japanese stablelm instruct gamma 7B needs ~8.6 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~16 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
15.9 tok/s
TTFT
12157 ms
Safe context
186K
Memory
8.6 GB / 17.3 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 | 15.9 tok/s | 6631 ms | 186K |
| Coding | C | Runs well | 15.9 tok/s | 12157 ms | 186K |
| Agentic Coding | C | Runs well | 15.9 tok/s | 17683 ms | 186K |
| Reasoning | C | Runs well | 15.9 tok/s | 14367 ms | 186K |
| RAG | C | Runs well | 15.9 tok/s | 22104 ms | 186K |
How japanese stablelm instruct gamma 7B (7B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C46 |
Q3_K_S | 3 | 3.4 GB | Low | C47 |
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 startUpgrade options
Raises estimated decode speed by about 242%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 106%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 91%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Yes, MacBook Pro M3 24GB can run japanese stablelm instruct gamma 7B with a C grade (Runs well). Expected decode speed: 15.9 tok/s.
japanese stablelm instruct gamma 7B (7B parameters) requires approximately 8.6 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 MacBook Pro M3 24GB, japanese stablelm instruct gamma 7B achieves approximately 15.9 tokens per second decode speed with a time-to-first-token of 12157ms using Q4_K_M quantization.
For coding workloads, japanese stablelm instruct gamma 7B on MacBook Pro M3 24GB receives a C grade with 15.9 tok/s and 186K context.
On MacBook Pro M3 24GB, japanese stablelm instruct gamma 7B can safely use up to 186K 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-m3-24gb" 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 |
| C47 |
Q4_K_M | 4 | 4.3 GB | Medium | C47 |
Q5_K_M | 5 | 5.0 GB | High | C48 |
Q6_K | 6 | 5.7 GB | High | C48 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C50 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Not always. MacBook Pro M3 24GB 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.