Low | C41 |
NVFP4 | 4 |
Copy-paste commands to run japanese stablelm instruct gamma 7B on your machine.
Run
lms load hf-thebloke--japanese-stablelm-instruct-gamma-7b-gguf && lms server startYes, Mac Studio M2 Ultra 64GB can run japanese stablelm instruct gamma 7B with a C grade (Runs well). Expected decode speed: 98.0 tok/s.
japanese stablelm instruct gamma 7B (7B parameters) requires approximately 12.9 GB of memory with Q4_K_M quantization.
The recommended quantization for japanese stablelm instruct gamma 7B is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M2 Ultra 64GB, japanese stablelm instruct gamma 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
For coding workloads, japanese stablelm instruct gamma 7B on Mac Studio M2 Ultra 64GB receives a C grade with 98.0 tok/s and 663K context.
On Mac Studio M2 Ultra 64GB, japanese stablelm instruct gamma 7B can safely use up to 663K 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-thebloke--japanese-stablelm-instruct-gamma-7b-gguf-on-m2-ultra-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| C41 |
Q4_K_M | 4 | 4.3 GB | Medium | C42 |
Q5_K_M | 5 | 5.0 GB | High | C42 |
Q6_K | 6 | 5.7 GB | High | C42 |
Q8_0 | 8 | 7.5 GB | Very High | C42 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C44 |
Not always. Mac Studio M2 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.