141 GB VRAM (+13)4800 GB/s (+1600)
C
Raises estimated decode speed by about 85%.94.4 tok/s decode
Raises estimated decode speed by about 85%.
~$30,000 MSRP
Can stabilityai japanese stablelm instruct beta 70b run on AMD Instinct MI250 128GB?
YES — Runs Great
C50Usable
○Estimated from fit model
stabilityai japanese stablelm instruct beta 70b needs ~64.6 GB VRAM. AMD Instinct MI250 128GB has 128.0 GB. With Q4_K_M quantization, expect ~51 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Share:
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) — 64.6 GB, 51.0 tok/s, Runs well
64.6 GB required128.0 GB available
50% VRAM used
Fit status
Runs well
Decode
51.0 tok/s
TTFT
3799 ms
Safe context
140K
Memory
64.6 GB / 128.0 GB
Memory breakdown
Weights42.7 GB
KV Cache8.2 GB
Runtime0.9 GB
Headroom12.8 GB
See how fast it feels
See how fast it feelsstabilityai japanese stablelm instruct beta 70b on AMD Instinct MI250 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: 51.0 tok/s decode · 3.8s TTFT (warm) · 127 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 51.0 tok/s | 2072 ms | 140K |
| Coding | C | Runs well | 51.0 tok/s | 3799 ms | 140K |
| Agentic Coding | C | Runs well | 51.0 tok/s | 5526 ms | 140K |
| Reasoning | C | Runs well | 51.0 tok/s | 4490 ms | 140K |
| RAG | C | Runs well | 51.0 tok/s | 6907 ms | 140K |
Quantization options
How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on AMD Instinct MI250 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | D40 |
Q3_K_S | 3 | 34.3 GB | Low | C41 |
NVFP4 | 4 | 39.2 GB | Medium | C42 |
Q4_K_M | 4 | 42.7 GB | Medium | C43 |
Q5_K_M | 5 | 50.4 GB | High | C44 |
Q6_K | 6 | 57.4 GB | High | C45 |
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 options