RTX PRO 6000 Blackwell Workstation Edition 96GBGünstige Wahl1792 GB/s (+992)
C
Raises estimated decode speed by about 224%.35.3 tok/s Dekodierung
Raises estimated decode speed by about 224%.
ca. $9,999 MSRP
Can stabilityai japanese stablelm instruct beta 70b run on Mac Studio M2 Ultra 128GB?
YES — Runs Great
C50Usable
Estimated from fit model
stabilityai japanese stablelm instruct beta 70b needs ~65.6 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~11 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 65.6 GB, 10.9 tok/s, Runs well
65.6 GB required92.2 GB available
71% VRAM used
Fit status
Runs well
Decode
10.9 tok/s
TTFT
17816 ms
Safe context
68K
Memory
65.6 GB / 92.2 GB
Memory breakdown
Weights42.7 GB
KV Cache8.2 GB
Runtime0.9 GB
Headroom13.8 GB
See how fast it feels
See how fast it feelsstabilityai japanese stablelm instruct beta 70b on Mac Studio M2 Ultra 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 10.9 tok/s decode · 17.8s TTFT (warm) · 27 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 10.9 tok/s | 9718 ms | 68K |
| Coding | C | Runs well | 10.9 tok/s | 17816 ms | 68K |
| Agentic Coding | C | Runs well | 10.9 tok/s | 25914 ms | 68K |
| Reasoning | C | Runs well | 10.9 tok/s | 21056 ms | 68K |
| RAG | C | Runs well | 10.9 tok/s | 32393 ms | 68K |
Inference speed
stabilityai japanese stablelm instruct beta 70b inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for stabilityai japanese stablelm instruct beta 70b at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~15 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 14.6 | Heavy offload |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 14.1 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 13.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 10.9 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 10.3 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 9.9 | Too big |
| 2× RTX 4090 24GB | 48 GB | Q4_K_M | 7.7 | Heavy offload |
| RTX 5090 32GB | 32 GB | Q4_K_M | 7.0 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 7.0 | Heavy offload |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 6.2 | Heavy offload |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 4.7 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 4.0 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 3.6 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.7 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.4 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.3 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.1 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | C42 |
Q3_K_S | 3 | 34.3 GB | Low | C44 |
NVFP4 | 4 | 39.2 GB | Medium | C45 |
Q4_K_M | 4 | 42.7 GB | Medium | C46 |
Q5_K_M | 5 | 50.4 GB | High | C47 |
Q6_K | 6 | 57.4 GB | High | C47 |
Q8_0Best for your GPU | 8 | 74.9 GB | Very High | C47 |
F16 | 16 | 143.5 GB | Maximum | F0 |
Get started
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 startUpgrade-Optionen
Hardware, die stabilityai japanese stablelm instruct beta 70b gut ausführt
Frequently asked questions
Can Mac Studio M2 Ultra 128GB run stabilityai japanese stablelm instruct beta 70b?
Yes, Mac Studio M2 Ultra 128GB can run stabilityai japanese stablelm instruct beta 70b with a C grade (Runs well). Expected decode speed: 10.9 tok/s.
How much VRAM does stabilityai japanese stablelm instruct beta 70b need?
stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 65.6 GB of memory with Q4_K_M quantization.
What is the best quantization for stabilityai japanese stablelm instruct beta 70b?
The recommended quantization for stabilityai japanese stablelm instruct beta 70b is Q4_K_M, which balances quality and memory efficiency.
What speed will stabilityai japanese stablelm instruct beta 70b run at on Mac Studio M2 Ultra 128GB?
On Mac Studio M2 Ultra 128GB, stabilityai japanese stablelm instruct beta 70b achieves approximately 10.9 tokens per second decode speed with a time-to-first-token of 17816ms using Q4_K_M quantization.
Can Mac Studio M2 Ultra 128GB run stabilityai japanese stablelm instruct beta 70b for coding?
For coding workloads, stabilityai japanese stablelm instruct beta 70b on Mac Studio M2 Ultra 128GB receives a C grade with 10.9 tok/s and 68K context.
What context window can stabilityai japanese stablelm instruct beta 70b use on Mac Studio M2 Ultra 128GB?
On Mac Studio M2 Ultra 128GB, stabilityai japanese stablelm instruct beta 70b can safely use up to 68K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for stabilityai japanese stablelm instruct beta 70b?
Not always. Mac Studio M2 Ultra 128GB 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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