stabilityai japanese stablelm instruct beta 70b needs ~58.5 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~11 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
11.0 tok/s
TTFT
17664 ms
Safe context
27K
Memory
58.5 GB / 64.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 11.0 tok/s | 9635 ms | 27K |
| Coding | C | Tight fit | 11.0 tok/s | 17664 ms | 27K |
| Agentic Coding | C | Runs with offload (needs ~1.7 GB host RAM) | 7.5 tok/s | 37378 ms | 27K |
| Reasoning | C | Tight fit | 11.0 tok/s | 20876 ms | 27K |
| RAG | C | Runs with offload (needs ~1.7 GB host RAM) | 7.5 tok/s | 46722 ms | 27K |
How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | C46 |
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 startアップグレードオプション
RTX PRO 6000 Blackwell Workstation Edition 96GBコスパ重視Raises estimated decode speed by about 221%.
Adds memory headroom for longer context windows and future model growth.
〜$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBコスパ最良Raises estimated decode speed by about 185%.
Adds memory headroom for longer context windows and future model growth.
〜$9,999 MSRP
NVIDIA H20 96GBNVIDIAアップグレードRaises estimated decode speed by about 590%.
Adds memory headroom for longer context windows and future model growth.
〜$12,000 MSRP
Yes, NVIDIA A16 64GB can run stabilityai japanese stablelm instruct beta 70b with a C grade (Tight fit). Expected decode speed: 11.0 tok/s.
stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 58.5 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 NVIDIA A16 64GB, stabilityai japanese stablelm instruct beta 70b achieves approximately 11.0 tokens per second decode speed with a time-to-first-token of 17664ms using Q4_K_M quantization.
For coding workloads, stabilityai japanese stablelm instruct beta 70b on NVIDIA A16 64GB receives a C grade with 11.0 tok/s and 27K context.
On NVIDIA A16 64GB, stabilityai japanese stablelm instruct beta 70b can safely use up to 27K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-richarderkhov--stabilityai---japanese-stablelm-instruct-beta-70b-gguf-on-a16-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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