stabilityai japanese stablelm instruct beta 70b needs ~55.2 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With NVFP4 quantization, expect ~8 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
12.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
6.3 tok/s
TTFT
30884 ms
Safe context
4K
Memory
58.7 GB / 46.1 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 6.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised (needs ~6.7 GB host RAM) | 6.9 tok/s | 15407 ms | 4K |
| Coding | F | Too heavy | 3.5 tok/s | 55592 ms | 4K |
| Agentic Coding | F | Too heavy | 5.4 tok/s | 52374 ms | 4K |
| Reasoning | F | Too heavy | 6.3 tok/s | 36500 ms | 4K |
| RAG | F | Too heavy | 5.4 tok/s | 65467 ms | 4K |
How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | C48 |
Q3_K_SBest for your GPU | 3 | 34.3 GB | Low | C47 |
NVFP4 | 4 | 39.2 GB | Medium | F0 |
Q4_K_M | 4 | 42.7 GB | Medium | F0 |
Q5_K_M | 5 | 50.4 GB | High | F0 |
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 startOpções de upgrade
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$2,499 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$2,499 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$3,199 MSRP
NVIDIA H100 80GBMaior saltoMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$40,000 MSRP
Yes, MacBook Pro M4 Pro 64GB can run stabilityai japanese stablelm instruct beta 70b at NVFP4 quantization (Very compromised (needs ~6.5 GB host RAM)). The recommended Q4_K_M requires 58.7 GB which exceeds available memory, but at NVFP4 it needs only 55.2 GB. Expected decode speed: 7.7 tok/s.
stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 58.7 GB at Q4_K_M quantization. On MacBook Pro M4 Pro 64GB, it fits at NVFP4 using 55.2 GB.
The recommended quantization is Q4_K_M, but on MacBook Pro M4 Pro 64GB the best fitting quantization is NVFP4, which uses 55.2 GB.
On MacBook Pro M4 Pro 64GB, stabilityai japanese stablelm instruct beta 70b achieves approximately 7.7 tokens per second decode speed with a time-to-first-token of 25038ms using NVFP4 quantization.
For coding workloads, stabilityai japanese stablelm instruct beta 70b on MacBook Pro M4 Pro 64GB receives a F grade with 3.5 tok/s and 4K context.
On MacBook Pro M4 Pro 64GB, stabilityai japanese stablelm instruct beta 70b can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Not always. MacBook Pro M4 Pro 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.
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