NVIDIA H20 96GB经济之选96 GB VRAM (+16)4000 GB/s (+1961)
C
Raises estimated decode speed by about 89%.75.9 tok/s 解码
Raises estimated decode speed by about 89%.
~$12,000 MSRP
Can stabilityai japanese stablelm instruct beta 70b run on NVIDIA A100 80GB?
YES — Runs Great
C54Usable
Estimated from fit model
stabilityai japanese stablelm instruct beta 70b needs ~60.1 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~40 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 60.1 GB, 40.1 tok/s, Runs well
60.1 GB required80.0 GB available
75% VRAM used
Fit status
Runs well
Decode
40.1 tok/s
TTFT
4827 ms
Safe context
55K
Memory
60.1 GB / 80.0 GB
Memory breakdown
Weights42.7 GB
KV Cache8.2 GB
Runtime1.2 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsstabilityai japanese stablelm instruct beta 70b on NVIDIA A100 80GB
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: 40.1 tok/s decode · 4.8s TTFT (warm) · 100 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 | 40.1 tok/s | 2633 ms | 55K |
| Coding | C | Runs well | 40.1 tok/s | 4827 ms | 55K |
| Agentic Coding | C | Tight fit | 40.1 tok/s | 7020 ms | 55K |
| Reasoning | C | Runs well | 40.1 tok/s | 5704 ms | 55K |
| RAG | C | Tight fit | 40.1 tok/s | 8776 ms | 55K |
Quantization options
How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | C43 |
Q3_K_S | 3 | 34.3 GB | Low | C45 |
NVFP4 | 4 | 39.2 GB | Medium | C46 |
Q4_K_M | 4 | 42.7 GB | Medium | C47 |
Q5_K_M | 5 | 50.4 GB | High | C47 |
Q6_KBest for your GPU | 6 | 57.4 GB | High | C47 |
Q8_0 | 8 | 74.9 GB | Very High | F0 |
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 start升级选项
能流畅运行 stabilityai japanese stablelm instruct beta 70b 的硬件
Frequently asked questions
Can NVIDIA A100 80GB run stabilityai japanese stablelm instruct beta 70b?
Yes, NVIDIA A100 80GB can run stabilityai japanese stablelm instruct beta 70b with a C grade (Runs well). Expected decode speed: 40.1 tok/s.
How much VRAM does stabilityai japanese stablelm instruct beta 70b need?
stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 60.1 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 NVIDIA A100 80GB?
On NVIDIA A100 80GB, stabilityai japanese stablelm instruct beta 70b achieves approximately 40.1 tokens per second decode speed with a time-to-first-token of 4827ms using Q4_K_M quantization.
Can NVIDIA A100 80GB run stabilityai japanese stablelm instruct beta 70b for coding?
For coding workloads, stabilityai japanese stablelm instruct beta 70b on NVIDIA A100 80GB receives a C grade with 40.1 tok/s and 55K context.
What context window can stabilityai japanese stablelm instruct beta 70b use on NVIDIA A100 80GB?
On NVIDIA A100 80GB, stabilityai japanese stablelm instruct beta 70b can safely use up to 55K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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