stabilityai japanese stablelm instruct beta 70b needs ~62.2 GB VRAM. Mac Studio M3 Ultra 96GB has 69.1 GB. With Q4_K_M quantization, expect ~13 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
Tight fit
Decode
13.0 tok/s
TTFT
14844 ms
Safe context
30K
Memory
62.2 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 | C | Tight fit | 13.0 tok/s | 8097 ms | 30K |
| Coding | C | Tight fit | 13.0 tok/s | 14844 ms | 30K |
| Agentic Coding | C | Runs with offload (needs ~0.8 GB host RAM) | 12.5 tok/s | 22488 ms | 30K |
| Reasoning | C | Tight fit | 13.0 tok/s | 17542 ms | 30K |
| RAG | C | Runs with offload (needs ~0.8 GB host RAM) | 12.5 tok/s | 28109 ms | 30K |
How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on Mac Studio M3 Ultra 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | C45 |
Q3_K_S | 3 | 34.3 GB | Low | C47 |
NVFP4 | 4 | 39.2 GB | Medium | C47 |
Q4_K_M | 4 | 42.7 GB | Medium | C47 |
Q5_K_MBest for your GPU | 5 | 50.4 GB | High | C47 |
Q6_K | 6 | 57.4 GB | High | F0 |
Q8_0 | 8 | 74.9 GB | Very High | F0 |
F16 | 16 | 143.5 GB | Maximum | F0 |
Copy-paste commands to run stabilityai japanese stablelm instruct beta 70b on your machine.
Run
lms load hf-richarderkhov--stabilityai---japanese-stablelm-instruct-beta-70b-gguf && lms server startOpciones de mejora
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$3,999 MSRP
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$3,999 MSRP
NVIDIA H100 80GBMayor saltoSube la velocidad estimada de decodificación alrededor de un 407%.
~$40,000 MSRP
Yes, Mac Studio M3 Ultra 96GB can run stabilityai japanese stablelm instruct beta 70b with a C grade (Tight fit). Expected decode speed: 13.0 tok/s.
stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 62.2 GB of memory with Q4_K_M quantization.
The recommended quantization for stabilityai japanese stablelm instruct beta 70b is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M3 Ultra 96GB, stabilityai japanese stablelm instruct beta 70b achieves approximately 13.0 tokens per second decode speed with a time-to-first-token of 14844ms using Q4_K_M quantization.
For coding workloads, stabilityai japanese stablelm instruct beta 70b on Mac Studio M3 Ultra 96GB receives a C grade with 13.0 tok/s and 30K context.
On Mac Studio M3 Ultra 96GB, stabilityai japanese stablelm instruct beta 70b can safely use up to 30K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. Mac Studio M3 Ultra 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.
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